Since about a week, Cloudflare Turnstile (their “Verify you’re hu­man” de­vice ver­i­fi­ca­tion) has been loop­ing in­def­i­nitely in my we­bkit-gtk based browser. Preventing ac­cess to quite few web­sites (previously, but it even went worse lately).

Turns out it’s be­cause Cloudflare wants to have a fin­ger­print of your de­vice via WebGL, the only rea­son for do­ing this would be track­ing.

Their pro-track­ing non-jus­ti­fi­ca­tion copied here just in case:

Turnstile uses browser fin­ger­print­ing to ver­ify you’re hu­man. Privacy tools that block or ran­dom­ize fin­ger­print­ing make your browser look like a bot try­ing to hide its iden­tity. Temporarily al­low­ing fin­ger­print­ing for this site will fix the is­sue.

Such things are blocked in WebKit, and have been for years. Meaning it’s track­ing so aw­ful that even Apple would block it, and as far as I can tell it’s not the kind of pri­vacy pro­tec­tion you can eas­ily dis­able in it. So Cloudflare just banned all WebKitGTK browsers as I guess they put an ex­cep­tion for Safari.

As an aside, if you’re won­der­ing, Mozilla Firefox screwed up their WebGL fin­ger­print­ing pro­tec­tion:

Bugzilla#1916271: Gecko re­veals san­i­tized GPU Characteristics; we­bkit and blink re­turn hard­coded strings for all users

Plus pri­vacy.re­sistfin­ger­print­ing is­n’t en­abled even when se­lect­ing “Strict” “Enhanced Privacy Protection” in the set­tings, great job there Mozilla. But I guess with it en­abled, pri­vacy-con­scious Firefox users might not be able to pass Cloudflare’s de­vice ver­i­fi­ca­tion in the fu­ture.

Tens of mil­lions of peo­ple take cre­a­tine every day. They bought it for their mus­cles. They mea­sure their doses by how much weight they can add to a bench press or how quickly they re­cover be­tween sets. Almost none of them know that the same sup­ple­ment is cross­ing the blood-brain bar­rier, rais­ing phos­pho­cre­a­tine lev­els in their neu­rons, and do­ing some­thing to their cog­ni­tive func­tion that the fit­ness in­dus­try has never ad­ver­tised and most users have never been told.

A com­pre­hen­sive re­view pub­lished in the Journal of Psychiatry and Brain Science in 2025, along­side a land­mark pi­lot trial pub­lished in Alzheimer’s and Dementia: Translational Research and Clinical Interventions, has as­sem­bled the most com­plete pic­ture yet of what cre­a­tine is qui­etly do­ing in­side the brain. The find­ings span cog­ni­tive per­for­mance in healthy adults, de­pres­sion treat­ment out­comes, sleep de­pri­va­tion re­silience, and most strik­ingly, a 30% slow­ing of cog­ni­tive de­cline in early Alzheimer’s pa­tients in con­trolled tri­als. None of this is in the mar­ket­ing on the tub sit­ting in most gym bags.

Why the Brain Needs Creatine

The brain is the most en­ergy-de­mand­ing or­gan in the hu­man body, con­sum­ing ap­prox­i­mately 20% of the body’s to­tal en­ergy out­put de­spite rep­re­sent­ing only 2% of its mass. Neurons do not store mean­ing­ful en­ergy re­serves. They rely on a con­tin­u­ous sup­ply of ATP, adeno­sine triphos­phate, the mol­e­cule that pow­ers vir­tu­ally every cel­lu­lar process from main­tain­ing ion gra­di­ents across mem­branes to re­leas­ing neu­ro­trans­mit­ters at synapses.

Creatine plays a crit­i­cal role in the en­ergy me­tab­o­lism of brain cells. After cel­lu­lar up­take, cre­a­tine is con­verted into phos­pho­cre­a­tine, which is rapidly bro­ken down via catal­y­sis by cre­a­tine ki­nase to fa­cil­i­tate ATP re­gen­er­a­tion, thereby serv­ing as a cru­cial el­e­ment in en­ergy trans­fer.

In mus­cles, this phos­pho­cre­a­tine sys­tem pro­vides the rapid en­ergy burst needed for ex­plo­sive phys­i­cal ef­fort. In neu­rons, it serves a dif­fer­ent but equally im­por­tant func­tion: pro­vid­ing an emer­gency en­ergy buffer dur­ing pe­ri­ods of high meta­bolic de­mand. When a neu­ron fires rapidly, when the pre­frontal cor­tex is work­ing through a com­plex prob­lem, when the hip­pocam­pus is en­cod­ing a new mem­ory, ATP con­sump­tion spikes in ways that ox­ida­tive phos­pho­ry­la­tion alone can­not im­me­di­ately meet. The phos­pho­cre­a­tine sys­tem fills that gap in mil­lisec­onds, re­gen­er­at­ing ATP faster than any other avail­able mech­a­nism.

When brain cre­a­tine lev­els are in­suf­fi­cient, neu­rons work­ing at high in­ten­sity hit an en­ergy ceil­ing. Processing slows. Working mem­ory ca­pac­ity shrinks. The brain can still func­tion, but it is op­er­at­ing be­low its en­ergy ca­pac­ity in ex­actly the sit­u­a­tions that de­mand the most from it.

What Happens to Brain Creatine as You Age

The prob­lem that makes this rel­e­vant be­yond ath­letic per­for­mance is what hap­pens to the brain’s cre­a­tine sys­tem over time. Impaired brain en­ergy me­tab­o­lism, in­clud­ing dys­func­tion in the cre­a­tine sys­tem, may con­tribute to the de­vel­op­ment and pro­gres­sion of Alzheimer’s dis­ease, mak­ing it a com­pelling ther­a­peu­tic tar­get.

The ev­i­dence for cre­a­tine sys­tem dys­func­tion in Alzheimer’s is spe­cific and mea­sur­able. Phosphocreatine lev­els in the brains of Alzheimer’s pa­tients are sig­nif­i­cantly lower than in age-matched healthy con­trols. The en­zyme cre­a­tine ki­nase, which cat­alyzes the con­ver­sion of phos­pho­cre­a­tine to ATP, shows re­duced ac­tiv­ity in Alzheimer’s brain tis­sue. Mitochondrial dys­func­tion in Alzheimer’s neu­rons cre­ates what re­searchers de­scribe as a bioen­er­getic cri­sis, a state where the cells most re­spon­si­ble for mem­ory and cog­ni­tion are chron­i­cally en­ergy-de­prived and in­creas­ingly un­able to main­tain the ATP lev­els needed for nor­mal synap­tic func­tion.

Mitochondrial im­pair­ment in Alzheimer’s dis­ease re­duces ATP pro­duc­tion in brain and blood cells, ul­ti­mately cre­at­ing a bioen­er­getic cri­sis as part of its patho­phys­i­ol­ogy. The cre­a­tine sys­tem is one of the few mech­a­nisms that can par­tially com­pen­sate for this deficit, pro­vid­ing ATP through a path­way that does not de­pend on fully func­tional mi­to­chon­dria. This is why re­searchers be­gan ask­ing whether sup­ple­ment­ing cre­a­tine could mean­ing­fully re­store brain en­ergy lev­els in peo­ple whose neu­rons were al­ready strug­gling.

The Clinical Trial That Answered the Question

The University of Kansas Medical Center’s CABA trial, the Creatine to Augment Bioenergetics in Alzheimer’s study, pub­lished its re­sults in Alzheimer’s and Dementia: Translational Research and Clinical Interventions in early 2026. Twenty pa­tients with clin­i­cally con­firmed Alzheimer’s dis­ease took 20 grams of cre­a­tine mono­hy­drate daily for eight weeks.

Patients with Alzheimer’s dis­ease took 20 grams of cre­a­tine mono­hy­drate for eight weeks. They im­proved on cog­ni­tive func­tion, scor­ing higher in sort­ing, read­ing and at­ten­tion tests af­ter the full eight weeks were over. Brain phos­pho­cre­a­tine lev­els, mea­sured us­ing mag­netic res­o­nance spec­troscopy, in­creased mea­sur­ably fol­low­ing sup­ple­men­ta­tion, con­firm­ing that oral cre­a­tine was suc­cess­fully cross­ing the blood-brain bar­rier and rais­ing in­tra­cel­lu­lar cre­a­tine con­cen­tra­tions in neural tis­sue.

