Search: “r-localllama”

I asked 3 Qwen 3.8 models to solve recent AtCoder problems at 4 thinking efforts

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

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I have an ESC4000 G3 with 8x T4s in it - what is the fastest way I can deploy Qwen3.5-9B for about 10-15 users concurrently: currently using llama.cpp

DGX agent

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

model-releasesr-localllama

10 Oct 2026

Improve token per second without touching quant

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

Agents

Is anyone running Qwen3.8 Flash Next with a 1M context?

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

agentsr-localllama

microsoft doubles down on local ai with nvidia, but with a cost

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

Hardware

No more RTX 5090

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

hardwarer-localllama

Open-source Mac app that runs EmbeddingGemma 2 locally to search your files by what’s in them

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

Qwen 3.6 35B A3B: 131K context + vision on 6GB VRAM

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.

Qwen Flash Next @ 137 tok/s & 3,497 tok/s Prefill w/ 512k context on a 5090, 192gb ram, Windows Build, comparing Strata and Infernix

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

Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel

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

Strata with Qwen3.8 Flash Next UD-Q4_K_XL

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

Typesafe ai raised 870m $ on hype (jev)

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

A 24KB stand-alone HTML-LLM that can generate consistent stories

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

DGX Monarch - Dual Spark Rendering w/ ComfyUI

For those of you who have been wanting to utilize your dual Spark cluster for image/video gen with ComfyUI - today is your day. I started this personal project back in April of this year, and it’s at

Epyc inference rigs

I posted on here a while back asking for advice on upgrading to an Epyc system. Thanks to all who commented. I’ve taken the plunge and ordered: - Epyc 7443 CPU (24 cores, 48 threads, 128 lanes) - H12S

GitHub - google-ai-edge/ml-drift: GPU-Accelerated AI/ML Inference

Blog Post : ML Drift: Next-Gen GPU AI/ML Inference at the Edge - Google Developers Blog The Google AI Edge Team is excited to announce the open-source release of ML Drift, our high-performance, cross-

GLM 5.3 Flash opensource @ the top of Artificial Analysis Cyber Index over Claude.

It seems Dario's 'too powerful for you users' strategy is paying off: we have two open models at the top of the leaderboard, surpassing every single model from Anthropic. Open source prevails. Even Mi

H2O-Lightning-4B: Apache-2.0 4B Decision model, official #1 open model on JevBench (above Jev)

Disclosure: I work at H2O.ai. We released H2O-Lightning-4B, an open-weight (Apache-2.0) model for the 'decisions API' style of inference that Jev made popular: you send a state plus typed questions (p

I made a portable AI memory format that transfers both context and skill

Last month I created a portable memory format as a .txt file as a small project. I named it as: Memory Journal, in a shape of a skill card. It's essentially a large prompt wrapped in a text file. Init

Just another purely open-weight models benchmark

Live results, coding benchmarks included, agentic benchmarks included, domains and other classifications filters included. (Spoiler: DeepSeek-V4-Vision-Exp rules, but other models have their rule area

LlamaTale v0.43.0 - MCP Server for story creation

In the dawn of the LLM craze, when wild Llama2's roamed free and 4k tokens was considered a fairly long context for local models, I forked an interactive fiction and MUD library called Tale to experim

My 'Anthropic/ChatGPT in a box' is... now open source and publicly hosted on Github.

It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu

[Paper] DLoop: Looped Speculative Decoding

Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With

[Paper] EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory

Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Be

[Paper] Stepped MoE: Segment-Level Routing with Configurable Inference Complexity

Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures

Qwen 3.6 35B appreciation post

Referring to specifically Unsloth's UD_Q4_K_XL quant because that's going to be a question, and is relevant regardless. It's old now. It's not great at coding medium sized or even small-ish projects.

Qwen 3.8 Flash Next-GSQ-RCO-IQ2_XS at ~21 tok/s on just an RTX 3060 12GB + 16GB DDR4 RAM(No gate pruning, 100% bit-exact)

(Don't judge by the screenshot, the cache is cold. It hits 24+ tok/s with a warm cache!) About two months ago, I made a post here asking whether predicting which MoE experts would be used on the next

Qwen-Image-2.1-Turbo released!

Qwen-Image-2.1-Turbo, create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation archite

Qwen3.8-27B: 159 tok/s on R9700, 64 tok/s on Strix Halo

LemonSeed Studio is an iPad editor/IDE with on-device inference on an AMD GPU in a Thunderbolt enclosure. It embeds the unmodified upstream Linux amdgpu + amdkfd driver (mac_linuxgpu) as a PCIDriverKi

Qwen3.8-27B UD-IQ4_XS Heretic + MTP on a 16 GB card with 55-68 tok/s (24gb and 12gb versions available too)

I wanted the uncensored Qwen3.8-27B (llmfan46's Heretic build, MTP head preserved) on my RTX 4080 with MTP on (because I've been testing TONS of configs, and realized that would be the one) And none o

Qwen3.8 27b with 200K ctx + MTP on 12GB Ampere Cards

Hello all! Thank you to everyone that contributed their feedback and notes for LlamAmpere the last time I posted. I've continued to chip away at improvements for the 3090 crowd -- this release is mode

rtx 4090 + huawei atlas duo for qwen flash next ?

