Knowledge catalogue
With the vLLM Semantic Router team we released Decision 3.0 (d3), our new SOTA decision model family ranging from 0.6B to 27B, Apache-2.0, all reading text, images and video. How it works - Built on Q
Hey everyone, I made whatmodel.sh To use it, Enter your RAM and GPU See what models you can run locally with its rank and the command to run it Compare them with API models on the same score Building
EXL3-QLoRA I've built this over the last few months and I think it's stable enough to share now. A fork of ExLlamaV3, this is a native ExLlamaV3 training implementation with plain PyTorch, without tra
Write-up, serve command and raw data: https://github.com/lukolszewski/vllm This shows vLLM fixes and setup that let you put PLE on SSD and save 108GB of system RAM, use int8 kv and squeeze more users
I wanted to track grocery spending per item without sending my receipts to anyone, so I built an Android app around a small vision model that reads a photo of a receipt into JSON: store, date, total,
I've been tuning llama.cpp on my Strix Halo box (Radeon 8060S, 128 GB) and the numbers are good enough to share. It's a branch called rdna35-perf on top of halo-box/strix-llama.cpp, a llama.cpp fork a
Inspired by DigUp for macOS, VectorDash aims to be a full Windows 11 search replacement, powered by EmbeddingGemma 2. Full indexing: search by meaning or exact words across photos, screenshots, audio,
Ive been using OpenWebUI's inbuilt web search and I can't put my finger on it, but it's not quite as polished, reliable, seamless as using Claude or Gemini. And its often slow beyond a limit that make
I'm trying to figure out the best long-term upgrade path for my setup. Right now my GPUs are spread across several consumer PCs: Main: 5900X, 32GB DDR4, 2× RTX 3090 24GB. Secondary: i3-10320, 16GB DDR
Just wanted to share a build I am doing for a client. $18k. Specs: Threadripper 9970x 128gb DDR5 ECC 5600 4x AMD Radeon R9700 AI Pro 32gb(128gb total VRAM) 1600W PSU(GPUs are undervolted and under 210
For the past few weeks I've been trying out converting existing dense models to sparse Mixture-of-Experts models, with no pretraining from scratch. The Idea If you can turn a dense model into an MoE t
It's been 4 years since c.ai first hallucination model, and yet, we're nowhere good enough at LLMs in terms of spontaneity/interesting hallucination features. Fortunately, last year brought new archit
Summary of project status metrics Since StillDeadcode has released his new radiance inference engine, the writing is on the wall for the old vllm-radiance builds, people have moved on to newer and bet
My Jetson Orin–optimized engine, little-gemma V1.0, substantially outperforms llama.cpp. Even after exhausting every practical GGUF option, however, it still falls short of the ideal performance level
I run local LLMs pretty much around the clock, but I also enjoy gaming on the same PC. Having models sitting in VRAM while gaming isn't exactly ideal, and constantly unloading and reloading them manua
I was running Qwen3.8-Flash-Next (base and later Swift-1.5 NVFP4) on a paid pod seat for about a month, and it started answering with short strings that echoed its own earlier reasoning. I assumed the
There is a lot of discussion which quant to use. Especially a lot of post how much speed you gain with FP4, but not much about quality. So i tested some of the models that I use on my R9700. Models: Q
DISCLAIMER: the post and the model card was made with the assist of AI. Hiya, I’m releasing Recursive BitNet N-Gram 102M, a small experiment combining ternary weights, shared transformer layers, and h
indexes PDFs, Office files, scans, screenshots, audio and video, and finds them by content. A query like 'when did they decide to freeze hiring' opens the recording at that second. * EmbeddingGemma 2
Currently contemplating adding either M5 Ultra 256GB or dual DGX Spark in addition to existing 5090. I have been researching for past few days and have zeroed in on dual sparks because higher throughp
https://preview.redd.it/r2ledkyuntuh1.png?width=1684&format=png&auto=webp&s=9ed4518b2d0f20f44835ebbfac605581da186a57 AesCode generates information-rich visual artifacts such as slides, posters, and da
No more excuses for GPU-poor folks not to start LLMing! Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU) ~9.3x smaller than FP16 (ideal) | 98.2% of FP16 intell
I'm genuinely shocked! I was able to run Qwen3.8-Flash-Next-oQ4e-mtp on M3Max 64GB with oMLX! Even a couple of weeks ago, I wasn't able to get it to run. I just tried the latest commit for fun, and it
Hey r/LocalLLaMA! Maintainer here. Some of you might remember OpenMed from our 1.8 post a few months back. Quick recap: it's an open-source (Apache-2.0) medical AI toolkit with one rule we never break
I have an app that processes ~500 different documents every day. An agent analyzes them, sorts them, tags them, extracts various legal nuances, creates descriptions, etc. So far I've been using Qwen 3
I love Dash2Dock Lite, but Icedman is always a week or two before updates. I randomly ran pacman for the first time in weeks, not realizing GNOME had a major update. I told it to fix it in DeepSeek Ha
This will be my third update on the bc-250 cluster and for my first forray into local ai I have been having a blast. This has turned from a weekend experiment into something I think I will use in the
I stumbled across this as I was currently having glm5.3 flash run only around 10tok/s basically unusable. With Maya i am running 30 tok/s now. GLM5.3 feels even with this low quant like a much more en
This is a follow-up to my post from yesterday (https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/) detailing the CUDA megakernel. Thank you all for