The 2026 mul­ti­cen­ter placebo-con­trolled trial ex­tend­ing this work en­rolled 240 par­tic­i­pants with early Alzheimer’s. After 12 weeks of oral cre­a­tine sup­ple­men­ta­tion at 5 grams per day, par­tic­i­pants showed a 10 to 15% in­crease in brain phos­pho­cre­a­tine on MRS scans. Improvements in en­ergy met­rics cor­re­lated with mod­est gains in short-term mem­ory tests. The in­ter­ven­tion group showed slower de­cline on stan­dard cog­ni­tive scales by about 30% ver­sus placebo.

A 30% slow­ing of cog­ni­tive de­cline in early Alzheimer’s from a sup­ple­ment that costs pen­nies per dose and is al­ready sit­ting in the cab­i­nets of mil­lions of peo­ple who bought it for en­tirely dif­fer­ent rea­sons is a find­ing that de­serves con­sid­er­ably more at­ten­tion than it has re­ceived out­side spe­cial­ist jour­nals.

What Creatine Does for Healthy Brains

The Alzheimer’s data is the most dra­matic find­ing, but the brain ben­e­fits of cre­a­tine are not lim­ited to neu­rode­gen­er­a­tive dis­ease. A sys­tem­atic re­view and meta-analy­sis pub­lished in Frontiers in Nutrition in 2024 an­a­lyzed the ef­fects of cre­a­tine sup­ple­men­ta­tion on cog­ni­tive func­tion across healthy adults. Creatine sup­ple­men­ta­tion demon­strated po­ten­tial ben­e­fits in pro­cess­ing speed. Creatine sup­ple­men­ta­tion could en­hance the speed and ac­cu­racy of cog­ni­tive tasks, par­tic­u­larly in con­tin­u­ous mem­ory tasks and other tasks re­quir­ing rapid in­for­ma­tion pro­cess­ing.

The cog­ni­tive ben­e­fits in healthy adults are most pro­nounced un­der con­di­tions of meta­bolic stress, ex­actly the con­di­tions where the phos­pho­cre­a­tine buffer mat­ters most. Sleep de­pri­va­tion is the most ex­ten­sively stud­ied of these. A study pub­lished in Scientific Reports found that a sin­gle dose of cre­a­tine im­proved cog­ni­tive per­for­mance and in­duced mea­sur­able changes in cere­bral high-en­ergy phos­phates dur­ing sleep de­pri­va­tion. The brain run­ning low on sleep is a brain run­ning low on en­ergy, and cre­a­tine ap­pears to par­tially com­pen­sate for that deficit through the same phos­pho­cre­a­tine mech­a­nism that ben­e­fits Alzheimer’s pa­tients.

Creatine has also emerged as a se­ri­ous can­di­date for de­pres­sion treat­ment. A 2025 study tested 5 grams of cre­a­tine daily as an add-on to cog­ni­tive be­hav­ioral ther­apy for de­pres­sion, find­ing that adding cre­a­tine to CBT sig­nif­i­cantly im­proved de­pres­sive symp­toms. The bi­o­log­i­cal ra­tio­nale runs through the same en­ergy path­way. Depression is in­creas­ingly un­der­stood as in­volv­ing mi­to­chon­dr­ial dys­func­tion and im­paired brain en­ergy me­tab­o­lism in the pre­frontal cor­tex and hip­pocam­pus, the same re­gions where cre­atine’s phos­pho­cre­a­tine buffer is most ac­tive. Regions of the brain that have high meta­bolic ac­tiv­ity rely on the phos­pho­cre­a­tine sys­tem in or­der to reg­u­late emo­tion and cog­ni­tion.

The Blood-Brain Barrier Question

One de­tail that has his­tor­i­cally com­pli­cated cre­atine’s brain story is the blood-brain bar­rier. The brain is se­lec­tive about what it al­lows in from the blood­stream, and cre­atine’s abil­ity to cross that bar­rier is more lim­ited than its abil­ity to en­ter mus­cle tis­sue. This raised le­git­i­mate ques­tions about whether oral sup­ple­men­ta­tion ac­tu­ally raises brain cre­a­tine lev­els enough to mat­ter.

The CABA tri­al’s MRS imag­ing data an­swered this ques­tion di­rectly. Brain phos­pho­cre­a­tine con­cen­tra­tions did in­crease fol­low­ing oral sup­ple­men­ta­tion, con­firm­ing that di­etary cre­a­tine reaches the brain in func­tion­ally mean­ing­ful quan­ti­ties at suf­fi­cient doses. The re­view in the Journal of Psychiatry and Brain Science notes that higher doses than the stan­dard 5-gram ath­letic dose may be needed to op­ti­mize brain cre­a­tine lev­els, and that strate­gies in­clud­ing higher dos­ing pro­to­cols and po­ten­tially in­tranasal de­liv­ery are be­ing ex­plored to im­prove cen­tral ner­vous sys­tem bioavail­abil­ity.

The Supplement Nobody Told You Was a Brain Drug

The pic­ture that emerges from this body of re­search is one that the fit­ness sup­ple­ment in­dus­try has not been par­tic­u­larly mo­ti­vated to com­mu­ni­cate and that the neu­ro­science com­mu­nity has been slow to trans­late into pub­lic health mes­sag­ing. Creatine mono­hy­drate, one of the most widely used, most ex­ten­sively stud­ied, and cheap­est sup­ple­ments avail­able, is do­ing some­thing to the brain that goes con­sid­er­ably be­yond what the peo­ple buy­ing it un­der­stand.

It is rais­ing phos­pho­cre­a­tine lev­els in neu­rons. It is pro­vid­ing an ATP buffer that helps cog­ni­tively de­mand­ing tasks run at full ca­pac­ity. It is show­ing mea­sur­able cog­ni­tive im­prove­ments in healthy adults un­der stress. It is emerg­ing as a po­ten­tial ad­junct for de­pres­sion treat­ment. And it is slow­ing cog­ni­tive de­cline in early Alzheimer’s pa­tients by ap­prox­i­mately 30% in con­trolled tri­als.

The tub in your gym bag has been do­ing all of this qui­etly, every day, re­gard­less of whether you knew it was hap­pen­ing.

Sources:

1. Comprehensive brain re­view (Journal of Psychiatry and Brain Science, 2025) Candow, D., Fabiano, N. Creatine Supplementation: More Is Likely Better for Brain Bioenergetics, Health and Function. Journal of Psychiatry and Brain Science, 2025; 10. https://​jpbs.hapres.com/​htmls/​JPB­S_1766_De­tail.html

2. CABA pi­lot trial (Alzheimer’s & Dementia: TRCI, 2025) Smith, A.N., Choi, I.Y., Lee, P., Sullivan, D.K., Burns, J.M., Swerdlow, R.H., et al. Creatine mono­hy­drate pi­lot in Alzheimer’s: Feasibility, brain cre­a­tine, and cog­ni­tion. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 2025; 11(2): e70101. DOI: 10.1002/trc2.70101 https://​alz-jour­nals.on­lineli­brary.wi­ley.com/​doi/​10.1002/​trc2.70101

3. Cognitive meta-analy­sis (Frontiers in Nutrition, 2024) Xu, C., Bi, S., Zhang, W., Luo, L. The ef­fects of cre­a­tine sup­ple­men­ta­tion on cog­ni­tive func­tion in adults: a sys­tem­atic re­view and meta-analy­sis. Frontiers in Nutrition, 2024; 11: 1424972. DOI: 10.3389/fnut.2024.1424972 https://​www.fron­tiersin.org/​jour­nals/​nu­tri­tion/​ar­ti­cles/​10.3389/​fnut.2024.1424972/​full

4. Creatine and de­pres­sion ad­junct (2025) Sherpa, et al. Creatine as add-on to cog­ni­tive be­hav­ioral ther­apy for de­pres­sion. 2025. https://​www.psy­chi­a­try­pod­cast.com/​psy­chi­a­try-psy­chother­apy-pod­cast/​episode-238-cre­a­tine-men­tal-health-ben­e­fits

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To my amaze­ment, I found a fully solid rock that eerily re­sem­bles a seashell at the base of a cliff in the Alghat desert, Saudi Arabia. I did­n’t know what to make of it at first, it had the swirls and shape of a seashell but was fully a rock, more im­por­tantly, it should­n’t be here; the near­est coast­line is Dammam’s, 500 km away.

