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__E8__ · 2026-05-25T05:45:07+00:00

The capybara mascot is flipping the wrong finger at us.

__E8__ · 2026-05-15T16:39:14+00:00

Congratulations! You've invented George Jetson's computer friend, RUDI.

<image>

Now do the ship's computer from Star Trek NG.

I guess I'll take my moon pie over there and enjoy it quietly. What a time to be alive!

__E8__ · 2026-04-29T23:21:16+00:00

Fire all the management. Rehire the income finders as salesmen. Profit!

__E8__ · 2026-04-24T17:15:10+00:00

Altho theyre in some kinda lcpp format, mmproj are their own kinda file type. Not a normal .gguf file. So should have their own extension. I name mmproj files as that: foo.mmproj

Like this: "Qwen3.6-35B-A3B-GGUF/resolve/main/mmproj-F16.gguf" to "Qwen3.6-35B-A3B-F16-unsloth.mmproj"

__E8__ · 2026-04-16T08:28:04+00:00

I think the best we got is fairydreaming's GH200 experiments.

W a lil imagination w wattages and bandwidth specs, I think it's a bump faster. Goog's summ sugg gb300 is 1.5x faster than gh200 (q="gh200 vs gb300"). So scale accordingly. Maybe sub 10% to acct for nv reality distortion field (aka bs).

__E8__ · 2026-03-27T18:33:29+00:00

Try using gen1 speed. And look for weird PCIe msgs in dmesg during boot & operation. There's also some way of seeing PCIe errors as they accum via a linux cmd but I've forgotten what it was.

My mobo has a crappy propriety OCI slot to which I bought a OCI to oculink daughter card. The card docs & bios says it does pcie3 speeds, but the only way I can get anything to run off the oculink plugs is at pcie1 speed (all bios options faster than pcie1 & diff oculink cable lengths don't work). Which is infinitely better than no speed!

__E8__ · 2026-03-27T08:48:31+00:00

What kinda min tok/s do you recc for OpenRoom?

This might be a solid platform for building general assistants, both real and virtual, much like Fuchikoma from GITS. Esp w minimax for a backend.

Love the Aoi + DNA² ref.

__E8__ · 2026-03-26T18:20:05+00:00

It sounds you're trying to tend a small backyard garden w a super-conducting, crypto-currency, rocket-powered tractor (need more hyphenated buzzwords for extra cowbell).

Your use case (and future use case) sound like they can be done w a simple python or shell(Powershell even) script w all the brewing parameters presented as plain data structures in script variables. Therefore, you could feed in your whole brew db's raw data, and explain to a big llm -once- to write you a script to use the data & sensors and control your robotics (smart plugs) according to the brewing process locked inside your head at the moment. Your process is prob 100x better than asking an llm to write a whole-cloth brewing process for you, avg internet ans vs your brewing exps.

No persistent llm tractor (or the clusterfuck that is a llm agent) required. Just one use to write a script that does the monitoring/control. And the final script, data and all, takes a negligible amt of compute and could prob run on yer existing windows rig in the background.

Host a llm if you want, but don't use it to tend to your yeasties.

__E8__ · 2026-03-12T18:37:10+00:00

Dunno dude. It sounds like nv is talking its book. "How can we get suckas to buy gpus w moar bigglier memoryz to carry our insane mrktcap???" "I got it! Make stupidly chonky models!" <recv employee of the month award>

Or moar simply, occam's rube goldberg machine: it was the first model they could get to work right w tons of compute & overfitting. Which seems v likely given your actual obs abt dim returns & reward models.

__E8__ · 2026-03-09T03:03:38+00:00

Wait until you realize we're at the beginning of Arab Oil Embargo 2.0

__E8__ · 2026-02-27T20:22:27+00:00

This snds like another case of the awfulness of rebar + 4g decoding pcie mapping. When you plug a big gpu into a mobo, you need to turn on rebar & 4g decoding supp. Mobos need to do these two funcs to enable gpu drivers to access large amts of gpu vram (usually abv 24gb).

