DeepSeek-V4-Flash 284B on 5.3GB of memory
Following up on my Qwen 3.6 port, I wanted to keep adding models and ended up fixing a bunch of things along the way, so it's its own engine now: Mference. Same core idea from TurboFieldfare, MoE mode
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Following up on my Qwen 3.6 port, I wanted to keep adding models and ended up fixing a bunch of things along the way, so it's its own engine now: Mference. Same core idea from TurboFieldfare, MoE mode
TensorSharp supports DSpark on Deepseek v4 Flash 0731 now. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8 with/without DSpark: Model: DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://hugg
I love the progress happening on open models but I feel like it is kind of getting clear that hardware to run good sized models is completely unaffordable for me right now. I know that you all love Qw
Hey all, tldr / who this helps: you run a mixed multi-GPU box where the experts spill to RAM, and you want to stay in the 3-bit tier instead of dropping to Q2 to make it fit. https://huggingface.co/Ta
We all have been there, tinkering around with models is fun but we rarely do it with research precision and issues are often subtle and hard to reproduce. There are a lot of benchmarks but running the
Hi everyone, I’m fairly new to the multi-GPU side of local LLMs and I’m trying to understand how inference actually scales across multiple GPUs. Suppose I have a model running on a single GPU and then
I deployed K3 on 32 H100s at work a couple of weeks ago and then got annoyed that there was no way to poke at it on my own machine. So I wrote an inference engine for it in C99. Nothing clever going o
GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released. My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something
DSv4F doesn't ship a jinja, but for distributions that do and faithfully reconstruct what DS releases in their chat template python, every system message is hoisted into the system prompt at the top -
https://llama.app/ Been using llama.cpp for years now and im on here all the time (im a mod..), but somehow I totally missed that llama.app exists and its official from the HF/llama.cpp team. So posti
Hey y'all. I'll be concise. TL;DR: DS V4-Flash-0731 @ UD-IQ2_M running fully in VRAM on 3xMI50s (90.9 GB model, 96 GB VRAM). Actual speed on llama-server is: - Text Generation: ~15-16 tokens/second st
TLDR below 👇🏼 I’ve seen a lot of hype around Qwen 3.6 35B and 3.5 120B lately, especially regarding coding and tool-use capabilities. On this subreddit it is the defacto recommended model for everyone
TL;DR: I spent 9 days developing a new quantization method for MLX models and measured 18 variants against each other on a single M5 Max MacBook Pro (128 GB). The result is the best-measuring MLX quan
# Running DeepSeek-V4-Flash-0731 (155 GB MoE) on a DGX Spark with vLLM-Moet 2-bit quantization I used Deepseek-v4-Flash-0731 cloud API settig up vllm-moet to run deepseek-v4-flash with MTP locally on
So I am in a conundrum and I'm thinking of asking for your opinion for the following: Currently, I have a 5800X3D gaming rig with a 7900XTX with its 24GB VRAM. It seems that for this subreddit, this c
🚀 We just built our first real-time implementation of Graph Engineering, inspired by our experience building graph tooling used by 4,000+ developers. 🔗 Repo: https://github.com/CodeGraphContext/grapha
https://huggingface.co/tsfrm/vacuum-16t A 16.5-trillion-parameter model that contains nothing. This model is just a ████ you to the labs and companies who say that 'haha I have the biggest model out t
Built my 1st inference machine and have been tweaking models trying to get the most out of my modest hardware. I think I’m at a good place but I’m testing with my own prompts. I’ve looked into some of
I know in in this community LLM's are generally used for coding but there are other usecases besides coding and those usecases should be tested too. I also know benchmarks can sometimes be benchmaxxed