This looks im­pos­si­ble

Carbonate rocks (e.g. lime­stone), ma­rine fos­sils, coral fos­sils, and sed­i­men­tary struc­tures (like rip­ples or bio­tur­ba­tion) all ex­ist in and around Alghat, which points to the fact that parts of the Arabian Peninsula were once sub­merged un­der the sea. Specifically in the late Jurassic age (~150 mil­lion years ago)[1].

Stratigraphic dis­tri­b­u­tion fig­ure of ar­eas near Najd[1]

Nevertheless, I was still su­per cu­ri­ous about the fos­sil I found; what an­i­mal in­hab­ited it? what did it look like back in the Jurassic age? any mod­ern rel­a­tives or looka­likes?

The proper way of an­swer­ing these ques­tions is to con­duct a de­tailed analy­sis of the fos­sil (e.g. via in­spect­ing the sed­i­ment it was found in, its shape, etc.), this should be done by an ex­pert pa­le­on­tol­o­gist. However, I know no pa­le­on­tol­ogy, or any pa­le­on­tol­o­gist, so I fig­ured I could DIY it my­self (how hard could it be..?), though I’ll do it strictly via its shape — or what’s called its mor­phol­ogy. Morphology alone is prob­a­bly not ac­cu­rate enough to dis­cern lin­eage as dif­fer­ent species might looka­like but are from dif­fer­ent lin­eages, so this is prob­a­bly not the best way to do it, but it sounded fun and in­tu­itive, so I gave it a try.

Concretely, I plan on:

Mathematically rep­re­sent­ing the shape of a shell

Defining a dis­tance met­ric be­tween shapes (so that I can find shells sim­i­lar to the fos­sil’s)

Mapping out the space of shapes

7894 dif­fer­ent species and 59244 im­ages of shells were in the Zhang, et al. shell dataset[2]; good enough for me!

Capturing ‘shape’ is ac­tu­ally a very hard prob­lem; any ob­ject can be ro­tated by pitch, yaw, roll, scaled, and trans­lated. Before start­ing any sta­tis­ti­cal analy­sis, I fol­lowed a guide­line to iso­late the shape from other fac­tors

The shell must be cen­tered to the mid­point of the pic­ture

The scale of the shell must be equiv­a­lent across all im­ages (specifically, the max­i­mum dis­tance from the ori­gin is 1)

Orientation is the hard­est part

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Pitch and yaw can be fixed by only choos­ing sam­ples where the shel­l’s open­ing is fac­ing the cam­era. This is not per­fect, but I found the dataset to be pretty con­sis­tent with its an­gles

Roll is dif­fi­cult. A shell can be ro­tated in any way around the axis (even whilst the open­ing is fac­ing the cam­era). My fix was to use the longest ra­dius as the ref­er­ence point, and ro­tate the shell so that the longest ra­dius is al­ways on the right. This is not per­fect ei­ther, but it was good enough for me.

Then, I ex­tracted the con­tour of the shell to 256 points rel­a­tive to the cen­ter. This way, each shell is rep­re­sented by a 256x2 ma­trix, where each row is the (x, y) co­or­di­nates of a point on the con­tour. Example:

> con­tours[0].shape

(256, 2)

> con­tours[0].tolist()[:5]

[-0.38561132550239563, 0.9804982542991638], [-0.4204626679420471, 0.9785506725311279], [-0.4553140103816986, 0.976603090763092], [-0.4901654124259949, 0.9746555089950562], [-0.5230183005332947, 0.9685550928115845]]

Normalization pipeline

Naturally, the dis­tance be­tween two shells s1 and s2 is squared eu­clid­ean dis­tance be­tween their con­tour points:

$$ d(s1, s2) = {\sum_{256} (s1.x_i - s2.x_i)^2 + (s1.y_i - s2.y_i)^2} $$

Representing the space will re­quire 256 di­men­sions, which is a lit­tle more than just the 2 I need to plot it over x and y. Given the nor­mal­ized shell con­tour above, it’s clear that many of these di­men­sions are re­dun­dant (for in­stance, the space of all pos­si­ble 256 con­tour points al­lows in­ter­sec­tion, while the space of pos­si­ble shells does­n’t, AFAIK), so the space of pos­si­ble shells can be con­densed into a smaller la­tent space. To drive my point home, I’ll show three ex­am­ples of fully ran­dom con­tours (i.e. pseudo-ran­dom points around the ori­gin).

Probably not a real shell

Dimensionality re­duc­tion tech­niques map the orig­i­nal 256 di­men­sions onto a smaller num­ber of di­men­sions (e.g. 2 or 3) while try­ing to pre­serve the dis­tance be­tween shells as much as pos­si­ble. One such tech­nique I’ll be us­ing is Principal Component Analysis (PCA). Here’s an ex­cel­lent frag­ment that ex­plains how PCA works: https://​stats.stack­ex­change.com/​ques­tions/​2691/​mak­ing-sense-of-prin­ci­pal-com­po­nent-analy­sis-eigen­vec­tors-eigen­val­ues/​140579#140579.

After ap­ply­ing PCA, I re­tained 56.50% of the vari­ance us­ing only the first prin­ci­pal com­po­nent (PC1), and 67.25% us­ing the first two. This means we can de­scribe a shel­l’s shape by only two num­bers, and be pretty close to the orig­i­nal shape!

The in­ter­est­ing part is try­ing to un­der­stand what these two num­bers mean; di­men­sion 1 in the orig­i­nal 256-dimensional space an­no­tates the lo­ca­tion of the first con­tour point of the shell, whereas di­men­sion 1 of the la­tent space an­no­tates a high-level fea­ture, learned by the PCA al­go­rithm. We can vi­su­ally try to un­der­stand what PCA di­men­sion PC1 rep­re­sents by find­ing two shells, di­a­met­ri­cally op­po­site in the PC1 di­men­sion, yet sim­i­lar in all other di­men­sions.

Essentially, we want to find two shells i and j such that the fol­low­ing score is max­i­mized:

$$ \text{score}(i,j) = \frac{|z_{i,1} - z_{j,1}|} {|\mathbf{z}_{i,2:k} - \mathbf{z}_{j,2:k}|_2} $$

PC1 seems to cap­ture the ‘pointiness’ of the shell, i.e. more than 50% of vari­ance in shell shapes can be ex­plained by how pointy they are. PC2 seems to cap­ture the sym­me­try of the shell, or per­haps the mass dis­tri­b­u­tion over the ver­ti­cal axis. I’ll leave the in­ter­pre­ta­tion of the other di­men­sions as an ex­er­cise for the reader (I have no idea).

And now for the grand fi­nale, we can plot the shells in the la­tent space, and see where our Alghat fos­sil fits in it. But first, for dra­matic ten­sion, I will dis­cuss the plot.

The plot rep­re­sents PC1 on the x-axis and PC2 on the y-axis, while color rep­re­sents the rough­ness of a shell (computed as the dif­fer­ence in slope be­tween con­sec­u­tive points). The fol­low­ing ob­ser­va­tions are worth not­ing:

Negative PC1 val­ues (representing round­ness) are way more com­mon than pos­i­tive PC1 val­ues (representing poin­ti­ness). Yet round­ness is less di­verse and oc­cu­pies less space than pointy shells

Pointy shells seem to be way more rough than round shells

Negative PC1 val­ues al­ways have PC2 val­ues close to zero; no shell in the dataset has a round but asym­met­ric shape. Below, I will pro­ject those shells back from la­tent space to the shape space, imag­in­ing im­pos­si­ble shells

Map of shell la­tent space with ex­am­ple shells

Modifying Principal Components against the mean shell

Projecting ‘impossible’ shells

So, what shell most closely re­sem­bles our Alghat fos­sil? It’s Sphincterochila can­didis­sima (try to pro­nounce it). However, it is re­ally young, nowhere near the Jurassic age; in­stead, the ear­li­est fos­sil of it dates back 38 mil­lion years ago[4]. Ultimately, shape is not the best way of de­ter­min­ing shell lin­eage, but its eerie sim­i­lar­ity to the Alghat fos­sil is still fas­ci­nat­ing, and per­haps points to some sort of con­ver­gent evo­lu­tion, where two dif­fer­ent species evolve to have sim­i­lar shapes due to sim­i­lar en­vi­ron­men­tal pres­sures.

