Search: “r-localllama”

Following up on yesterday's post about running everyone's faves on 2 x 16gb cards while maximizing performance and KV. Previous post data used abandoned Cu130 VLLM image. Stats here are done on cu129-

TL;DR: On a Qwen3.6-35B-A3B Q6 setup sized for 64K context on a 24GB RTX 3090, spilling eight MoE expert layers to CPU freed enough VRAM to increase -b from 512 to 1024 and -ub from 128 to 512. Prompt

Hi all. These are my system specs: dual xeon e5 2696 v2 , 160gb DDR3 ram ECC(1600mhz), 3 gpus: 3060 12gb, p100 16gb, 3050 6gb. And a 400gb nvme sdd RAID0, 3000 mb/s. The model is Deepseek-flash-0731 U

Looking for best current solutions for combining cloud models and local models seamlessly inside a harness' orchestration Edit: Right now, we don't have harnesses (that I'm aware of) that are blending

First of all, my setup: Ryzen 9 5950x DDR4 3200Mhz 64gb (2x32) Dual 3090s, no NVLINK Runtime: llama.cpp Nvidia Drivers 610 Windows 11 25H2 Qwen 3.6 27B Q8 I've been using llama-server with --split-mod

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

So I can either pull the trigger on a 128gb AI max+ 395 laptop or wait for RTX Spark for LLMs. Maybe I get it now and the price of the spark is super high so it's a good purchase or maybe the Spark sh

https://preview.redd.it/o6ik6qboeohh1.png?width=1134&format=png&auto=webp&s=4016f26c50c1d93bd3d0c7e880e9b55a2d75310f I have been running Qwen3.6 27b for a little while (mostly coding tasks) and recent

Just came across this coding benchmark: SciCode Artificialanalysis.ai reports a ranking which contradicts the feeling we've towards those models in real life coding. Is Gemma 4 really that good, or a

As for me, I own a system with an RTX 5090, Ryzen 9 9950X3D2, and 64 GB of DDR5. Every time I see research come out with a new way to train AI, I immediately think to try it on my system to see the re

Seen a ton of posts today about the DeepSeek API price hike. Half the feed is doom-posting, the other half is explaining basic GPU economics. Honestly, I get the cost side. Sub-cent tokens were never

[Fully open source under GPL3, made from the ground up for use with local models, no subscriptions, no corporate backing] When i first started this, it was meant to be a fully lightweight, extremely m

I'm the author, so discount the enthusiasm accordingly. This is an unaffiliated community port, not endorsed by the vLLM project, which it uses to verify its correctness. What started it: I love vLLM,

I built this because the existing benchmarks were using random data and with MTP content types can vary a lot on what performance you see. 5% or more with content types. BetterBench is designed to hav

Link to the article: KV Cache Quantization Benchmarks: KVarN, Precision Tail KLD benchmarks with BeeLlama.cpp v0.4.0, fork of llama.cpp with more KV cache quantization options. Models: Qwen 3.6 27B Q5

NVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-readable representations with text, layout classes, bounding boxes, and reading-order information. Given a Red, Green, Blu

nvidias nemotron omni is open weights and it sees, hears and reasons. theres already a 4bit mlx quant on hugging face but only the text backbone loads with standard mlx tooling. the model card says it

🐦‍⬛ Magpie-TTS Multilingual 🦜 Nemotron Speech Streaming EN 0.6B 🦜 Nemotron-3.5 ASR Streaming 🦜 Parakeet CTC 1.1B 🦜 Parakeet TDT 0.6B v3 🥦 NanoCodec Merged PR https://huggingface.co/nvidia/magpie_tts_m

GGUFs here: https://huggingface.co/ReadyArt/gemma-4-31B-it-scotoma-2-GGUF Disclaimer: By slop, we are specifically talking about specific tics with the model(sentence structures), but this doesn't inc

I love to see these impressive models coming out that compete with the giants from companies like Z.ai, Moonshot, Alibaba, etc. A win for the open source/weight community is always welcome. While I am

Users also report that the free version was significantly downgraded after the release of the new models this is very important for us when considering local hosting. A lot of people decided not to bu

So I had been building ScreenMind, kinda like local ai desktop assistant that uses Gemma 4 for screen analysis, voice memo transcription, and meeting transcription — all through llama-server. Everythi

daily reminder not to trust benchmarks and run it yourself. claimed e2e speedup is ~40%, forwards are ~140% faster I would wager that compared to a naive kernel anyone can write it's more in the range