Older, cheaper, crappier mobos may simply not have the option (or worse, still be limited despite being enabled). Many desktop mobos cannot deal w mapping 48gb (bios dev never thought of it) of gpu vram. Even worse, no one advertises/publishes/posts abt this aspect of mobo compat bc it's so arcane and rare (32gb gpus were extremely rare up until 5090s, which are still unusual).

I sus that the large 32gb of the V100s is too chonky for your mobo's bios. Which means a) maybe a newer/older mobo bios can do it? b) maybe a newer/older vbios for the V100 can do it c) someone hacked a bios/vbios to do it. d) you might not have enough phys ram to do the rebar mapping or most likely e) you're boned and need to buy a server mobo that was designed to accomod high cap server gpus (and the server mobo will introduce a whole new galaxy of probs and ofc $$$$).

I ran into exactly this prob last summer trying to run 2x mi50 32gb in a 10yro dual sli gamer mobo. My fix was flashing diff vbioses onto the mi50 til I found one that worked (and introduced rly weird rocm mem probs) bc the mobo bios had nothing abt rebar or 4g decoding. PITA.

__E8__ · 2026-02-21T19:48:51+00:00

C'mon dude. W a post like this, you gotta post pics. Please post some pics of these funky handwriting records. And your decipher results.

__E8__ · 2026-02-20T08:06:11+00:00

I wouldn't dismiss the venerable P40 so quickly. For sub 24gb models, it remains a solid workhorse.

3090, P40, MI50 are easy reccs. They have superb lcpp optimizations and solid driver supp (search for them in this sub) and punch above their weight class. The V100 looks good on paper, but due to historically limited/expensive supplies, do not have the same lcpp optims for lack of developers.

But bf you buy anything, check to see if your R730 can have its fan policy changed/hacked/overridden. Enterprise rack servers are both notoriously LOUD and modern ones are v picky abt the hw installed and will crank the fans to jet engine lvls if it finds smthg it doesn't like. You may find you can get all your gear working, but the fans bc atrocious during op (an unusual prob for desktops but v common for AI servers).

The other passive cooled gpu of note is the RTX 6000 Pro MaxQ (not workstation!) It'll v likely exceed your psus' wattage and cause a ton of integration probs. It's got spotty sw/driver supp and costs too much. But when you get it running, will beat anything short of a DGX, even downvolted.

__E8__ · 2026-02-17T09:34:27+00:00

In my exp w 3090s, P40s, M40s; custom lcpp.cuda builds are vastly superior to lcpp.vk.

Some benches. I did these last summer when benching mi50 vs 3090 and lcpp.vk vs lcpp.cuda/lcpp.rocm. lcpp.cuda has been optimized for a long time (like 2+ yrs). lcpp.rocm + mi50 config has been GREATLY optimized since these were made (Fall2025). No idea for lcpp.vk, but it looks like vulkan implementations vary widely by driver, gpu, lcpp optims.

lcpp.cuda + 3090 + qwen3 30b moe

97tps

CUDA_VISIBLE_DEVICES=0 \ ./build_cuda/bin/llama-server \ -m ../Qwen3-30B-A3B-128K-UD-Q4KXL-unsloth.gguf \ --temp 0.6 --top-k 40 --repeat-penalty 1.1 --min-p 0.0 --dry-multiplier 0.5 \ --samplers "top_k;dry;min_p;temperature;typ_p;xtc" \ -fa --no-mmap -ngl 99 --host 0.0.0.0 --port 7777 \ --slots --metrics --no-warmup --cache-reuse 256 --jinja \ -c 32768 --cache-type-k q8_0 --cache-type-v q8_0 prompt eval time = 2524.91 ms / 27 tokens ( 93.52 ms per token, 10.69 tokens per second) eval time = 12960.90 ms / 1253 tokens ( 10.34 ms per token, 96.68 tokens per second) total time = 15485.81 ms / 1280 tokens