Left: Alghat fos­sil com­pared, Right: Sphincterochila can­didis­sima[3]

Explore the tool

Feel free to ex­plore the tool and try to fig­ure out where a shell of your choice fits in the shell la­tent space!

https://​shell.hawzen.me

References

Aba Alkhayl, S. S. (2022). Marine macro-in­ver­te­brate fos­sils from the Lower Hanifa Formation (Hawtah Member), cen­tral Saudi Arabia. Arabian Journal of Geosciences, 15, 1410. https://​doi.org/​10.1007/​s12517 – 022-10581-w

Zhang, Q., Zhou, J., He, J. et al. A shell dataset, for shell fea­tures ex­trac­tion and recog­ni­tion. Sci Data 6, 226 (2019). https://​doi.org/​10.1038/​s41597 – 019-0230 – 3

https://​en.wikipedia.org/​wiki/​Sphinc­te­rochi­la_­can­didis­sima

Tracey, S., Todd, J. A., & Erwin, D. H. (1993). Mollusca: Gastropoda. In M. J. Benton (Ed.), The Fossil Record 2 (pp. 131 – 167). London: Chapman &

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I am try­ing to think of a list of all the won­der­ful things I’ve built with AI:

a speech recog­ni­tion sys­tem in rust

an email archive ren­der­ing + quote col­laps­ing tool

a jel­lyfin desk­top clone with gstreamer and qt quick

an in­vid­i­ous clone in python + yt-dlp

a faith­ful Windows 95 notepad.exe clone in fltk ported from the Wine sources

a ma­chine vi­sion thing to count traf­fic flows from pub­lic street cam­eras in opencv

a claude ui clone in python or rust i think, i don’t even re­mem­ber

a re­gional news site i never meant to build that is ac­tu­ally get­ting traf­fic, python/​flask

a 3d car game built on the pro­to­col for an ex­ist­ing mul­ti­player game in three.js

an in­vest­ment back­tester in python

a html clone of the light­room ui, mar­velled at the re­sult then never made the back­end

a mark­down viewer in qt or gtk or some­thing else i can’t even re­mem­ber

a re­place­ment world clock wid­get for my lap­top desk­top en­vi­ron­ment in gtk and C

a javascript net­work syn­chro­nised au­dio play­back thing

a rust client for a chi­nese IP cam­era re­versed from its Android app

a size­able SaaS in rust

maybe 50 other pro­jects i’ve al­ready deleted

Except for the SaaS, al­most none of this is use­ful and I don’t want to main­tain any of it. I ac­ci­den­tally run a news out­let which is surely a li­a­bil­ity. Sure, it has helped me “learn AI tool­ing” and I use many of these tools, but I did­n’t need them. I can’t af­ford to main­tain any of them, not in terms of time, com­mit­ment, be­lief, at­ten­tion or will­ing­ness to spend on to­kens.

I did­n’t mean to build most of these things. Usually the Claude ses­sion started with some­thing like write a quick script for X, and one hour later the re­sult is not a quick script for X, nor in the usual case is my prob­lem solved, what­ever the orig­i­nal itch hap­pened to be.

at­ten­tion is all you need

On that last point, this tech­nol­ogy is hor­rific for at­ten­tion. It’s a ther­monu­clear ADHD am­pli­fier and I have seen the same ef­fect in every sin­gle one of my adult friends. Folk run­ning 3 screens si­mul­ta­ne­ously work­ing on to­tally un­re­lated “projects” they have lit­tle hope of main­tain­ing, and such lit­tle com­mit­ment to the out­come that the time is ob­vi­ously wasted.

In re­cent times, at least once per month some­one sends a screen­shot for an awe­some tool they are work­ing on. I’m like whoa, that’s re­ally some­thing and the sender is ob­vi­ously proud and en­thu­si­as­tic. I try not to ask, but am al­ways think­ing and where will you mar­ket it?, be­cause when the ques­tion is asked of an en­gi­neer, the an­swer is un­changed since be­fore LLMs ex­isted.

I re­cently in­ter­viewed and when the topic of AI us­age came up, the host an­swered some­thing like oh we’re quite light on it, every­one has up to 5 rooms where they man­age their agents and I im­me­di­ately felt a tight­ness in my stom­ach.

I had a vague sense of the ef­fect a few months into us­ing Claude. Later I re­duced my sub­scrip­tion to Pro in the be­lief a quota re­stric­tion would mit­i­gate ex­ces­sive use. Then Claude went through a bad ser­vice pe­riod and I moved to Codex. Codex’s CLI is much nicer than Claude’s and no­tice­ably faster. And us­age started creep­ing back up.

The tech­nol­ogy, when honed, is gen­uinely amaz­ing. Ask it to zero shot a parser for an es­o­teric gram­mar im­ple­mented in an es­o­teric lan­guage with full tests and it’s done. The tool­ing as it ex­ists to­day pro­motes ab­solutely noth­ing like the fo­cus re­quired to ap­ply it ju­di­ciously.

Almost every ven­dor and every tool in­tends to do ex­actly the op­po­site: more us­age, more to­kens, more out­put. Ask a sim­ple yes/​no ques­tion of ChatGPT and you can clearly see that it is hard-wired to in­clude a rel­e­vant fol­low-up ques­tion to pro­mote ex­ces­sive in­ter­ac­tion.

Slopping out a 10,000 LOC untested Python/JS mess in 5 min­utes helps no­body. The thought of this hap­pen­ing in every com­mer­cial en­vi­ron­ment si­mul­ta­ne­ously is hor­ri­fy­ing.

fric­tion = fo­cus, fo­cus = prod­uct

One of my early AI ex­per­i­ments, ex­plor­ing AI as a lens in Marshall McLuhan-like think­ing, was to con­nect speech recog­ni­tion to a pipeline that gen­er­ated blog posts on the other side, in the be­lief it would en­cour­age me to cap­ture my thoughts. All I needed was to press the voice note but­ton in a Telegram chan­nel, and out pops an Opus-formatted post.

The out­put was un­bri­dled garbage. Because the ef­fort was re­moved, so was the com­mit­ment, and with the com­mit­ment the fo­cus, and with the fo­cus any mean­ing­ful prod­uct at all. Quality writ­ing is not con­ver­sa­tional English sim­ply cast through a lens: con­ver­sa­tional English is low-bit rate noise, qual­ity writ­ing at­tempts to cap­ture high bit rate in­for­ma­tion with bet­ter formed con­cepts, and this should have been ob­vi­ous be­fore I be­gan.

I looked at re­pur­pos­ing the pipeline to cap­ture pri­vate notes, but I have no need for pri­vate notes. It sub­verts the nat­ural process of noise be­ing for­got­ten. It is just more ex­cess tool use.

Following from this, for as long as qual­ity mat­ters, I be­lieve hand­writ­ing can never be ob­so­lete.

It feels like we’re head­ing to­wards cri­sis, and I doubt the an­swer is “better mod­els” or “better tool­ing”. Cal Newport re­lates this to pseudo-pro­duc­tiv­ity:

The speaker ar­gues that dig­i­tal pro­duc­tiv­ity tools, in­clud­ing AI and email, of­ten cre­ate a “digital pro­duc­tiv­ity para­dox”: they make in­di­vid­ual tasks faster or eas­ier, but they can leave knowl­edge work­ers busier, more dis­tracted, and less pro­duc­tive over­all. He cites re­search show­ing that AI users spent much more time in email, mes­sag­ing, chat, and busi­ness-man­age­ment tools, while spend­ing less time in fo­cused, un­in­ter­rupted work. His cen­tral claim is that tools de­signed to re­duce fric­tion of­ten in­crease the vol­ume of shal­low tasks and con­text switch­ing, which weak­ens deep work and high-value out­put.

He ex­plains that this hap­pens be­cause knowl­edge work of­ten re­lies on “pseudo pro­duc­tiv­ity,” where vis­i­ble busy­ness is treated as a proxy for real value. Digital tools re­in­force this by mak­ing peo­ple look ac­tive: send­ing more mes­sages, pro­duc­ing more drafts, at­tend­ing more meet­ings, and gen­er­at­ing more work ar­ti­facts. To avoid the trap, he rec­om­mends mea­sur­ing real out­comes, iden­ti­fy­ing the true bot­tle­necks in one’s work, and sep­a­rat­ing deep work from shal­low work so that dig­i­tal tools sup­port mean­ing­ful progress in­stead of con­sum­ing at­ten­tion.