Most agent memory setups run a model call on the way in. Something reads the turn, decides whether it's worth keeping, rewrites it into a 'memory', tags it with a type and an importance score. That's

There are a lot of LLM benchmarks but few, if any, harness benchmarks. I am thinking this would be a really good community project to build one. End goal: a leaderboard of harness performance (multipl

Hey everyone, I’ve been building Speechfony - a desktop app for reading PDFs (and EPUBs) with offline text-to-speech. Open a document, listen sentence-by-sentence with highlighting, or export selected

Inspired by a post from u/giveen I motivated claude (no patinence on my side to work through everything myself) to help me get DS running on my MacBook M5 Pro 64GB and it exceeded my expectations.. be

I'm mosty interested in 128-192GB VRAM with 128-256GB RAM to spare, so SSD streaming is basically not even necessary. Seems only FP4 is supported, so older hardware will likely be slow - no Unsloth GG

https://github.com/yhfgyyf/vllm-deepseek-v4-sm89 I couldn't believe that someone actually got vLLM working with this particular set of GPUs, but here it is. The video is from right after I got it work

I mean, Deepseek V4 Flash is an absolutely fantastic model, even though I can't run it on my machine it's so fascinating to see how it performs. Knowing that potentially it could be run at home is rea

It's the purple cluster on the top left (the good corner...) I'm running the MXFP4 version from Bartoswski with Dspark at 1K t/s prefill and 90 t/s gen (average). I tried different sampling params, yo

A follow up to the launch of Mference, it now supports and runs Inkling-Small 276B-A12B. Inkling-Small (Thinking Machines, Apache 2.0), from the pipenetwork/Inkling-Small-MLX-4bit conversion: 276B tot

A Qwen3.5-35B derived model with an interesting architectural difference that results in larger throughput and less token consumption (allegedly): https://huggingface.co/internlm/Intern-S2-Mobius subm

As you all know the model is 2.69B parameters with a 128K context window and purpose-built for multi-step agent workflows. What you are seeing is the Q4_K_M GGUF running on my own inference engine bui

TensorSharp's MoE CPU-offload feature has been merged into main. Here is the parameters description of this feature: Mixture-of-Experts CPU offload: --n-cpu-moe <N> | -ncmoe <N> Keep the routed MoE ex

Prime Agent is an open-source coding and research agent for general and long-running work. A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficien

So one of yall mentioned that cuda 13.1 or 13.2 is broken for unsloth so I looked in to it, and they were right. I had 13.2 installed, after I switched to 13.3 no more looping!!! Before the cuda updat

Questions & Responses(in BOLD) below. Favorite question(s) moved to end of the thread with combined responses(removed duplicates). Be optimistic folks. I'm sure we're getting other models too apart fr

People may remember the Qwen3-TTS llama.cpp demo from a few months ago. That PR said it probably wouldn’t be merged because llama.cpp was missing some of the graph and API pieces it needed. A new impl

Hey everyone! Scenema Audio is now a native ComfyUI custom node. Same model that powers scenema.ai now quantized so it fits on 8GB VRAM. When we first released it a few months ago as an API and Docker

So I'm looking at https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF and I realize my 128GB of DRAM just isn't cutting it for this (incredibly powerful) model. If only I had another 64GB, I th

We run a local model instance in our company that the dev we hired built for us. We're a trade business and we want to further use our on hand hardware for it. The specs given we have is a 5090 gpu wi

Xiaomi-Robotics-1 is a robot foundation model trained on over 100K hours of real-world manipulation trajectories. It is a Vision-Language-Action (VLA) model engineered for out-of-the-box mobile manipu

Liquid AI released LFM2.5-2.6B today, and this might be more relevant to local AI than another massive model most people cannot run. The model is only 2.69B parameters, has 128K context, supports tool

A new llama.cpp PR (#26563) adds a heatmap that tracks which MoE experts are used most often. Instead of keeping every expert on the GPU or offloading all of them, it caches the frequently selected ex

I‘m doing a research proposal at my company about running local LLMs to replace daily coding models. Qwen 3.6 27B (or 3.8 potentially) is widely seen as the best model in that 20-60GB space, is that s