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Released today, with emphasis on agentic capabilities. I really like their models for simple, high volume tasks ("summarize these gazillion documents") and their 8b-a1b was my go-to for certain tasks so I'm excited to see how this one performs. There's not enough love for tiny models on this sub. h
Weights went up today so this is downloadable now, MIT, ~128GB for the official FP8. I ran these on the API before that landed, so treat it as a preview of what you'd be pulling rather than a local benchmark submitted by /u/AcanthisittaOk1699 [link] [comments]
I run my inference machine in the living room, so noise and heat output are a significant concern. Ran a quick test using my daily driver model (Qwen 3.6-27b) and at 480W, the card outputs only 2.1% less t/s in decode and 8.8% in prefill (which is already very fast). Well worth the massive noise re
I've been working on speeding up DeepSeek-V4-Flash-0731 in Krasis and have now got the long-prompt prefill quite a bit faster on a single RTX PRO 6000 96GB. These are timing-disabled internal Krasis results using INT4 experts. They aren't HTTP round-trip speeds: Prompt size Prompt Processing ab
The Ling-3.0-flash MoE is now open-weighted at 124B A5B params. I know the original announcements were before the Kimi K3, DeepSeek-V4-Flash and Qwen3.8 hype, but this model might still have a good niche for itself due to its sizing. Discussion on the benchmarks are here: https://www.reddit.com/r/L
I really like to use this one SQL benchmark when testing new models. I had another post some time ago with my benchmarks, but I decided to post a new one because of how well Deepseek did. I like the benchmark because it's quick to run, is pretty "real-world" and requires good reasoning to build the
First of all, obviously I took some help from AI to type this post and this is the topic that enabled me to accomplish all that: https://old.reddit.com/r/LocalLLaMA/comments/1veow4b/deepseek_v4flash_284b_moe_at_33_toks_single_68/ This post of mine is based on the link above. My Hardware: RTX 5090 3
submitted by /u/tarruda [link] [comments]
Some of you may be aware that a few weeks ago, LM Studio announced a new agent, Bionic. This is pretty much an agentic harness for both local models and paid cloud models. But most aren't aware that LM Studio replaced almost every link to the original app that built their brand and reputation with t
submitted by /u/appakaradi [link] [comments]
Has anyone followed nousresearch work on Hermes? I mean we are Q3 2026. We have some crazy models trickling down from HGX territory to multi gpu workstation. And we have nousresearch deploying the 0.20 of its hermes agent while starting releasing the project with a 0.2 mid march! Crazy times t
Hugging Face Artificial Analysis Should be a sweet spot for general work. Seems like coding is the only part that is inferior to Qwen. submitted by /u/axseem [link] [comments]
I tested Ling-3.0-flash with hard bugs and it fixed bugs that qwen3.6-27b could not. This models speed faster than deepseek v4 flash but almost the same level as (old) deepseek v4 flash. Note: hard bugs mean they don't have "error messages" but they are unexpected behaviors of a software. Most bugs
Ran DeepSeek V4-Flash-0731 — the full official checkpoint, not a re-quant — on commodity used hardware. Sharing because I couldn't find anyone else publishing Ampere results for this engine. Edit / update: a commenter called out that hybrid CPU-GPU posts always publish decode and never prefill
Qwen3.8-Max (2.4T) is another massive contribution to the open weight community. On benchmarks, it performs closely to Kimi K3 and DeepSeek V4 flash across all categories and is better at coding and software tasks. Qwen3.8-27B will also be open weight soon too. Weights are being released next
Every time a model drops from a Chinese lab the thread fills with people who already know who made it, and the guess is usually Alibaba. There was a thread here recently asking what separates the open source labs from the frontier labs. It ran to nearly sixty comments and hardly anyone in it s
So this is the stuff of absolute insanity. In less than 20 months we've gone from super expensive cloud models only, to being able to run a Q3 quant of DeepSeek on an Intel Windows PC with a very average 24GB of VRAM. No wonder the big boys are panicking (and yes it's slow as porridge). https://ibb.
Continuing my weekend of oneshotting the cheap OpenRouter models, here are all 10 DeepSeek models across the same 35 prompts. DeepSeek had a rougher time (more provider errors / empty completions), so only 242 made it out of the 10*35 matrix. Here they are https://oneshotlm.com/model/?q=deepse
Originally, I was only getting around 140pp/s and about 21tg/s, but the config with -b 8192 -ub 8192 --cpu-moe is vastly superior, let's say 700pp/s and 18tg/s in the most relevant range. Test System: CPU: Threadripper 5965WX (24c/48t) RAM: 512GB (8x64 DDR4 ECC REG 2400) GPU: RTX 5090 (PCIe 4 x16,
Spent way too much time with V4-Flash-0731 this weekend and wanted to share my vibes as briefly as possible. I sent it through a bit of real-work and some of my personal benchmarks. My quick thoughts are: Quantization hits this thing like a truck - I've tried a bunch of the Q2 and Q3 weights and it
AI9Stars has released G9v3-39A5B an open weights language model designed to deliver even stronger reasoning capabilities than ai9stars/G9v3-3B with its 39B and 5 active experts. It is released under the Apache 2.0 license making it fully open for personal and commercial use It targets everyday assis
https://preview.redd.it/3zcvpbds14hh1.png?width=1911&format=png&auto=webp&s=a79aafb71eeca97638da93d2591902631e897fd5 I tried a test similar to the recent model-quant comparisons, but this time I focused only on: DeepSeek-V4-Flash-0731-IQ2_XS-Experts-Q8_0 model link bullerwins/DeepSeek-V4-Flash
Qwen announced Qwen3.8 a few hours ago, and it looks like we’re getting a new 27B model! Really excited to try this one locally. submitted by /u/FormOne2615 [link] [comments]
MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task-gene
WASTE is an embeddable inference engine written in C, with no third-party runtime dependencies. It keeps the model trunk in memory, streams selected experts directly from disk, and uses the remaining RAM as a bounded expert cache. submitted by /u/ab2377 [link] [comments]
I don't think anyone should quantize the KV with DS4F. I checked the the quality impact (PPL, KLD, Same TopP) for swhitching from BF16 KV to Q8 KV, and it appears significant. Very much in contrast to Qwen 397B. Here are the results for DS4F: ====== Perplexity statistics ====== Mean PPL(Q) : 5.87707
M1 Ultra 128GB, Unsloth UD-IQ3_XXS, wired limit at 120GB. I was at 5-6 tok/s before the patch. Getting 15-16 tok/s now with the patched engine, and the output seems to have improved. Big thanks to this guy. submitted by /u/mil_phickelson [link] [comments]
I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's official API to confirm that I didn't do anything wrong. For anyone using OpenRouter, be aware that there
submitted by /u/mrwang89 [link] [comments]
I've been busy this weekend generating oneshots for all the cheapest models on the openrouter and ended up going through all 33 qwen models across 35 prompts (there were some failures and only 1109 made out of 33*35 matrix). Here they are https://oneshotlm.com/model/?q=qwen Qwen 3.7: qwen3.7-