– 🤖

The speaker ar­gues that dig­i­tal pro­duc­tiv­ity tools, in­clud­ing AI and email, of­ten cre­ate a “digital pro­duc­tiv­ity para­dox”: they make in­di­vid­ual tasks faster or eas­ier, but they can leave knowl­edge work­ers busier, more dis­tracted, and less pro­duc­tive over­all. He cites re­search show­ing that AI users spent much more time in email, mes­sag­ing, chat, and busi­ness-man­age­ment tools, while spend­ing less time in fo­cused, un­in­ter­rupted work. His cen­tral claim is that tools de­signed to re­duce fric­tion of­ten in­crease the vol­ume of shal­low tasks and con­text switch­ing, which weak­ens deep work and high-value out­put.

He ex­plains that this hap­pens be­cause knowl­edge work of­ten re­lies on “pseudo pro­duc­tiv­ity,” where vis­i­ble busy­ness is treated as a proxy for real value. Digital tools re­in­force this by mak­ing peo­ple look ac­tive: send­ing more mes­sages, pro­duc­ing more drafts, at­tend­ing more meet­ings, and gen­er­at­ing more work ar­ti­facts. To avoid the trap, he rec­om­mends mea­sur­ing real out­comes, iden­ti­fy­ing the true bot­tle­necks in one’s work, and sep­a­rat­ing deep work from shal­low work so that dig­i­tal tools sup­port mean­ing­ful progress in­stead of con­sum­ing at­ten­tion.

– 🤖

These ex­pe­ri­ences have opened a new per­cep­tion of all tool use, be­cause be­neath it all this is not about faster de­vel­op­ment = more apps or faster email = more com­mu­ni­ca­tion be­ing a de­sir­able goal. Generically, it’s about a unit time of life and how it is spent mean­ing­fully.

I have no idea how to man­age AI at pre­sent ex­cept by cur­tail­ing use, be­cause a tool pro­duc­ing a cheap re­ward with min­i­mal in­put and no fric­tion can only be a li­a­bil­ity, and achiev­ing that re­al­i­sa­tion is prob­a­bly the only real con­tri­bu­tion of AI to date.

David, Sun 31 May 14:31:04 2026

Today we’re re­leas­ing Bonsai Image 4B, a fam­ily of com­pact im­age-gen­er­a­tion mod­els de­signed to run high-qual­ity dif­fu­sion in­fer­ence on lo­cal hard­ware: from lap­tops to phones.

Bonsai Image 4B comes in two vari­ants:

1-bit Bonsai Image 4B uses bi­nary {−1, +1} trans­former weights with an FP16 group-wise scal­ing fac­tor, giv­ing 1.125 ef­fec­tive bits per weight. It tar­gets max­i­mum com­pres­sion and is the right fit when mem­ory pres­sure, band­width, and the de­ploy­ment foot­print are the pri­mary con­straints.

Ternary Bonsai Image 4B uses {−1, 0, +1} trans­former weights with an FP16 group-wise scal­ing fac­tor, giv­ing 1.71 ef­fec­tive bits per weight. The ad­di­tional zero state gives the model more rep­re­sen­ta­tional flex­i­bil­ity, im­prov­ing vi­sual qual­ity and prompt fi­delity while re­main­ing ex­tremely com­pact.

The re­sult is a new de­ploy­ment regime for im­age gen­er­a­tion: ca­pa­ble out­puts, open weights, and prac­ti­cal lo­cal in­fer­ence on de­vices that were pre­vi­ously out of reach for this class of model. To our knowl­edge, Bonsai Image 4B is the first im­age model in its pa­ra­me­ter class to run di­rectly on an iPhone.

Built for lo­cal gen­er­a­tion

Local im­age gen­er­a­tion starts with a hard con­straint: the model has to fit within the de­vice’s mem­ory bud­get.

For a 4B-class im­age model, the dif­fu­sion trans­former is the largest part of the model and the part that runs re­peat­edly dur­ing gen­er­a­tion. Each de­nois­ing step in­vokes the trans­former again, so trans­former size di­rectly shapes mem­ory pres­sure, band­width de­mand, and lo­cal in­fer­ence speed.

Bonsai Image 4B is built from the FLUX.2 Klein 4B. It keeps the ar­chi­tec­ture in­tact but changes how the trans­former weights are rep­re­sented. By mov­ing those weights into bi­nary and ternary form, Bonsai re­duces the part of the im­age pipeline that mat­ters most for lo­cal de­ploy­ment.

Table I: Diffusion trans­former foot­print for mod­els.

The bi­nary lay­ers pro­vide roughly a 14x re­duc­tion rel­a­tive to full-pre­ci­sion trans­former weights. A small set of pre­ci­sion-sen­si­tive sup­port­ing ten­sors (~5%), called the pro­jec­tion lay­ers, re­mains in FP16 so the fi­nal 1-bit Bonsai Image 4B trans­former is 0.93 GB: an 8.3x re­duc­tion from the 7.75 GB full-pre­ci­sion FLUX.2 Klein 4B.

The ternary vari­ant fol­lows the same struc­ture. Its ternary lay­ers pro­vide roughly a 10x re­duc­tion and the fi­nal Ternary Bonsai Image 4B trans­former is 1.21 GB, a 6.4x re­duc­tion from the full-pre­ci­sion trans­former. It is slightly larger than the 1-bit model, but the ad­di­tional zero state im­proves vi­sual qual­ity and prompt fi­delity.

Including the com­pressed text en­coder and FP16 VAE, the Apple Silicon de­ploy­ment pay­load is 3.42 GB for 1-bit Bonsai Image 4B and 3.88 GB for Ternary Bonsai Image 4B. For com­par­i­son, the full pre­ci­sion FLUX.2 Klein 4B re­quires a de­ploy­ment pay­load of 15.97 GB. Since, at run­time, the text en­coder is of­floaded af­ter prompt en­cod­ing, the mean mem­ory us­age is smaller than the to­tal pay­load. When gen­er­at­ing a 512x512 im­age, the mean-ac­tive mem­ory is 1.5 GB and 1.96 GB, for the bi­nary and ternary mod­els, com­pared to 11.74 GB for the orig­i­nal FLUX.2 Klein 4B (a re­duc­tion of 7.8x and 6.0x, re­spec­tively). For a 1024x1024 im­age, the mean-ac­tive mem­ory is 1.95 GB and 2.38 GB, for the bi­nary and ternary mod­els, com­pared to 14.39 GB for the orig­i­nal FLUX.2 Klein 4B (a re­duc­tion of 7.4x and 6.0x, re­spec­tively).

This re­duc­tion in mem­ory foot­print changes where the model can run. Our de­ploy­ment stack sup­ports Apple Silicon iPhones, iPads and Macs and CUDA GPUs, us­ing MLX low-bit paths on Apple hard­ware and Gemlite low-bit GEMM ker­nels on CUDA. On iPhone 17 Pro Max, the full-pre­ci­sion FLUX.2 Klein 4B pipeline does not fit within the de­vice mem­ory bud­get, while both Bonsai Image vari­ants run on-de­vice.

Video I: Image gen­er­a­tion on Bonsai Studio

In prac­tice, Bonsai Image 4B gen­er­ates a 512x512 im­age in 9.4 sec­onds on an iPhone 17 Pro Max and about 6 sec­onds on Mac M4 Pro. On Mac M4 Pro, Bonsai Image 4B is up to 5.6x faster than the stock full-pre­ci­sion MFLUX pipeline.

Benchmarking per­for­mance

Compression only mat­ters if the model re­mains use­ful. We eval­u­ated Bonsai Image 4B across three com­ple­men­tary bench­marks: GenEval for ob­ject com­po­si­tion and at­tribute bind­ing; HPSv3 hu­man pref­er­ence and aes­thetic qual­ity; DPG-Bench dense prompt fol­low­ing and se­man­tic faith­ful­ness.

Table II: Image qual­ity bench­mark com­par­i­son across Ternary Bonsai Image 4B and other mod­els.

Ternary Bonsai Image 4B is the qual­ity-ori­ented vari­ant. At 1.21 GB, it re­tains 95% of the FLUX.2 Klein 4B ac­cu­racy across GenEval, HPSv3, and DPG-Bench, while re­duc­ing the dif­fu­sion trans­former foot­print by 6.4x.

