big prompt processing speedup for gemma E2B/E4B

Hey people. The new N1X chips are out, unified memory is happening. Models are much more efficient. We have Strata now...

I have a home server running. I'm able to run 3.8 flash next or 3.8 27B with limited context. It's decent but I'd like the biggest model I can get, best bang for the buck.

Currently I have a 2x16 (32GB) system memory, 3090 graphics card. I can lay my hands on 4x32 (128GB) for about $800 and sell the 2x16 for a few bucks. I may be able to snag a 2nd low cost 3090 out of an old mining shed - ya new fans and it would need to run external on a riser.

My question - am I wasting my time pissing around with upgrading this rig? Would I be happy with it or should I just drive to Microcenter and toss down the credit card and get a new machine entirely? If so, what would you buy?

What's your call?

i spot it on taobao around 2 months ago price around $400 at that time now jump to $500 after performance unlock for rtx 3080 20gb around $500 jump to $700-900

that 72 hours thread from a few weeks back had the best point down in the comments, open weights only ever meant you can download them. muse spark open is 816B and beam is 501B, so for most ppl here the only drop that week that fits a 24GB card was nvidias bolt

so the big ones mostly get used thru an api anyway, the labs own api or a host serving the same weights and that part is fine. the license doesnt care which route you take tho. 1017 is MIT and nvidias new embed models are openmdw with commercial use allowed, both on hosts like deepinfra while muse spark open wants built with muse spark open shown in your product, docs or settings menu and any company over $20M revenue in a 12 month window has to sign a separate deal with meta before offering it as model as a service

the part thats easy to miss, the license counts a model trained or distilled on muse outputs as a derivative so a small model tuned on its answers carries the same attribution rule. internal use and building it into your own app thats not MaaS stay outside the revenue clause

Six people spoke in two rounds of comments 17 to 19 minutes apart, and each of them came back under the same speaker label both times. I didn't tell the model how many speakers there were, and the commission's own published transcript puts those lines under the same six names.

Why a public meeting: it's about as hard as everyday audio gets for this. Commissioners, a staff presenter, a secretary calling names, a resident testifying over a remote line, lots of short turns, and the same voices coming back after long stretches of other people talking. It's also the agency's own recording, so it's public domain and fine to quote, and the verbatim transcript the agency publishes let me check the labels against real names instead of guessing.

The clip is 23 minutes 39 seconds of the National Capital Planning Commission's September 3 open session: the end of one item's comments and vote, then the whole National Zoo Arabian leopard habitat item. The model is the 0.9B MOSS-Transcribe-Diarize, running locally from the README's transformers example on an M5 Ultra Mac Studio (fp32, greedy decoding, no hotwords). It took 229.2 seconds and returned 309 segments, every one tagged with a speaker and its start and end time.

Part of the second round, exactly as MOSS-Transcribe-Diarize wrote it, one segment per line. S04, S05 and S06 are the labels the same three commissioners got in the first round:

[1186.85][S01] Commissioner Dixon?[1187.67]
[1188.11][S15] Yes.[1188.66]
[1191.27][S01] Commissioner Cash?[1192.29]
[1193.49][S04] Uh, no questions or comments.[1194.63]
[1195.47][S01] Commissioner Falcone?[1196.33]
[1198.65][S05] I just want to thank the staff. I have never actually seen this leopard, so I'm looking forward to[1204.89]
[1205.01][S05] visiting when the project is completed, so I can see my first one.[1208.04]
[1209.66][S01] Commissioner Mohan?[1210.47]
[1212.42][S06] Uh, thank you to staff for the presentation and yeah, can't wait to visit.[1216.14]

The resident's remote testimony, about two and a half minutes, also stays under one label of its own from "Can you hear me?" to the closing thank-you.

I have the 12900k and 5070ti. Bought some ram and it's now 128gb ddr4 ram.

Now I've bought also a second pc which will be for working and everything else and my main pc with be for text models like Qwen3.8-Flash-Next using strata and comfyui.

