Knowledge catalogue
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,
Dataset of 43 images, captioned with qwen3 VL 4B instruct, 50 word caption focusing on: Composition, Subject's hair, expression, clothes, pose, background Training Parameters: (10 rep x 43 image) x 6
Setup as follows: Proxmox -> Debian -> Docker -> Ollama. Other containers work. Compose file contains gpu device. Does it need nvidia runtime or any other options? If someone could provide an example
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?” “
There is a lot of buzz around running 100B parameter models on cheap local hardware using ternary (1.58-bit) quantization like Microsoft's BitNet architecture. The theoretical hardware shortcuts are i
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
I am trying to decide on buying the ollama pro subscription. But their usage policy is vague as hell. I don't mind the 'GPU usage time' but how much GPU time do I actually get? I still don't seem to f
Most graph-based LLM interfaces use a canvas as a visual layer over what is still a linear chat. I wanted the graph itself to determine what Ollama receives. ThoughtDAG has one rule: wires are the con
This was my Bachelor's Final Project: implementing YOLO26n inference completely from scratch using ARM64 Assembly Language and C, without relying on existing inference frameworks. The goal was to unde
I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kag
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
What quantization or storage size tiers should we expect further down the line? This site suggests q4 and q8 are coming. But I don't know how to translate that to storage size for local inference. htt
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
NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial. I'm building a SaaS platform in Sri Lanka that handles documents a
I am setting up codex+ollama+qwen3.6:27b for my hobby coding project on a windows pc with rtx5090 - earlier i tried to setup vllm in Ubuntu container but couldn’t get that to work - now using ollama,
https://preview.redd.it/umcg41g2kkfh1.png?width=502&format=png&auto=webp&s=7b1ed545cd1c7840a461101bd02d78ab4a9cb40d I tried to create an Ollama account to purchase a subscription a few months ago. But
I have been running some experiments with smaller open-weight LLMs on multiple-choice questions of Swedish medical licensing exams. On a dataset called MedQA-SWE, GPT-4 scored 84% accuracy in 2024 and
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
How does it work? I read that the limts were more than what Opencode Go offers and subscribed to the 20USD Pro plan. But In practice based on how the usage bar fills up it seems like the 5 hour window
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
Hey everyone, I have been looking into how people source compute for their Inference workloads (and in general). I wanted to understand some specific pain points here. If you've used online services l
There are a few reasons why problems from International Mathematical Olympiad function as a good benchmark for LLMs: - The problems are new, not included in the training data of any model - Hard math
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 have tried ollama locally on my gaming PC (5070 with 12bg of VRAM) It works pretty nice on qwen2.5-coder:7b (and 14b) So I decided to take a step further, and install an openWebUI instance on my hom
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
I apologize that this question has been asked in various flavors over time, but I couldn't find anything in the posts before that matches the options I have. I have a PC and a mac, both available in m
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
A little over a year ago I got frustrated trying to fine-tune SDXL on my RTX 3060. Every option either forced lower resolution, locked away important settings behind massive config files, or needed a
About 2.5 years ago I tried making an AI comic. It failed. Characters changed, environments drifted, and every page needed manual editing. So I started over with one simple rule: One prompt = one fini
I wanted to challenge myself and see how far I could push Krea 2 and LTX 2.3, so I decided to create a short cinematic action trailer. It ended up being one of the most enjoyable AI projects I've work
like 8 months ago I was tinkering with both and gemini was so much better i went with that. I had a project at work that I needed an AI to sift through a manual and schematic for and no matter what, g
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
https://preview.redd.it/yw41s1jikefh1.png?width=1942&format=png&auto=webp&s=3a180ae6443c1db9f7b0ce621533a4b2aa553921 Hi, I've been running Ollama on my Unraid server since the llama2 era. I use to be
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
I ported Krea 2 Identity Edit support to Forge Neo as an extension — the instruction-based, identity-preserving edit LoRA that until now required ComfyUI + custom nodes. It replicates the full dual-co
Hey everyone, I’m posting this question in the subreddit because I haven’t been able to figure it out with the help of AI. I’ve asked ChatGPT and Gemini, but their answers are all over the place. So,
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
Hi everyone, I'm looking for a local alternative to Kling AI 3.0 Motion Control that I can run in ComfyUI. What I'm specifically looking for is a model or workflow that allows me to: - Control charact
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