Search: “r-localllama”

I just managed to get it running on windows and this thing is fucking insane. I get around 550-720t/s depending on task at hand. Previously to get to such numbers i would have to do batching and agent

Nvidia CEO Jensen Huang “Distillation - learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from one

“China is working hard to catch up, and the United States should take steps to keep its advantage. Most important, it should continue to prohibit American companies from selling the most advanced chip

I've been building Krasis, an MoE-focused runtime for streaming big models through limited VRAM on NVIDIA consumer/workstation GPUs, and I think this is the most interesting result so far: Ornith-1.0-

I finished the speed leg of my spec-decode benchmarking for Qwen3.6-27B, main algorithms across quants. Overall: the heavier the quant, the more spec-decode buys you (10 of 10 speculative configs rank

Running local models means every token counts — an 8K or 16K window fills up fast when you're stuffing graph context into prompts for RAG. I benchmarked 10 graph serialization formats (JSON, GraphML,

I was refreshing my youtube and found out my favourite reviewer uploaded a battery test of 78 smartphones: https://youtu.be/MpgUFrsIWSQ the author said they started using robotic arm to simulate a per

Instead of 2T+ models, continuing to release highly capable small to medium size LLMs would really help to keep this community vibrant. Hardly anyone can even dream of running the recent 1.5-2T+ beast

When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “

I am writing a fairly complex C++ windows MFC application. I have a few 3090s and can run F16 Qwen3.6-27B with 256K context and MTP. The quality of code is exceptional with this quant vs its lower qua

This is our biggest comparison yet. We've taken 23 Gemma 4 E4B models from huggingface and ran them through the abliterlitics gauntlet. We also have a new abliterlitics discord, feel free to jump on a

Last week someone here said ThinkingCap and Fable Fusion 'really do beat the OG' for agentic work, so I ran it: 6 self-grading tasks, 5 reps, 3 models, 90 isolated runs. Tooling, since that's half the

GigaChat Audio 10B is an audio-native LLM built on top of the GigaChat 3.1 Lightning text model. A Conformer speech encoder and a modality adapter feed audio embeddings directly into a Mixture-of-Expe

TL;DR llama.cpp fork with more KV cache quantization features, with all claims supported by benchmarks: KVarN, KV cache precision tail, additional types of standard KV cache (q2_0-q3_1, q6_0, q6_1), a

clem 🤗 on 𝕏: https://x.com/ClementDelangue/status/2081056675558195657 • Radical transparency: let’s release the traces from the “rogue” agents so the entire research community can study what happened.

curious how people here approach buying high-end/workstation cards (RTX 6000 Ada, 5000 Ada, etc) for local LLM work, do you actively watch pricing/timing on these specifically, or is the consumer 5090

Running GLM-5.2 (the new glm-dsa arch), Unsloth UD-Q4_K_XL, on a 4-socket Xeon E7-8880 v4 box with 1TB RAM and a single RTX 3060 12GB. ik_llama.cpp, experts on CPU (--cpu-moe), 24 attention layers on

I ran DeepSeek V4 Flash through Claude Code, OpenCode and Pi on my own benchmark, and the quality came out basically the same across all three while the time and tokens spent was wildly different. Cla

Qwen3.6-27B: SFT vs continued pre-training vs RL? I’m interested in adapting Qwen3.6-27B, but I’m increasingly unsure whether conventional SFT/LoRA is the best route if the goal is to add a capability

Andrej Karpathy, a prominent advocate for open-source AI and a co-founder of OpenAI, appears to have removed Anthropic from his X bio, suggesting he may have left the company. Karpathy joined Anthropi

Hey r/LocalLLaMA — maintainer here, obviously biased. OpenSmith is an open-source Python tracing tool for LLM pipelines. The idea: drop u/trace on any function, run opensmith ui, get a full local dash

Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this

https://preview.redd.it/qoogv5m1skfh1.png?width=688&format=png&auto=webp&s=3f06fb28ceec3cd3ab95bcebd0c71374332aac9b Built a simple desktop GUI (Tkinter) that supports multiple local TTS engines (curre

