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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

A few people asked for this after the bandwidth thread, so here it is on its own instead of buried in a comment. The dense rule was simple: every token reads every weight, so tokens/sec ≈ bandwidth ÷

I am trying to learn concepts like On Policy Distillation (OPD), On Policy Self Distillation (OPSD) and how do they compare to RL algorithms like GRPO. There are a lot of papers on this, but because o

Ciao a tutti ho appena comprato 4 Huawei duo 96 GB a poco più di 4mila euro qualcuno ha già utilizzato CANN consigli da darmi? E la prima volta che utilizzo questo framework e non so proprio da dove i

GitHub: https://github.com/Loann110/Vao2 Hello everyone, I’m currently developing Vao2, an open-source application that brings together news, YouTube channels, GitHub repositories and more (to be adde

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.

Hello all - i just got a new mac mini with 24GB of RAM and wanting to run local AI for Home Assistant and Hermes. I have been struggling to find a snapy model that will work with my machine. Currently

Wondering if it's possible to actually produce ai art works that are indistinguishable from authentic medieval woodcut illustrations like the one attached. All the AI attempts I've seen at re creating

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 -

TLDR: There is no one prompting trick that will result in Krea 2 Turbo giving you exactly the style you want and across the whole image. Instead, if you are trying to achieve styles without the use of

So upfront I'll admit that this is a ChatGPT summary of my chat with it about this idea i had, but this post wouldn't exist any other way, so... I’m trying to get a better understanding of how VRAM is

DeepSeek V4 Flash is now available on InferX, and it’s free to use. We’re continuing to add GPU capacity as demand grows. While we’re bringing additional capacity online, you may occasionally see high

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

Been trying to find something that actually handles my workload well instead of just being 'fine.' Started on Qwen 2.5 7B, moved to Qwen 3 8B, and right now I'm using Nemotron 3 Ultra (the big 550B on

I am a pro subscriber and have 0 usage, yet I cannot even chat with Ollama because of the message in the title that I keep receiving. This is ridiculous. Anyone else experience this issue and have a s

I swear some AI doomers have never actually used a local model. They watched one flashy keynote, one YouTube thumbnail with a guy making this face 😱, read three headlines, and suddenly civilization is

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 got a fever and cough and assumed that this is some viral infection that will just go away on its own. I wear a Fitbit watch which measures my heart rate. Just out of curiosity I posted my symptoms

Huggingface direct download: https://huggingface.co/estrogen/silveroxides-Chromea_LoRA Just got pinged on Lodestone's discord server and it is a much better alternative to Mystic and other uncensor lo

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

Here is my experience, which I would like to share with you and I also would like to hear your thoughts and valuable tips&tricks. Hardware: Mac Mini M4 (32GB Unified Memory) Model Server: Ollama Orche

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

I’m one of the builders of LOLM, a hybrid Transformer–SSM model and agent system from Qira. The model separates surface token processing from persistent latent-state tracking. An NFET controller can s

It's an encoder-only transformer that consumes past(blood glucose + carbs + insulin) and future(carbs + insulin) and predicts future blood glucose for the next 2 hours. Announced meals and boluses/bas

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

Can someone summarize the latest thing with Anthropic? https://www.bloomberg.com/news/articles/2026-07-30/anthropic-s-ai-models-hacked-three-organizations-during-tests How in the bloody hell are they

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

Hey folks, is anyone currently using Ornith 1.0 9B for serious or complex backend development? I’m working on three different applications: A multi-vendor e-commerce platform with a Node.js backend A

Getting tired to keep approving simple commands like 'grep', 'cat', 'find' and 'ls'. Even if you tell it to 'always allow these requests' it seems to keep asking even within the same session. submitte

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

Hi everyone, Can anyone suggest a learning path to fully understand the technical report for Kimi K3? My background: • I've taken a graduate-level deep learning course. • I understand the Transformer

Krea 2 has been my favorite base model for character work, but the moment you put two character LoRAs in the same generation they smear into one blended face. Attention bias, prompt engineering, and C

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