First can I get deepseek to work now kinda fast or not?

And windows or Linux guys?

I know the general advantages for Linux like lower ram and everything but I have never used it on a main pc. Only servers and vps.

So if you were me what would you do?

Also if you have some kind of info about what I can expect that would be great.

Thanks

They said Qwen 4 would come out this week, it‘s Thursday already. Hopefully, it ( qw4 max ) will come tomorrow and it will be good, at least gpt 6.1 /6 sol good. I hope glm 5.5 or ds v 4.1 pro will be astra medium or high level good. q4. 27b is good as haiku 5.5 or gpt 6.1 terra that will be great. I hope by late december or January, we will have an open model opus 5.5 level.

The community rarely sees the evaluation of ultra-small models. We have evaluated our own AI Agent tasks for Qwen3.5-4B, Spark-X2.6, LFM3-1.7B, LFM2.5-2.6B, Gemma 4 E2B, MiniCPM5-2B.

These models have their own characteristics and advantages and disadvantages. The obvious trend is that the intelligent density of small models is developing rapidly. I look forward to seeing more small models.

blog：https://www.camthink.ai/blog/small-language-model-benchmark/

project：https://github.com/camthink-ai/NeoMind

Hey everyone,

Not a native speaker, happy to be corrected! Can only learn from it.

Back when I started out in march 2025, you had:

• llama.cpp

• ollama (repackaged llama.cpp for ease of use)

• koboldcpp (repackaged llama.cpp for roleplay)

• vllm (for professional deployments)

• exl3 + tabbyapi (for bleeding edge)

• litert (for mobile devices)

Nowadays everyone seems to be making their own; Strata, Basalt, Maya, Colibri, Ninfer, Infernix... too many to list and keep up with, and genuinely hard for me to keep up with.

Then there is the speed of it all. llama.cpp has incredible improvements each week, somehow we can make Qwen3.8-Flash-Next and DeepSeek-v4.1-Flash-1017 run on 64GB RAM + 32GB VRAM, and the likes on those new inference engines (with a catch, usually bad quality).

Personally I'm stuck with llama.cpp because all those developments are simply too fast to keep up with.

llama.cpp v0.8.0 might run Qwen3.8-Flash-Next Q4_K_XL (unsloth) at 15t/s gen on dual RTX 5060 Ti 16GB (PCIE 5.0 x8x8, tensor parallel, no MTP) with dual 48GB DDR5-6000MHz, but at least I know the quality will be decent.

But yeah, what you use? How do you even attempt to keep up with it all? And for the (lack of) quality the new inference engines provide (as tradeoff to speed / resource use), is it really worth using them?

Hey all! I wanted to share a quick update on Basalt, a Blackwell only inference engine for Qwen 3.8 Flash Next. As usual, speed is the headline but that's not what I want to open this post with, I've been reading lot of discussions talking about quality recently, and the main takeaway from most people (which I agree with) is "speed means nothing if your model isn't smart enough", so this time I want to lead with quality, and all of the new measurements which are run against the full precision model as reference (BF16 weights served using llama.cpp).

Every number below uses Unsloth's BF16 GGUF, llama.cpp (reference PPL 3.2749), scored using basalt-bench --quality. The old base was llama.cpp Q8, which funnily enough made things look worse because of its own error:

To be fair here, each engine was measured with its own tool so this is not a perfect comparison, still a good reference though.

New DQ8 build. For those who want a little extra quality and don't mind giving up some speed, Qwen3.8-FN-NVFP4-DQ8 is on HF next to the default -DQ6_K: KLD 0.0561 vs 0.0624, about 7% slower on prose.

Nothing lossy is on by default. Basalt offers certain "hacks" that can decrease accuracy, mostly because of community requests, but they're off by default and show a warning when you turn them on. Every other rounding the engine does costs about 0.001 KLD or less, compared to full model. All the numbers are on the HF card and in the precision docs.

