Search: “context”

New/Old benchmark that provides a lot of answers for local LLM. I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/

I recently saw someone complaining that opencode causes compactions way too early (with 30k tokens still left). I fixed this issue and I thought I'd share my config to see if it helps anyone. I know p

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. You can even run 405B Llama 3.1 on

Hello everyone, tried so hard to optimize my config and finally I simply get up to 70 t/s with q6 variant. And wanted to share with you guys so that other people with the same setup can enjoy. Please

I know the front page is completely dominated by the Qwen3.8 27B. I know it's a fantastic model and everyone is rightfully discussing it, but I wanted to shift gears and talk about another model I’ve

Two days ago I released a hyper-optimized Qwen3.8-27B inference engine for an RTX 3090 (82 tps single request, 672 peak) - yesterday's update took that to 99 tps single-user / ~1,000 tps at 64 concurr

I've been experimenting with Qwen3.8-27B using DeepSeek Harness. It's a monster at long-horizon tasks, and the results were pretty wild. DeepSeek Harness ran on my Windows PC and connected over LAN to

Title pretty much says it all. I’ve deployed Qwen 3.8 27B using vLLM on an RTX 6000 Pro (tried multiple vLLM releases and launch recipes), but I can't get it into a usable state because of crazy long

Hey! Like a lot of people here with consumer GPUs (RTX 4060 8GB in my case), I wanted to see if I could use local models for daily coding tasks instead of burning cloud credits on simple boilerplate.

Hi, After a long night of optimizations, I believe I have made the fastest inference engine for Qwen3.6-28B on a 3090. Quick metrics: - 250w power capped - Up to 195k context (ships with 150k for safe

The uncensored FP8 build that showed up on HuggingFace this week is about 31GB in block-FP8, roughly half the BF16 weights. People keep reading '27B' and assuming it drops onto a 3060 or any single 24

Good day to all. I'm fairly new running local models and would like to pick your brains on an issue I'm running into. I'll first tell you the issue, then give you my set up. I run into issues where th

I'm currently testing out Qwen3.8-27B using Unsloth's UD-Q4_K_XL running a freshly rebuilt llama.cpp. I have a 22GB RTX 2080TI on which I'm able to fit 100k context with q8_0 quantization, and using M

I’m genuinely pretty disappointed with how my Ollama Pro annual subscription turned out. For context, I’m a technology editor and developer, and I had actually used Ollama Pro before. My previous expe

https://preview.redd.it/nqr9nj5028jh1.png?width=1516&format=png&auto=webp&s=47af40a0737429dc65f2e455b87b2db81725cb14 So I wanted to see... is it possible/viable to run this perhaps once in a while som

I've been building an open-source tool for diagnosing world models, the kind that predict future frames from a starting context and a sequence of actions. It compares a rollout against ground truth an

Hey guys, Just finished benchmarking DeepSeek V4 Flash 284B + DSpark on a single RTX PRO 6000 96GB. Short version: DSpark: ~15–17% faster generation on my coding workload On this setup, the DSpark dra

Everything started with the sudden death of my old ASRock J1900. While looking for the perfect ITX replacement, I stumbled upon the Chinese CW-NAS-ADLN-K motherboard, which looked perfect on paper: In

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

Looking for V100 users to share your config and it's performance. GPU: Tesla V100 PCIE 32Gb Qwen3.6 27B Q4_K_M + Q8_0 MTP 128K context length Pi coding agent llama.cpp model preset: [*] spec-default =

J'ai consacré beaucoup de temps à l'optimisation de DeepSeek-V4-Flash-0731 GGUF sur une seule RTX 3090. Mon exigence absolue pour chaque configuration était la suivante : Le modèle doit rester utilisa

Hello everyone I want to join the hype of posting specs. CPU: AMD EPYC 74F3 24-Core RAM: 8 Channel 3200 DDR4 GPU: RTX A6000 48GB Prompt processing is in the high 70t/s (got down to mid 30t/s at 300k c

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

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 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

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

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

276B total parameters, 12B active, 1M context window. Blog post: https://thinkingmachines.ai/news/inkling-small/ NVFP4: https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4 GGUF's by Unsloth: h

Context: I want to give the community an Open Research (well open under Apache 2.0 clause) - tool that allows everyday users like us to look deeper into the local models we use consistently. Mechanist

Everyone is talking about Kimi K3, but if you jump straight into the technical report, you’ll quickly realize it’s standing on years of research -- just like any breakthrough is! If you want to unders

It has only been two days since I move 100% from Qwen3.5-27B F16 to ThinkingCap-Qwen3.6-27B F16. Where I was getting tps in 30-40 range (depending on the size of the context), I am definitely getting

Following some recent commits in llama.cpp, preserve_thinking behavior for chat templates included in older DSV4 ggufs got broken. This makes the model pretty dumb in a coding agent context. Adding kw

tldr; we are going to host K3 on A100s (yes, thats correct, we'll try to see if it holds up), H200s & B300s - expect results for A100s & H200s this week while we setup the B300 cluster this weekend &

Just noticed this exists, 1M context MOE with 48B par seems just like what Ive been looking for - it runs pretty damn fast too compared to Qwen 3.6 35B. after some testing it seems capable of producin

https://huggingface.co/swiss-ai/Apertus-v1.5-70B https://huggingface.co/swiss-ai/Apertus-v1.5-8B Apertus 1.5 is a family of 8B and 70B parameter language models designed to advance the state of multil

TL;DR: Everyone says QLoRA past ~13B is dead on a 24GB card. I got the full 32B FLUX.2 dev transformer QLoRA-training resident on the GPU on a 7900 XTX under native ROCm on Windows (no ZLUDA, no CUDA

Paper:https://arxiv.org/abs/2607.19058 Code (GitHub):https://github.com/nuemaan/skewadam Hi everyone, I just published a preprint on a new optimizer designed to tackle the massive VRAM bottleneck in M

Hey everyone, I’m a Full-Stack Developer with 6+ years of experience. I’m relatively new to AI-assisted development workflows and want to build a production-ready, enterprise-level Next.js web applica

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before

Below Upstream Status sections are from https://github.com/PrismML-Eng/Bonsai-demo Upstream Status for Binary Q1_0 is supported out of the box in upstream llama.cpp across many backends: CPU (generic,

A developer expanded on Andrej Karpathy's LLM Council concept by implementing an enhanced system with Docker containerization, Model Context Protocol (MCP) integration, skill modules, web search capab

ChatGPT's memory feature stores conversation context and user preferences within individual chats. This Reddit post discusses prompts designed to retrieve or extract the stored memory data that ChatGP

OpenAI announced a new memory architecture built on its 'dreaming' background process that improves ChatGPT's ability to carry forward useful context, follow user preferences, and remain accurate as t

MiniMax M3 launched on June 1, 2026 as the first open-weights model to combine frontier coding, a 1-million-token context window, and native multimodality. The model achieves top-tier performance on c

Mistral-7B v0.3 model achieves significant memory optimization when running at 128K context length in llama.cpp, reducing live VRAM usage from 22,657 MiB to 13,235 MiB while maintaining minimal perfor

A developer created a local Model Context Protocol (MCP) memory server that integrates Ollama to provide AI coding assistants with persistent memory capabilities while maintaining complete privacy and