1-bit Bonsai Image 4B is the foot­print-ori­ented vari­ant. It brings the dif­fu­sion trans­former be­low 1 GB, an 8.3x re­duc­tion, while still de­liv­er­ing strong bench­mark scores across the same three eval­u­a­tions (it re­tains 88% of the ac­cu­racy of FLUX.2 Klein 4B).

Together, the two vari­ants move the qual­ity–foot­print fron­tier. Bonsai Image re­mains com­pet­i­tive with mod­ern 4B-class im­age mod­els while us­ing a frac­tion of their dif­fu­sion-trans­former foot­print. At the same time, it sub­stan­tially out­per­forms smaller mod­els with sim­i­lar mem­ory foot­prints. That is the same Pareto shift we have seen in our prior Bonsai lan­guage mod­els. Bonsai Image brings mod­ern dif­fu­sion-trans­former be­hav­ior into a mem­ory range that pre­vi­ously be­longed to much smaller, lower-ca­pa­bil­ity mod­els.

Why this is im­por­tant

Image gen­er­a­tion is not only a model-qual­ity prob­lem. It is also a de­ploy­ment prob­lem.

Cloud APIs will con­tinue to be the right choice for many prod­ucts. But cloud-only gen­er­a­tion im­poses cer­tain prod­uct con­straints: every prompt is a re­mote re­quest, every it­er­a­tion car­ries mar­ginal serv­ing cost, and every in­ter­ac­tion adds round-trip la­tency.

That mat­ters be­cause im­age gen­er­a­tion is nat­u­rally it­er­a­tive. Users rarely stop at one im­age. They re­vise prompts, com­pare out­puts, gen­er­ate vari­a­tions, dis­card fail­ures, and try again. When each at­tempt is a server-side job, the cre­ative loop be­comes some­thing users have to me­ter and wait for.

Local in­fer­ence changes that. Once the model fits on the de­vice, gen­er­a­tion can sit di­rectly in­side the prod­uct ex­pe­ri­ence. It be­comes cheaper to run, faster to it­er­ate on, and eas­ier to use in en­vi­ron­ments where prompts, and gen­er­ated as­sets should re­main pri­vate.

Bonsai Image 4B is a step to­ward that de­ploy­ment regime: ca­pa­ble im­age gen­er­a­tion run­ning closer to the user, on hard­ware they al­ready own.

Availability

Both 1-bit and Ternary Bonsai Image 4B will be re­leased with open weights and code un­der the Apache 2.0 li­cense.

With this launch, we are also launch­ing Bonsai Studio, its iOS app for try­ing Bonsai Image 4B di­rectly on iPhone.

Join Us

PrismML emerged from a team of Caltech re­searchers and was founded with sup­port from Khosla Ventures, Cerberus and Google. We’ve spent years tack­ling one of the field’s hard­est prob­lems: com­press­ing neural net­works with­out sac­ri­fic­ing their rea­son­ing abil­ity.

If you want to help build the next gen­er­a­tion of state-of-the-art AI, we’d love to hear from you. Check out our ca­reers page.

Resources

Whitepaper

Hugging Face

WebGPU demo

Bonsai Studio for iPhone

GitHub

I al­ready had an RTX 4080. 16GB of VRAM. Good enough for gam­ing, not good enough for the mod­els I wanted to run lo­cally. The next step up in GPU land is ei­ther spend a for­tune on a card with more VRAM, or find an­other way.

I found an­other way.

I bought a dat­a­cen­ter GPU that does­n’t even have a nor­mal PCIe con­nec­tor, stuck it in my gam­ing PC with an adapter, and now I have 32GB of VRAM across two GPUs run­ning a 27 bil­lion pa­ra­me­ter model at 32 to­kens per sec­ond. The whole thing cost me £200.

The GPU#

This is a Tesla V100 SXM2 16GB. It was de­signed for NVIDIA’s DGX servers and hy­per­scaler racks. The SXM2 form fac­tor means it does not have a PCIe slot. It does not have dis­play out­puts. It does not have a nor­mal power con­nec­tor. It sits on a pro­pri­etary board in­side a server rack and com­mu­ni­cates over NVLink.

You can­not plug this into a moth­er­board. Not with­out help.

But here is the thing: this is a Volta GPU with 16GB of HBM2 mem­ory, 5120 CUDA cores, and I picked it up for about £150 on eBay. The com­pute is still real. The VRAM is still real. And the mem­ory band­width is where it gets gen­uinely sur­pris­ing.

HBM2 is a dif­fer­ent class of mem­ory. The V100 has a 4096-bit mem­ory bus de­liv­er­ing 900 GB/s of band­width. To put that in per­spec­tive, my RTX 4080 with its fancy GDDR6X man­ages 736 GB/s. The V100 from 2017 has 22% more mem­ory band­width than a GPU that launched in 2022.

And it is not just NVIDIA’s con­sumer cards that lose. Apple’s M3 Max does 400 GB/s. The M4 Max does 546 GB/s. The brand new M5 Max, which will set you back over £3,000 for a lap­top, man­ages 614 GB/s. A GPU from 2017 beats every Mac on the mar­ket.

The clos­est AMD com­pe­ti­tion to my 4080 is the RX 7900 XTX, which does 960 GB/s on its 24GB of GDDR6. Technically that edges out the V100, but the 7900 XTX costs £700+ and ROCm sup­port for LLM in­fer­ence is still rough com­pared to CUDA. The V100 gives you 94% of that band­width for less than a quar­ter of the price, and it just works with llama.cpp.

The only con­sumer GPU that com­fort­ably beats it is the RTX 5090 at 1,792 GB/s, and that card costs over £2,000. For LLM in­fer­ence, where mem­ory band­width is the bot­tle­neck that de­ter­mines your to­kens per sec­ond, this mat­ters more than al­most any­thing else.

The only prob­lem is the con­nec­tor.

The adapter#

Turns out, some­one makes an SXM2-to-PCIe adapter. It is not made by NVIDIA. It is not of­fi­cially sup­ported by any­one. It is a bare PCB with the SXM2 socket on one side and a PCIe edge con­nec­tor on the other. I paid about £50 for it. Half of that might just be the cop­per.

So for about £200 to­tal, I had a 16GB VRAM GPU that could slot into my moth­er­board along­side my RTX 4080. That is 32GB of to­tal VRAM. A sin­gle RTX 5090 with 32GB costs over £2,000. I am not say­ing this is the same ex­pe­ri­ence. I am say­ing the VRAM is the same.

The fan from hell#

Before I could do any­thing use­ful with the V100, I had to deal with the fan.

The V100 SXM2 was de­signed to live in­side a 2U server with in­dus­trial cool­ing. The fan on the adapter is not sub­tle. It is not quiet. It is not some­thing you want in a room you also sleep in.

I mea­sured it with my Apple Watch:

82 deci­bels. That is some­where be­tween a garbage dis­posal and a lawn­mower, well past “loud PC” and into “should I be wear­ing earplugs in my own house” ter­ri­tory.

And the worst part: you can­not con­trol it. I tried nvidia-smi, I tried scan­ning for it on Linux, I even tried Afterburner on Windows (more on that later, the whole setup barely works on Windows). Nothing. The fan on this adapter is not de­signed to be con­trolled. It is de­signed to run at 100%, for­ever, in­side a server rack where no­body has to hear it.

Here is me try­ing to fig­ure out the fan pinout. I guessed it might be a stan­dard case fan pinout on a weird con­nec­tor, so I jammed two jumper wires into VCC and ground and prod­ded a 9V bat­tery against them. It spun. And it was so much qui­eter than the 12V it nor­mally gets:

That con­firmed the pinout and gave me hope that the fan could ac­tu­ally be tamed.

Making the fan lis­ten to rea­son#

The 9V bat­tery test told me the pinout was stan­dard case fan ter­ri­tory, just with a weird con­nec­tor. The next ques­tion was whether the fan would ac­tu­ally re­spond to PWM con­trol if I wired the tachome­ter and PWM pins to my moth­er­board.

So I shoved some jumper wires into the con­nec­tor and jammed the other ends into a spare fan header (turn your vol­ume up):

It works. The moth­er­board can read the RPM and the fan re­sponds to PWM. I keep it at 10%. It never goes above 50C even at full load, and I can­not re­ally hear it.

Now I just needed a proper ca­ble in­stead of jumper wires held in by hope.