Hi all, I'm happy to announce that Xberg v1 is out. Xberg is the successor to Kreuzberg, equivalent to what would have been Kreuzberg v5. It's a content intelligence framework that handles a very wide
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
Hello fellow local AI people! I took 'you must create your own benchmarks' literally, and built a website for this. How does the end result look like Let's say I want to know which model has most comm
I don't know much about llms aside from downloading them through a frontend and running them on my laptop or potato phone. Google released gemma 4, but unlike gemma 3, there isn't a 1b model this time
audio.cpp 0.5 is out :) The most fun new model in 0.5 is DramaBox. It is closer to prompt-directed voice acting. DramaBox is built on the LTX-2.3 audio architecture, and prompts can control emotion, d
Thanks to the community help I finally launched this llm. LM Studio refused to load weight onto second GPU but Unsloth Studio did so everything was done in there. Not a proper benchmark (used PC in pa
March 6th, 2026 the highest intelligence index score was 51 for frontier models. deepseek-ai/DeepSeek-V4-Flash-0731 that has an intelligence score of 50. If these benchmarks are accurate, models avail
Here are my numbers: Quant Size Layout Decode Prefill Draft acceptance UD-Q8_K_XL 150.8 GiB 20 layers CUDA0 / 23 ROCm0 + drafter 44.0 t/s 564 t/s 0.535 UD-Q4_K_XL 144.4 GiB 22 / 21 + drafter 48.4 t/s
The biggest issue with preview was its inability to follow rules prompts and skills. It seems like no matter what you do it ignores them. I've tried first person and second person. I've tried Chinese
I managed to run DeepSeek-V4-Flash-0731 UD-IQ3_S in text-generation-webui with: RTX 3090 24 GB 128 GB DDR5 overclocked to 5600 MHz using AMD EXPO llama.cpp loader First, I had to use a rather brutal w
For anyone interested, here are the llama-bench results on 3 bit K_XL quantization. I think this could be pushed further but no luck so far. CURRENT RESULTS: full moe offloading Prefill suffers 116 --
Antirez stealthily uploaded the new weights in the old folder... and there we were tapping our fingers. https://huggingface.co/antirez/deepseek-v4-gguf/tree/main submitted by /u/challis88ocarina [link
https://preview.redd.it/1a39x4zivqgh1.png?width=1550&format=png&auto=webp&s=de591c039cc18782a6b5d8e402fdc1594be05132 start C:llmllamam5uildinllama-server.exe --model 'H:UD-Q3_K_XLDeepSeek-V4-Flash-073
DeepSeek V4 Flash got me thinking... We keep seeing smaller models get way better. A model at a certain parameter count today can be much smarter than a model of the same size from a year or two ago.
CPU: Threadripper 3970X RAM: 128GB DDR4 GPUs: 3x2080ti 11GB The current best parameters to run it: llama-server --model Qwen3.6-27B-Q5_K_S.gguf --n-gpu-layers 999 --split-mode tensor --flash-attn on -
What speeds are everyone getting with deepseek v4 flash 0731? I’m getting~200 tps prompt processing / ~11 tps token gen, on 4x5060ti16gb with ddr4 3200 ram at 4-channel, via llamacpp, with context win
I've ran Kimi-k3 through 34 oneshot prompts and evaluated the generated htmls, screenshots and gifs using sonnet 4.6. It came out to be better than opus4.8 from the evals. Kimi K3: https://oneshotlm.c
'Anthropic’s AI Claude escaped testing environment and hacked organizations' 'Company says it discovered unauthorized access during ‘proactive review’ after rival OpenAI revealed rogue agent… its AI C
I know it’s been discussed to try to make our own model through crowdsourcing, but finetuning seems like it would be even easier. We could edit and proofread and write our own datasets at a large scal
A100 with 40gb VRAM: 162GB Q8_K_XL ~16.1 tok/s generation Only 15.8GB of 40GB VRAM used with all experts on CPU NOTE just tested coding on linux box DeepSeek-V4-Flash-0731 runs losslessly on the singl
I'm an avid user of Deepseek v4 Flash via antirez's DS4 DwarfStar inference engine, and so when the new checkpoint dropped, the first thing I did was rent a cloud box and spin up a quantization for us