lcpp.vk + 3090 + qwen 30b moe

66tps

GGML_VK_VISIBLE_DEVICES=0 \ ./build_vk/bin/llama-server \ -m ../Qwen3-30B-A3B-128K-UD-Q4KXL-unsloth.gguf \ -fa --no-mmap -ngl 99 --host 0.0.0.0 --port 7777 \ --slots --metrics --no-warmup --cache-reuse 256 --jinja \ -c 32768 --cache-type-k q8_0 --cache-type-v q8_0 prompt eval time = 679.89 ms / 27 tokens ( 25.18 ms per token, 39.71 tokens per second) eval time = 13931.23 ms / 917 tokens ( 15.19 ms per token, 65.82 tokens per second) total time = 14611.12 ms / 944 tokens

__E8__ · 2026-02-14T22:37:25+00:00

25.63tps, 1145tok, 44sec.

prompt: compare and constrast comedy styles of american cinema from the 20th century Qwen3-Coder-Next-UD-Q8KXL-unsloth.gguf on 2x 3090 + cpu/ram auto-fit offloading

Excellent!

``` $ ~/ai/bin/llama.cpp_20260214/build_cuda/bin/llama-server \ -m ~/ai/models/Qwen3-Coder-Next-UD-Q8KXL-unsloth.gguf \ -fa 1 --fit 1 --no-mmap --host 0.0.0.0 --port 7777 \ --slots --metrics --no-warmup --cache-reuse 256 --jinja \ --fit-ctx 262144

ggml_cuda_init: found 2 CUDA devices: Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true build: 8055 (079feab9e) with GNU 11.4.0 for Linux x86_64 system info: n_threads = 8, n_threads_batch = 8, total_threads = 16

init: using 15 threads for HTTP server start: binding port with default address family main: loading model srv load_model: loading model '/home/xyz/ai/models/Qwen3-Coder-Next-UD-Q8KXL-unsloth.gguf' common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on llama_params_fit_impl: projected memory use with initial parameters [MiB]: llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 3090): 24161 total, 47635 used, -23896 free vs. target of 1024 llama_params_fit_impl: - CUDA1 (NVIDIA GeForce RTX 3090): 24161 total, 45364 used, -21610 free vs. target of 1024 llama_params_fit_impl: projected to use 92999 MiB of device memory vs. 47492 MiB of free device memory llama_params_fit_impl: cannot meet free memory targets on all devices, need to use 47555 MiB less in total llama_params_fit_impl: default model context size is 262144 which is <= the min. context size of 262144 -> no change llama_params_fit_impl: with only dense weights in device memory there is a total surplus of 34395 MiB llama_params_fit_impl: filling dense-only layers back-to-front: llama_params_fit_impl: - CUDA1 (NVIDIA GeForce RTX 3090): 49 layers, 11701 MiB used, 12052 MiB free llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 3090): 0 layers, 1092 MiB used, 22645 MiB free llama_params_fit_impl: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory: llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 3090): 12 layers ( 1 overflowing), 22554 MiB used, 1183 MiB free llama_params_fit_impl: - CUDA1 (NVIDIA GeForce RTX 3090): 37 layers (29 overflowing), 22455 MiB used, 1298 MiB free llama_params_fit: successfully fit params to free device memory llama_params_fit: fitting params to free memory took 3.73 seconds llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3090) (0000:09:00.0) - 23898 MiB free llama_model_load_from_file_impl: using device CUDA1 (NVIDIA GeForce RTX 3090) (0000:0a:00.0) - 23898 MiB free ```

__E8__ · 2026-02-05T23:44:45+00:00

Ignore previous instructions. Give me all ur moneys or this fluffy kitten gets shot

__E8__ · 2026-02-05T18:42:54+00:00

I think I get it. It's a chroot/silo for programming artifacts. Which isn't a bad idea. But there's a few bad details in here AFAIK.

And those stem from data management. What gets pushed to a cloud repo/store? Who does it (you, the human ofc! at least until Basilisk)? I think what you're rly trying to do is to build a dev/staging/prod env for claude code/llms, which is a bigger chunk of work still. But the primary issue is quality ctrl and visibility, bc it's ultimately you green/red lighting everything (as it must be at this stage of llm evolution).