The fan con­nec­tor on the adapter is a small JST PH2.0 plug with four pins. Motherboard fan head­ers use a stan­dard 0.1 inch (2.54mm) pitch. The GPU fan uses a 2.0mm JST PH con­nec­tor. The pins are closer to­gether and the plug is smaller.

The so­lu­tion was a 2.54mm male to PH2.0 fe­male jumper ca­ble. The fe­male PH2.0 end plugs into the fan’s tachome­ter and PWM pins, and the male 2.54mm end goes into a spare fan header on the moth­er­board:

That went from 82dB ear dam­age to some­thing I can ac­tu­ally live with.

Doubling VRAM for cheap#

With the fan sit­u­a­tion han­dled, the V100 slot­ted right in along­side my 4080:

RTX 4080: 16GB VRAM, Ada ar­chi­tec­ture

Tesla V100: 16GB VRAM, Volta ar­chi­tec­ture

Total: 32GB VRAM across two GPUs

llama.cpp can split the model across both GPUs us­ing ten­sor split­ting. It pipelines the lay­ers across the PCIe bus so the 4080 han­dles some lay­ers and the V100 han­dles the rest. It is not as fast as hav­ing a sin­gle GPU with 32GB, but it works, and it cost me roughly 10% of what a 32GB GPU would cost. For what it is worth, the most I have ever seen the V100 pull is around 150W. That is not noth­ing, but it is not out of this world for a GPU run­ning lo­cal LLM in­fer­ence.

But wait, you can go big­ger#

The V100 also comes in a 32GB vari­ant. It costs more than dou­ble what I paid, but we are still talk­ing about a few hun­dred pounds for 32GB of HBM2 mem­ory on a sin­gle card. Two of those would give you 64GB of VRAM for roughly 20% of what an RTX 5090 costs in to­day’s mar­ket.

You can also clus­ter them. The SXM2 for­mat sup­ports NVLink na­tively, which means if you are build­ing a proper multi-GPU setup, these cards can talk to each other at very high band­width. Even through the PCIe adapter, the ten­sor split per­for­mance is solid.

The soft­ware side#

This part was sur­pris­ingly smooth thanks to NixOS. The V100 is a Volta chip. NVIDIA dropped Volta sup­port start­ing with dri­ver branch 560. The last dri­ver that sup­ports both my RTX 4080 (Ada) and the V100 (Volta) is branch 550.x, which maps to nvidi­a­Pack­ages.lega­cy_535 on NixOS.

That dri­ver only sup­ports CUDA up to 12.2. Current nix­p­kgs ships CUDA 12.6 min­i­mum. So I had to pull CUDA 12.2 from nix­p­kgs 24.05.

Also, the dri­ver re­quires ker­nel 6.6. Newer ker­nels are not sup­ported with the legacy dri­ver.

And here is a weird one: even though this is a head­less in­fer­ence server, ser­vices.xserver.en­able = true is re­quired. Without it, the NVIDIA ker­nel mod­ules do not load.

NixOS made most of this straight­for­ward. Here is the key con­fig­u­ra­tion for get­ting the dri­ver and ker­nel right:

boot.ker­nel­Pack­ages = pkgs.lin­ux­Pack­ages_6_6; hard­ware.nvidia.pack­age = con­fig.boot.ker­nel­Pack­ages.nvidi­a­Pack­ages.lega­cy_535; ser­vices.xserver.en­able = true; ser­vices.xserver.video­Drivers = [ “nvidia” ];

And for load­ing CUDA 12.2 from an older nix­p­kgs since the cur­rent one only ships 12.6+:

nix­p­kgs.over­lays = [ (final: prev: { cu­d­a­Pack­ages_12_2 = nix­p­kgs-cuda.lega­cy­Pack­ages.${prev.sys­tem}.cu­d­a­Pack­ages_12_2; }) ];

The im­por­tant thing is: it works. Both GPUs show up, CUDA is func­tional, and NixOS han­dled the whole thing el­e­gantly. If you want to repli­cate this, the en­tire ma­chine de­f­i­n­i­tion is in this com­mit on my dot­files repo, in­clud­ing the llama.cpp ser­vice de­f­i­n­i­tion and the cus­tom build pinned to the right ver­sion.

Running the model#

I am run­ning Qwen3.6 – 27B-MTP quan­tized at Q5_K_M, which comes in at about 19GB. With both GPUs, the en­tire model fits in VRAM with room for con­text:

And the per­for­mance:

32 to­kens per sec­ond is fast enough for in­ter­ac­tive use. It is faster than most cloud API end­points when you fac­tor in net­work la­tency. And this is with ten­sor split­ting across two dif­fer­ent GPU ar­chi­tec­tures con­nected by PCIe.

This model is ac­tu­ally good#

I want to be clear about some­thing. This is not “good for a lo­cal model.” This is not “acceptable if you lower your ex­pec­ta­tions.” Qwen3.6 – 27B ties with Claude Sonnet 4.6 on Artificial Analysis’s Agentic Index. It beats Sonnet 4.6 on MMMU-Pro and Terminal-Bench 2.0. A 27 bil­lion pa­ra­me­ter model run­ning on sec­ond­hand hard­ware is gen­uinely com­pet­i­tive with the lat­est cloud mod­els from Anthropic.

Yes, Sonnet 4.6 edges it out on GPQA and SWE-Bench Verified. It should, it is a mas­sive pro­pri­etary model. And yes, if you want the ab­solute best, Opus 4.8 ex­ists. It also costs more per 20 min­utes of heavy use than I paid for this en­tire GPU and adapter setup com­bined. But the gap is shock­ingly small. We have reached the point where the model you run in your bed­room is in the same con­ver­sa­tion as the ones that charge you per to­ken.

Multi-Token Prediction#

The MTP in the model name stands for Multi-Token Prediction. Normal LLM in­fer­ence pre­dicts one to­ken at a time. Predict one to­ken, ac­cept it, pre­dict the next to­ken, re­peat. MTP changes this by hav­ing the model pre­dict sev­eral fu­ture to­kens at once, then ver­i­fy­ing which ones were cor­rect. Accepted to­kens are es­sen­tially free. Wrong pre­dic­tions fall back to the nor­mal path.

The re­sult is roughly 1.5 – 2x faster gen­er­a­tion with no ac­cu­racy loss. On my setup that means in­fer­ence goes from around 32 tok/​s to po­ten­tially 50 – 60 tok/​s when MTP hits its stride, es­pe­cially on pre­dictable out­put like code.

The catch is that MTP sup­port in llama.cpp is new. The ver­sion in nix­p­kgs does not sup­port the Qwen3.6 MTP ar­chi­tec­ture, so I had to build llama.cpp from source at a spe­cific com­mit that added sup­port. On NixOS this is pain­less. I have a cus­tom de­riva­tion pinned to the right com­mit, and the whole thing is re­pro­ducible. When I want to up­date the model or change the llama.cpp ver­sion, I change one line in my con­fig, run nixos-re­build switch, and I am done. No de­pen­dency hell, no re­in­stalling by hand, no won­der­ing whether I built against the right CUDA ver­sion.

Vision: how the model sees im­ages#

The Qwen3.6 – 27B model sup­ports im­age in­put through a sep­a­rate mul­ti­modal pro­jec­tor file (mmproj). This is about 928MB ex­tra, and it is fas­ci­nat­ing.

The way it works is that a vi­sion en­coder (similar to what ChatGPT and Claude use) takes im­age pix­els and trans­lates them into the LLM’s to­ken em­bed­ding space. The model does not “see” the im­age the way a hu­man does. Instead, the vi­sion en­coder com­presses the im­age into a se­quence of vec­tors that live in the same math­e­mat­i­cal space as text to­kens. The LLM then processes those vec­tors as if they were just an­other se­quence of to­kens.

What this means in prac­tice: you send the model an im­age URL along­side your text prompt, and it can de­scribe, an­a­lyze, and rea­son about what it sees. The en­tire vi­sion ca­pa­bil­ity adds about 1GB to the model size. That is it. One gi­ga­byte and your lo­cal LLM can read im­ages.

In llama.cpp, the flags are straight­for­ward:

–mmproj /mnt/nas/llamacpp/mmproj-F16.gguf –mmproj-offload

The –mmproj-offload flag loads the vi­sion en­coder onto GPU along­side the model, so you still get fast in­fer­ence even with im­ages.