I have rtx 4090 and 64 gb of ddr4 ram. This is enough to fit smaller quants of qfn, but I found them not great and switched back to qwen3.8. However, there are option that bother me a lot, buy huawei

Running the uncensored Qwen3.8-27B (HauhauCS) on a 4090 at 262K context and ~130 tok/s

HauhauCS ships their uncensored Qwen3.8-27B as GGUF only. NInfer, a C++/CUDA wanted its own format. Now the same model that ran at 91.6 tok/s / 131K under llama.cpp does: 262K context (the model's ful

SlopSoup TV. A live, never-ending 24/7 pixel-art TV network inspired by old-school late night Adult Swim. No human makes any of it.

SlopSoup TV is a live, never-ending pixel-art TV network. Every script, character, voice, camera cut and schedule decision is made by models. It's made to be very absurd, dark and strange. Hardware: o

Stepping away from Benchmarks and Code, what models are you using for Creating writing projects

All anyone ever talks in this sub is about x new model having x benchmark scores and how well it can code. Or some new tech to increase t/s for qwen. So I wanted to go back to the roots of this sub an

Strix halo + CMP 170HX setup

A bit of backstory : i got lucky in August and was able to grab a CMP 170 HX 8GB for around 500 on alibaba. I tried later to get a second one for around 1k but my order got cancelled and prices went t

tencent/Youtu-Parsing-Omni · Hugging Face

Youtu-Parsing-Omni is a compact (5B) omni-modal parsing model. Given a single input — a document page, a natural image, a chart / flowchart, a geometry figure, an audio clip or an audio-visual video —

Tested Mellum2.1-12B-A2.5B on PI Coding Agent - surprisingly usable, but not great at one-shot projects

I tested Mellum2.1-12B-A2.5B (Q8) locally using Pi and llama-server. All five tests were one-shot. Results were pretty mixed: - Pelican SVG, Browser OS, Minecraft: Poor results. - Bouncing Hexagon: Ph

2 months (meme-d) research, Do People Notice the Difference Between AI Models?

Preamble and disclaimer, Sample size: 8, at any size of form this research is just screwing around being writing it down. This was just a little,(well... big), curiosity test I wanted to run to see wh

$2800 rig with 8x Radeon Pro V620 (256 GB VRAM) + custom vLLM fork = Qwen3.8-Flash-Next at 60 to 100 t/s decode and 3000+ t/s prefill

Post title is slightly misleading, I don't think you can get these for $350 each anymore but they're still pretty cheap all things considered. They're Radeon Pro V620's which are older RDNA2 enterpris

[audio.cpp] Recent updates you might have missed: Higgs Audio TTS use 48% less VRAM (< 6GB), HTDemucs 2.2× faster, PocketTTS 2.2× faster on CPU, and WebUI generation history feature

Hi all, a bunch of performance improvements have been landed in audio.cpp. The biggest highlight is Higgs Audio TTS, which now runs with around 6 GB VRAM, a 48% reduction in peak memory usage compared

Best current R9700 inference engine?

There are way too many forks to keep track of, so I've gotten lost. As far as I can tell, Radiance VLLM is best for models that fit in GPUs while some form of llama.cpp is probably best for MOE RAM-sp

Can any open-weight models handle a decomp/recomp project yet?

Opus5.5, Sol6.1, Fable, and Astra have all proven they can and the scene has exploded this past week. Part of that is from the tools and feedback loops maturing though. Are any open weight models (at

chatgpt's new intelligent ui was reverse engineered in less than 24 hours, and apparently you can recreate it with local llms

came across a pretty interesting technical breakdown of chatgpt's newly launched 'intelligent ui' feature, and thought this subreddit might find it interesting. for anyone unfamiliar with the concept,

Comparing Qwen3.8-27B fine-tunes and baselining vs. frontier

TL;DR: For my use case, mradermacher/Signal-3.8-27B-Terse-Coder.i1-Q4_K_M proved out. Ymmv based on your domain-specific tests. Time taken to solve problems compared to frontier models is massive; esp

Currently having high success with this little niche finetune i found sitting in the corner of huggingface

Currently having high success with this little niche finetune i found sitting in the corner of huggingface If you want to try it out here is a smaller quantisation iq3_s works really well in my codeba

GPU - Vulkan llama.cpp benchmarks sorted by price to performance

This table to help anyone looking to build a budget Data Center homelab. I copied the bulk of value based, mid level, decent speed results GPUs and feed it to AI or SI and here are the recommended res

Halogen + Qwen Flash Next keeps getting better

With latest Halogen version update (0.17.2), decode is consistently at ~45 tps even at high context with Qwen 3.8 Flash Next on a 128GB Strix Halo. This is some great work u/peonist-ai. Have been pump