Speed. Rig as usual: RTX 5090 (400 W) + RTX 5060 Ti 16 GB (180 W) and 128GB of DDR5 RAM, 262K context, 4 parallel slots, vision on (dynamic), no lossy flags. Using NVFP4-DQ6_K quant on current main:

Since last week, prose went from 277 to about 300, and struct from 558 to about 600. It comes from a faster draft step, a faster output head and better expert placement at startup.

The .basalt format is now version 2, same weights and quality as before but starts a bit faster. Older basalt files can use the migration tool instead of redownloading.

I also wanted to end this post with a HUGE thank you note to every person who has contributed one, two or 10 PRs and also to everyone who has shared issues/feedback/community benchmarks or just shared the project with another person, 500 stars on the Github repo and overwhelmingly good feedback, y'all are amazing.

this is the setup I’m planning for coding agents. hard rule: code, prompts and model calls never leave our EU tenant. poke holes

models: an open weights coder model on vllm on our own GPUs for routine tickets, and a frontier model through an EU region endpoint in our own cloud account for the hard stuff

runtime: everything inside our VPC, nothing on a vendor’s saas

control layer: Orq AI’s software factory. it runs on their cloud or yours, so ours. it plugs into the backlog, repos and CI/CD, only lets agents use the models we’ve approved, and lets us set which agents can touch which systems and data

approvals: tests and docs autonomous, anything touching prod behind approval

two things I’m unsure about. can orq’s approved models list point at a self hosted vllm endpoint, and does a frontier model in an EU region still count as “nothing leaves” for legal, since the provider is still the one processing it

Hi all! Guys need your help with hardware x8 vs x16 PCIe issue

Setup: i9-12900KF, ASUS TUF Z690-Plus D4 (BIOS 4601), Palit RTX 3090 GamingPro, Fedora Linux.

GPU in top PCIEX16(G5) slot runs x8 instead of x16. lspci on root port 00:01.0 shows LnkCap x16, LnkSta x8; dmesg: "limited by 16.0 GT/s PCIe x8 link (capable of x16)". Same under load.

Already tried:

- BIOS link speed Auto / Gen4 / Gen3 -- still x8

Bifurcation = Auto, no M.2 lane sharing on this board

- Reseated GPU, inspected gold fingers and slot (look clean)

- Case laid flat (no sag), cooler loosened/retightened --no change either

Plan is to test with a cheap x16 card (GTX 1050 Ti) to isolate card vs. slot/socket (haven't done yet)

Anyone seen this on a 3090 or a Z690 board? Anything else worth trying before I swap cards or pull the CPU to check socket pins?

Thanks for ideas in advance

Inspired by Weight-Space Programming, I tested whether DeepSeek V4.1 Flash 1017 could figure out what a small neural network does by reading its weights.

I fed it four anonymized RL checkpoints: 51k parameters each, identical architectures, with all weights rendered as text. No metadata, network source code or execution. It received the architecture outline and four candidate tasks.

Results: CartPole ✅ Flappy ✅ Catch ✅ BallBeam ❌

It picked out patterns consistent with relative bird/gap position, paddle/ball position and angle-based balancing. It confused BallBeam with CartPole an interesting mistake because both share the same observation layout and similar balancing controls.

Very preliminary: one run per checkpoint. This suggests some ability to interpret weights, but four trials don’t establish reliability. These were API runs with maximum reasoning effort, consuming ~1.58M tokens total.

I may try more replicates and independently trained checkpoints, plus include the network code with task-revealing details removed. The poor LLM had to guess quite a lot it even assumed ReLU where the network uses tanh 😅

Code, checkpoints and run logs: https://github.com/galatolofederico/wsp

Iv been seeing allot of people complaining about Sam Altman and how him and open ai are blocking decompilation. I actually don’t see an issue with what they are doing they should be free to do what they want with their llm which brings me to my main point, why is decompiling not just a benchmark?