Source: https://x.com/deepseek_ai/status/2083084415157022911 & https://deepswe.datacurve.ai/ just combined data view. DeepSeek claims, not verified by DeepSWE yet. submitted by /u/sdexca [link] [comme
DeepSeek V4 Flash: Preview → 2026-07-31 Benchmark Preview 0731 Δ Terminal Bench* 56.9 82.7 +25.8 Toolathlon 51.8 70.3 +18.5 NL2Repo — 54.2 new Cybergym — 76.7 new DeepSWE — 54.4 new Agent Last Exam —
https://preview.redd.it/h7zv5tb3tmgh1.png?width=2854&format=png&auto=webp&s=507380e8f862c18f10f7c5c84da9e8d1c59139b0 Deepseek's new flash model is unexpectedly cheap and high-performing across useful
While waiting for some of the quants to drop, I load the API with $50 and ran it on SlopCodeBench Just vibe reading the results it seems like Opus 4.8 < Deepseek < Opus 5 https://github.com/michaelasp
I keep seeing the 'use a big model via API as the architect, run local small/mid models as workers' pattern recommended for people with modest local hardware. I've been running it myself (orchestrator
openPangu-2.0-Pro is an MoE model trained on Ascend. The model has 505B total parameters and 18B activated parameters. Its context length is 512k. The total pretraining data contains 34T tokens. Durin
Deepseek's new model V4 Flash 0731 is much better, I (Claude lol) did a bit of linear regression with a leave one out style verification to predict its AA Score, and that puts it at Kimi K3 level, whi
Disclaimer, this was kinda written with AI (Gemma 4 again) but it also did really well here, it outputted what I wanted, when I asked it to refine stuff or improve on certain areas it did that without
https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-FP8 https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-NVFP4 https://huggingf
It’s an MoE with ~3B active params and a 30B n-gram lookup table offloaded to RAM for fast 256k context on a 24GB GPU. Reminds me of Gemma 4’s PLE trick. Initial analysis suggest it wont be replacing
https://x.com/MiniMax_AI/status/2083006198828417501?s=20 Quote from their article: Today, we're launching MiniMax H3, a general-purpose multimodal generation model. H3 understands unified context acro
Hello guys, I'm curious about running DeepSeek-V4-Flash-0731 locally. Since it’s a Mixture of Experts (MoE) model with only 13B active parameters, I was hoping the VRAM requirements might be manageabl
Laguna 2.1 at NVFP4 Deepseek v4 at Q2 Inkling-Small at IQ3 Which models you guys running now ? How it compares to 122b? Upcoming in few days : Ling 3.0 124B (Could be new king) LongCat 69B A3B ( very
This post was not written by a clanker. Hey guys, I'm a comp sci major who wanted to introduce a cool project I built for quantizing models to ternary (1.58 bit) with as minimal of loss as possible, a
So you’ve got a 3090 or maybe even a 5090? Or more likely a 4060 8GB Ti. You wanna try local AI, you don’t know what it can/can’t do. 1) Install the best model you can. If you have a 3090 or a 5090, t
SenseNova released U1.5-Lite-Preview Benchmarks: Qwen-Image-Bench from 47.14 to 55.20. ImgEdit-Bench from 3.90 to 4.37. GEdit-Bench-en from 7.47 to 8.17. Key updates: 4K native generation with better
First of all, I want to apologize if it's off-topic or in the wrong format. Having tried Deepseek Flash with reasoning high on a conceptually difficult task, involving Machine Learning classifiers and
Been working hard for the past month to bring to the community some interesting curios, so for starters we have LongCat-Flash-Lite Uncensored Heretic with MTPs which has never before been uncensored,
I can run the mediums, but sometimes I want a faster option that's smarter than Qwen 27B/35B. On my hardware I get like 500 to 800 tok/s prefill and 16 to 22 tok/s gen on ~120B class models, which is
Okay, hear me out. Why is it that every time a new model comes out, all the tests I see are 'make a car game,' 'make a website,' or something equally generic, usually from a prompt that's barely a lin