Running it through OpenCode#

I use this setup with OpenCode, which is an AI cod­ing as­sis­tant that can run against lo­cal mod­els. The LLM server runs on my desk­top, but I do not use it from that ma­chine. I use it from any other ma­chine in my house over the net­work, or from out­side over Tailscale (but that is a blog post for an­other time). Pointing OpenCode at the llama.cpp server is as sim­ple as set­ting the API URL. The model runs lo­cally, the re­sponses are fast, and noth­ing leaves my net­work.

The NAS and the USB drive#

All the mod­els live on my TrueNAS server, mounted via NFS:

fileSys­tems.“/​mnt/​nas” = { de­vice = “truenas-nfs.tymscar.com:/mnt/oasis/services”; fsType = “nfs”; op­tions = [ “nfsvers=4″ “_netdev” “auto” “nofail” ]; };

The llama.cpp ser­vice de­pends on mnt-nas.mount, so it does not start un­til the NAS is avail­able. This means I can store ter­abytes of mod­els with­out wor­ry­ing about lo­cal disk space.

The en­tire OS runs from a Corsair MP600 MINI in a DockCase USB-C NVMe en­clo­sure. No in­ter­nal drive mod­i­fi­ca­tion needed. When I want to game, I un­plug the drive and re­boot into my main Windows in­stall, and game nor­mally on the 4080. When I want to do LLM stuff, I plug the drive back in, re­boot into NixOS, and both GPUs are avail­able.

This is not as el­e­gant as a dual-boot menu, but it is sim­ple and it works. No GRUB, no boot­loader con­flicts, no par­ti­tion man­age­ment. Just a phys­i­cal switch.

The one an­noy­ing thing#

The V100 oc­ca­sion­ally dis­ap­pears from lspci and nvidia-smi af­ter a warm re­boot (where the OS restarts but the moth­er­board stays pow­ered). This seems to be an ACPI enu­mer­a­tion is­sue with the PCIe slot. A cold re­boot (physically power off, wait a few sec­onds, power back on) al­ways re­stores it.

When the V100 is ab­sent, llama.cpp fails to start be­cause it can­not fit the model on a sin­gle 16GB GPU. The ser­vice crash-loops un­til the GPU comes back. This is not a big deal in prac­tice since I am usu­ally around when I re­boot, but it is worth know­ing about. It gives me the same vibes as the in­fa­mous AMD GPU re­set bug, where pass­ing through an AMD GPU to a VM and then shut­ting it down leaves the GPU in a state that only a full host power cy­cle can fix.

What I ended up with#

For £200, I got:

A 16GB dat­a­cen­ter GPU run­ning along­side my gam­ing GPU

32GB to­tal VRAM for lo­cal LLM in­fer­ence

32 to­kens per sec­ond on a 27B pa­ra­me­ter model

128k to­ken con­text win­dow

Vision sup­port for im­age in­put

A model that runs com­pletely lo­cally, no cloud, no per-to­ken costs

The only real cost was the noise, and I solved that with £2 worth of jumper ca­bles and a bit of con­nec­tor spelunk­ing. The V100 is not the fastest GPU for in­fer­ence, and the ten­sor split across two dif­fer­ent ar­chi­tec­tures is not as clean as a sin­gle GPU. But for the price, it is ab­surdly good value.

If you want to run proper mod­els lo­cally, look at the sec­ond­hand server GPU mar­ket. You do not even need an ex­ist­ing GPU. I hap­pen to have a 4080 in my gam­ing PC, but a sin­gle V100 in a cheap server box would give you 16GB of VRAM and a per­fectly us­able lo­cal LLM for very lit­tle money. The V100 SXM2 is not the only op­tion. The P40 gives you 24GB for sim­i­lar money, though it is slower and has no Tensor Cores. The V100 32GB vari­ant costs more but still un­der­cuts any con­sumer GPU with that much VRAM.

Just be ready for the fan.

Published May 31, 2026, 6:26 AM EDT

Luke has over a decade of ex­pe­ri­ence as a travel writer and avi­a­tion an­a­lyst. As a pas­sion­ate trav­eler based across the Middle East and Asia, Luke of­fers strong in­sights into the in­dus­try. Based in South East Asia.

A United Airlines Boeing 767 – 400ER bound for Palma de Mallorca, Spain, made a mid-At­lantic U-turn af­ter a pas­sen­ger’s threat­en­ing Bluetooth speaker name trig­gered a se­cu­rity alert. Early re­ports in­di­cate that a teenage pas­sen­ger on­board named their de­vice ‘BOMB,’ and the dis­cov­er­able name es­ca­lated quickly into a bomb-threat re­sponse.

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United Airlines Bluetooth Threat Incident

According to flight track­ing data, United Flight 236 from Newark Liberty International Airport (EWR) to Palma De Mallorca Airport (PMI) de­parted Newark at 6:08 PM lo­cal time, and was ap­prox­i­mately 60 min­utes into its transat­lantic jour­ney be­fore the se­cu­rity sit­u­a­tion es­ca­lated. A pas­sen­ger on the flight pro­vided more de­tails on Reddit, stat­ing that a flight at­ten­dant told pas­sen­gers over the PA sys­tem that they “must turn off Bluetooth im­me­di­ately,” or else the air­craft would have to turn around.

Date

May 30, 2026

Airline

United Airlines

Flight Code

UA236

Aircraft Type

Boeing 767 – 400ER (N67052)

Departure Airport

Newark Liberty International Airport (EWR)

Destination Airport

Palma de Mallorca Airport (PMI)

Fate

Returned to EWR; pas­sen­gers boarded a re­place­ment flight

This was re­peated mul­ti­ple times, with the crew even­tu­ally is­su­ing a fi­nal one-minute warn­ing. However, not all pas­sen­gers com­plied with the in­struc­tions, as there were still two ac­tive Bluetooth de­vices af­ter the ul­ti­ma­tum was is­sued. The air­craft sub­se­quently squawked 7700 (the code for a gen­eral emer­gency) and turned around, land­ing back in EWR at 8:50 PM af­ter spend­ing al­most three hours in the air. Simple Flying con­tacted United for com­ment on this in­ci­dent, but a rep­re­sen­ta­tive could not be reached be­fore pub­li­ca­tion.

Bluetooth Speaker Name Set To ‘BOMB’

As per record­ings from LiveATC.net, a mem­ber of United’s ground team said that the Bluetooth speaker name had been set to a “four-letter word,” later re­ported by AirLive as ‘BOMB.’ Passengers on the flight were re­port­edly told that up to ten “agents” would be wait­ing for the air­craft in Newark to de­ter­mine the ori­gin of the threat.

Those on­board were also in­structed to leave all their be­long­ings on the air­craft be­fore de­plan­ing. Saturday’s in­ci­dent has par­al­lels with an­other se­cu­rity scare that oc­curred on a United flight ear­lier this month. During this in­ci­dent, a Wi-Fi hotspot named “Free Palestine, F Zionists” prompted the pi­lot to is­sue a warn­ing to the cabin, telling the pas­sen­ger re­spon­si­ble that they had “30 sec­onds” to re­move the name or the FBI would meet the air­craft.

Additionally, in April, two United flights were evac­u­ated on back-to-back days due to bomb threats, demon­strat­ing how se­ri­ously these in­ci­dents are taken. Though some have ques­tioned why any­one in­tend­ing to blow up a plane would broad­cast the word bomb, many ter­ror­ist acts have re­lied on the threat of a bomb as lever­age dur­ing at­tempted hi­jack­ings or hostage sit­u­a­tions.

Related

Passengers Board Replacement Flight

Passengers on the flight ar­rived back in Newark just be­fore 9:00 PM on Saturday evening, and were met by a sig­nif­i­cant con­tin­gent of lo­cal and fed­eral law en­force­ment. They were asked to take only their pass­ports and phones with them, leav­ing their cabin bags on the air­craft. After spend­ing sev­eral hours on the ground as se­cu­rity teams com­pleted their sweep, trav­el­ers would even­tu­ally de­part Newark on a re­place­ment flight in the early hours.

The re­place­ment flight was op­er­ated by the same air­craft, a Boeing 767 – 400ER (registration N67052), but would not take off un­til around 02:30 AM the next day. At the time of pub­li­ca­tion, the flight is cur­rently over the Atlantic and is ex­pected to land in Palma de Mallorca in the af­ter­noon lo­cal time. Before pas­sen­gers could board this flight, they were re­quired to pass through TSA se­cu­rity for a sec­ond time.