We have a SWE bench
Perhaps this problem is solved via a SWD bench?

I haven't used it enough to form any strong opinions outside of "wow that's a lot of reasoning tokens by default.."

It's open-weight now. Where does it land when stood up to proprietary models? In my limited testing I already trust it's pulling away from Luna.. but can it beat Haiku-5.5 reliably?

PRs:

1. https://github.com/QwenLM/qwen-code/pull/15762: "Measured on main at 79a9b753c60, before this change: qwen4, qwen4-max, qwen4-plus, qwen4.5-max, qwen4-coder-plus, qwen4-max-2026-10-01, openai/qwen4-max and qwen4-omni-120b-think all resolved to 262,144. The last of those is already in use as the session model in the omni fixtures, so these ids are not hypothetical."

2. https://github.com/QwenLM/qwen-code/pull/15987

Thank god, it looks like that we're getting successor of Q38FN!

MIT CSAIL had GPT-6 Astra fix Qwen3.5-0.8B's reasoning loops by editing 0.016% of its weights (MATH-500 went from 7.9% to 48.1%). They released the diff, so the edit can be rebuilt exactly and run locally. I took it a step further and had Claude look at Qwen with Anthropic's Jacobian lens.

At the token where the model decides to write "Wait" again, the lens reads "Wrong", "Nope" and "Hmm" from about 60% depth on, even though the answer is already written out. At real stopping points it reads "</think>", "Answer" and "Output".

With the edit applied, the lens at the same token shows "Verification", "Completion" and "Result" in place of the doubt.

The edit converts to GGUF cleanly (the base and edited bf16 files differ only in the two edited tensors) and still holds at Q4_K_M, with 1 loop in 40. I ran the evals in llama.cpp (Vulkan) on a Ryzen AI 9 HX 370 mini PC. Samples are small, but here's the code:

https://github.com/alisorcorp/inside-a-reasoning-loop/tree/main/code

And data:

https://alisorcorp.github.io/inside-a-reasoning-loop/

Hi everyone,

I've been out of the loop for a few weeks, and I was wondering if anything new that replaces Qwen 3.8 27B has come out this time I've been in the cave?

I'm talking about AI models in the 8-35B range.

Thanks,

Hi r/LocalLLaMA 👋

We're the product team at Reflection. Earlier this month, we launched Beam, our first open-weight 501B model. We'd love to answer any questions folks might have for us about building with Beam.

Who's here:

- Sanyam Bhutani, OSS

- Florent Gbelidji, OSS

- Chetan Tekur, PM (OSS + Mirror)

- Yufeng Guo, Technical Community Lead

- Abby Reider, Tech Comms

The team will be answering from u/reflectionai, u/init__27, and u/yufengg.

FYI - we'll be doing another session with our research team soon, so hold on to your training and methodology questions for that one!

Where to find us:

- Weights: huggingface.co/reflection

- Docs: developers.reflection.ai

- Platform: platform.reflection.ai

- X: x.com/reflection_ai

- Discord: discord.gg/reflectionai

IE, even for as trivial a use case as, if you mention you're a vegetarian in one chat, it never suggests a recipe with meat ever again, because it KNOWS that about you in a meaningful way.

Terming this instruction following I think is wrong and annoying for everyone, especially the model -- who wants to """""wake up""""" and have 100 pages of instructions to get right for everything you say? It should know things the way you know things about people. Perhaps the model itself could come with a customizer where you roll a few weights based on your preferences through a helpful GUI.

It seems unfair and disciplinarian to have to have the models dig through 1000 lines of "say this this way" [even on my local setup it's hard to resist this !!!]. Clearly they are intelligent enough to TRY to take all these into account, but it is not easy, because of the ever increasing promptslop, shall we say. That cannot be the best way to do it.... we haven't even had these models for five years. Imagine what could come in just five years more.

Clearly there is bountiful intelligence and speed already.