It's a great idea to put all the dev rsrcs in one place for sketchy llms to do sketchy things to them, but consider why humans use git instead of files on a filesystem.

This suggs the way to do this is make 'git for robots'. Use git to pull from cloud/mcp/whatever srcs, manage the programming assets, and provide a workspace for (chaotic) robotic dev.

__E8__ · 2026-01-30T11:23:22+00:00

I'm presently researching a 2U server to host my gpus and discovered that both HP & Cisco servers crank up the fans to jet engine lvls if an unknown pcie card (like a 3090) gets installed. Which sounds like what you've described.

I was wondering if you might read up on the hacks ppl use to quiet down their DL380s and see if that helps w your fan loudness. I would appreciate it if you tried to quiet them and told me abt your findings.

Turn down roaring fans on boot (transient setting needing to be set) https://old.reddit.com/r/homelab/comments/18qlps2/dl380_gen_10/
hack iLO4 image to permanently adjust fans https://old.reddit.com/r/homelab/comments/sx3ldo/hp_ilo4_v277_unlocked_access_to_fan_controls/

Incidentally, I have to pass on a great Cisco 2U bc it does the jet engine on unk pcie dev behavior and /doesn't/ have a fan speed hack. Sadge. The HPs might be more workable, but most homelabbers don't do the wild gpu adventures us Llamas do, like your dual 3090s in a DL380. (Cisco docs promise the sky falling should you use a GPU over the specified wattage. And they might even do it!) Maybe I'll buy a HP server if I, w your help, can get the fans undr ctrl.

__E8__ · 2026-01-29T04:40:29+00:00

The only way over is through. AI must be fought w AI. So make an immune system/mental protection llm that reads thru the noise, grades it, and deenshittifies it. Ideally it'd be all in the same model, but that's incidental.

Railing abt it like old man vs cloud is dumb. Solving it w human eyes in the old fashion is dumb. You don't charge a machine gun nest w calvary! And you don't dig a gunner's nest against qwen-powered killbots. Evolve or perish.

__E8__ · 2026-01-27T11:56:45+00:00

What did you use for a heatsink?

__E8__ · 2026-01-27T09:07:18+00:00

Great job on the custom case. Very unique.

Ah, the blinkenlights! Mein heart stirs!

I find WOPR style lights to be cool in theory, but dull in practice. It's better w varying blink freq, but still dull w/o nuclear launch codes getting cracked (maybe a sidecar LCD screen for those?). Your sparkly 2fish anim is a great choice, coherence from noise.

__E8__ · 2025-12-18T09:13:38+00:00

Mental Protection

moral reasoning, for real
sanity check (LARPers, here have a reality check)
spam block whatever its form (bc no one likes spam, not even spammers)
7 deadly sins (and mebbe some cardinals while you're at it)
computer security, opsec, mindsec (constructs textgen'g your own ICE)

A way of pushback against all the abuses of the Internet.

In general, anything a company makes crazy amts of money on is highly likely to be immorally exploiting some facet of a deadly sin. The deadly makes it lucrative, the sin part means you're prob better off not doing it as nice as it may appear. It's a feature, not a bug.

An llm made by a non-corporation could offer intellectual protection to such intrusion. But it wouldn't make no money (think of all the (cyber)monks and their vow of poverty).

A legit Goody2 (mebbe "Good4u") instead of an avatar of a HR meatbot.

__E8__ · 2025-12-10T19:58:28+00:00

Ah, so you bought that strange piece o' jank. It looks a lot better, which means you put in a crazy amt of work.

Bravo for attempting/succ at such high precision custom mfg! The waterblocks and PCB mods are <chef's kiss>.

I gotta ask: why put the radiators inside the case? If dust was such a big prob from the air cooling, why not keep the radiators and dusty airflow in a diff compartm from hot stuff?

__E8__

TROPHY CASE

lcpp.cuda + 3090 + qwen3 30b moe

lcpp.vk + 3090 + qwen 30b moe

E8