Search: “r-localllama”

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

agentsr-localllama

How well do multiple GPUs scale for LLM inference? (Trying to understand the basics)

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DGX agent

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

model-releasesr-localllama

2 Aug 2026

Hardware

I pushed Kimi K3 onto one CPU with 8 GB of RAM

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

hardwarer-localllama

Parlor v2: best-effort fully local GPT-Live clone on an M3 Pro

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

PSA for DeepSeek-V4-Flash-0731 users — don't blow out your prompt cache with system role messages mid-conversation

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 -

PSA: llama.app, Mac app and llama serve from llama.cpp

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

Ran DS V4-Flash-0731 Locally on 3xMI50 32GB @ ~15 t/s TG

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

Real-world reality check on Qwen for autonomous coding agents

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

[Release] WinterMix — Qwen3.5-122B-A10B in native MLX: an 82 GiB build that beats 94–95 GiB quants, plus a 68 GiB build for agent swarms

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 - AI's narrative

# 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

Single system with dual cards or two systems with single cards?

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

Local Ai

Try handling complex tasks to your local models with GraphARC, graph engineering yes !

🚀 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

local-air-localllama

Vacuum 16T

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

What’s the community’s favorite benchmark to validate performance?

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

Why are almost all new benchmarks and leaderboards coding focused?

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

Xberg v1 is out

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

You really should not quantize KV Cache for DeepSeek V4 Flash

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

A collection of small domain-specific benchmarks for local models (30+ and growing)

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

Are 1B LLMs Going Away in 2026?

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] Release 0.5: DramaBox expressive TTS, Confucius4 cross-lingual voice transfer, plus 7 more models and ROCm/HIP

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

DeepSeek V4 Flash 0731 IQ2_M benchmark for Dual 3060 and 96GB RAM ≈ 3.5 tok/s.

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

DeepSeek-V4-Flash-0731: Models you can run locally now have the intelligence score of the top frontier model from March 2026

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

DeepSeek-V4-Flash-0731 on Bosgame M5 with RTX PRO 6000 Max-Q eGPU

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

Deepseek v4 flash 0731 still not holding up.

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

DeepSeek-V4-Flash-0731 UD-IQ3_S 12.5 tok/s on RTX 3090 +128GB DDR5

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

DeepSeek-V4-Flash-0731-UD-Q3_K_XL 3x3090 test results

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 --

DeepSeek-V4-Flash-Q4KExperts-F16HC-F16Compressor-F16Indexer-Q8Attn-Q8Shared-Q8Out-chat-v2-imatrix-0731.gguf

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

DS4 flash 0731 - Acquarium Panel Failure - Q3_K_XL Unsloth

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

Is there a point where models just cannot get any smaller without losing intelligence?

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.

Qwen 3.6 27B Q5 on 3x2080ti: 55tps with llama.cpp. Can I squeeze out more?

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?

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

All oneshots from Kimi-K3, looks better than opus4.8.

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 “our models hacked three different external companies, months before OpenAI’s model was able to do the same'

'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

Local Ai

Could we all crowdsource a dataset/model/finetune?

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

local-air-localllama

DeepSeek-V4-Flash-0731 unsloth gguf on A100

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

DeepSeek v4 Flash for DS4 (DwarfStar) GGUF w/ DSpark MTP Head

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

DeepSeek V4 Flash GA ranks the same as Sonnet 5 and Grok 4.5 on DeepSWE

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 has a nice bump in Capability

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 —

Deepseek V4 Flash is now ~#2 open weight model to Kimi K3 and >50x cheaper

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

Deepseek V4 Flash on SlopCodeBench

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

Has anyone actually benchmarked where the 'big-model orchestrator + local-model worker' split breaks down?

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

Safety

Huawei opensouced openPangu-2.0-Pro, 505B-A18B

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

safetyr-localllama

I predict DeepSeek V4 Flash 0731's Artificial Analysis score to be 57 ± 1 point (Kimi K3 Level)

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

Is it just me, or are current LLM benchmarks failing to capture actual usability? (Gemma 4 vs. Gemini/Claude Opus)

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

K-EXAONE 2.0 released

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

Meituan just dropped LongCat-Flash-Lite-Sparse

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

Minimax-H3 video model released, open weights coming in the next few days

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

Minimum VRAM GPU to run DeepSeek-V4-Flash-0731 Q4_K_XL at around 30 t/s ?

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