At Bitwize, we've been building Logue, a native macOS app for AI meeting notes and writing, and we just open-sourced it (MIT). We're sharing it here because the whole point is that it runs 100% on-dev

I am seeing more and more videos as posts about how OpenAI is in complete financial ruin, Anthropic isn't much better. Their expenses go with the revenue they make etc etc. Meta made big investments i

Qwen3.6-27b is fantastic! It makes me wonder if there's a hard ceiling to smaller sized models. Do you guys think the ceiling of intelligence for smaller models will be constrained by factors like par

3 year lurker, now i finally got my server up and running. dont know which model to choose. llama.cpp or vllm, what makes more sense? mainly single user with maybe 2-3 more additional users in family,

Hi everyone, I have a system which I have been using for gaming, R7 5700X, 48GB DDR4, RTX3090 24GB. But I want to use it for Local AI to reduce my reliance on cloud AI providers (mainly usage limits -

Cuda and Vulkan Benchmark: TensorSharp vs. llama.cpp I would like to share my latest open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like

If you run local agentic coding harnesses (Aider, Claude Code, etc.), prompt evaluation usually eats up most of your execution time. Every turn re-evaluates thousands of identical prefix tokens_system

I understand that the laguna model is either still buggy or potentially benchmaxxed. So I’d like to know for people who really tested, are DS flash or Hy3 really better in your usecase? submitted by /

Hi everyone! Over the past five months I've been working on DKV (DifferentialKV), an open-source project exploring KV-cache compression for long-context local LLM inference. The goal is to reduce KV-c

Hello, I've been learning how to use local LLMs for a year or so on my workstation, using a RTX3090. Current setup : - i5 12400 - 64gb RAM - RTX 3090 - OS : Fedora KDE workstation I'm using LMStudio t

I’m building the ultimate AI tool vault, but every great collection has a few missing pieces. Note: I will react to every comment AI's currently installed: Qwen3.5-0.8B-UD-Q4_K_XL.gguf(classification)

I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful. Today I’m releasing Inflect v2, with two

I am a long time iOS app developer. In the last year I have been using Cursor+Claude/others to assist with app development. I am concerned that the current low pricing will disappear eventually. I am

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

Everything runs through WebGPU, in-browser or in electron/tauri apps. It's fully portable and supports either Nvidia and Apple Silicon (Metal). The actual kernels are optimized for the specific hardwa

After a long and grueling effort spearheaded by ngxson, llama.cpp now fully supports MCP for all protocols. Over-the-web HTTP servers were already supported in the client (since they don't require any

tests done power limiting the GPU on LACT - real power usage varies wildy at 20W it ranges from 25W to 56W same behavior happens on every setting prompt for the test runs: https://github.com/lukesdevl

https://preview.redd.it/a24z80gr6afh1.png?width=1181&format=png&auto=webp&s=4a844ebe2319eb6230dbdc63c9caf492bed5ff47 So, this came up on: https://www.microsoft.com/en-us/corporate-responsibility/topic

I've spent the last year or so playing around with open source MLX and GGUF models on iPhone hardware. Given the limitations in memory, GPU/CPU/ANE, and in turn the context window I've been trying to

Hi Guys, I'm an old coder and DBA that has been in the field for almost 40 years. More and more the jobs I was doing for work are being taken over by AI and the need for my type of work is diminishing

RTX 4070, 32gb system ram, Linux. NVIDIA-SMI 610.43.03, KMD Version: 610.43.03, CUDA UMD Version: 13.3 Systemd service modifications: [Service] Environment='OLLAMA_HOST=0.0.0.0:11434' Environment='OLL

https://preview.redd.it/wbq8ullnbafh1.png?width=1409&format=png&auto=webp&s=e6d2fe2b1c87c724bc64003c25f917dcee53260f I finally got round to tweaking this, with a bit of help from GLM5.2. The trick to

Since a lot more people are trying to build their own multi-GPU machines, I thought I should help to prevent a common mistake people make with building multi-GPU machines. Which is using an Intel cons