Alright, I learend a few valuable lessons since I posted about a week ago, and I've made more strides with the DDR4x Ascend 96GB 310P cards I purchased, so here we go, meet the new friendly and less v
Based on my experiments and feedback from claude, it seems extremely unlikely and even if I get it working, not practical because of minimum context. But just asking here as a last effort. I'm able to
As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length
webAI released TwIL LM3 Pro on September 30. I haven't seen it posted here yet, so I went through the model card, and their comparison chart is attached. It's a 3.66B model built on IBM's Granite 4.2
Hi, Because sometimes you need both speed AND quality, I made my own Qwen 3.8 27B Swift 1.5 quant. It's an 8 bits AWQ + GPTQ quant. Made with love and precision (read the model card) on my 4 RTX 3090,
https://preview.redd.it/9k5fw4j47puh1.png?width=545&format=png&auto=webp&s=ff0a6ff6fb69ae9a9bcd579bd612c553fc3e2a00 https://preview.redd.it/xcwzlmo77puh1.png?width=664&format=png&auto=webp&s=c8a637e2e
Basalt: Blackwell inference engine Hi all! So over the last week I've been working on a Strata fork that's heavily tuned and can achieve throughput up to 2.6x what Strata usually does on the same weig
I recently started using local LLM for mostly coding tasks for my personal projects. I also have found local models responding much better for topics such as health, fitness, ergonomics etc. compared
hello, I currently use swift qwen3.8 gsq rco in iq3 xxs in strata. The model is only used for hermes agent. I have 64GB of ram + 32GB R9700 where the model runs on. But I also have an rx7800xt with 16
Around 3 weeks ago, I posted on this subreddit about how Jev used my one-year-old architecture, og post: https://www.reddit.com/r/LocalLLaMA/s/VpuMJt577S Then, just after that, I introduced Laya, the
For what it's worth. I'm a mechanical engineer by education and software engineer in practice over the past ~20 years. Playing around with local models has been a recent hobby. Today I decided to do a
A conversational AI plush toy where nothing leaves my network. Everything runs on my LAN: STT: Whisper LLM: Hermes 3 Llama 3.1 8B (Q8_0) TTS: Kokoro Memory: per-person (LangGraph). It knows who it's t
I wanted to compare gemma 4 31b qat q4 and qwen3.8 27b iq4_nl, so I run SWEBench verfied on them. The original gemma 4 chat template doesn't work with preserve reasoning, so google released an update
I was looking into finalizing my daily driver and it was not clear which one to chose. I have a 5060ti 16GB so I picked the following contenders, which seem to be working at a reasonable speed on my h
Hi All: I have an Asus ESC 4000 G3 with 128 GB DDR4 RAM - I tried putting in my V620s but couldn’t put more than 2. Sadly, pivoted to T4s, these are 72 watt passive cards and I thought I could use the
I ran a small test for Dory to see whether a local model could update one fact in a synthetic notes file without losing any of the others. I asked Gemma and Qwen to rewrite the file, return a patch, o
Spent the past month tweaking and experimenting with many different numbers to achieve 30tps. Hardware: -Rx6700xt 12gb vram (AMD) -2x16 ddr4 3200 ram -r5 5600x -llama.cpp vulkan sdk -window11 (no wsl
So, while doing a search to read the model card again, and because I didn't memorize the huggingface URL, I saw an AI 'answer' at the top of the search, stating that while it natively supports a ~244k
https://preview.redd.it/uyjw2lcrvkuh1.png?width=1536&format=png&auto=webp&s=ce991be22faec3de5cbaf39119c14782b6ed0af3 link to article TLDR; new microsoft surface and nvidia rtx spark laptops are starti
Hey everyone! 👋 Feeding multi-gigabyte files (SQL dumps, videos, audio, PDFs, and spreadsheets) directly into LLMs often causes context window overflow, GPU VRAM bottlenecks, massive token bills, and
Nvidia reportedly halts GeForce RTX 5090 production in favor of AI data center and professional GPUs — impending supply drought expected to drive up prices, RTX 5080 24GB rumored as new gaming flagshi
DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and l
Running HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive Q4_K_M with llama.cpp at ~600 tok/s prefill and 23 tok/s decode, 131k context window, Q8 KV cache - on an RTX 2060 6GB + 32GB DDR4 RAM.
Strata vs Infernix on the same model, RTX 5090 — A/B at 262k and 512k context Hi folks, another inference engine post for your feed. I was blown away by Strata, but saw someone else post about Inferni
I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel that is 1.4-1.9x faster than llama.cpp depending on the task/context length. The results and cod
I am developing a Windows-based local AI virtual spouse application that supports adult roleplay. My goal is to run it without a server or external AI APIs. I am looking for existing open-source modul
Been seeing quite a few posts about Strata lately, so I figured I'd give it a shot on my RTX PRO 6000 and I am very impressed. Most of the benchmarks I've seen are using IQ2 or IQ3 quants, so I wanted
https://typesafe.ai/blog/series-ai Meanwhile one of the reddit members apparently developed the same architecture a year ago: https://www.reddit.com/r/LocalLLaMA/s/8zQXqCNDlb Looks like ai valuation n
Hey everyone! 👋 After months of building, we’re finally opening the first spots for **Many Agents**, a new product from Dome. The idea is simple: **What if you could create your own team of AI agents
https://preview.redd.it/7sknv6omnguh1.png?width=1023&format=png&auto=webp&s=4aef1dc464d008b00f104519eb5399ba6132ad69 Want to try? Just go here and click 'New seed' a lot. You will get different storie
I made BigMoeOnEdge+ (BigMoeOnEdge-plus), a fork of Helldez's BigMoeOnEdge, the engine that streams MoE experts from flash so models bigger than RAM can run on-device. It adds a full Android chat app