Paul (@paulyoung)

macOS와 Linux 간 연동을 성공시킨 뒤 모델을 로드할 준비를 하고 있다는 내용입니다. Exolabs를 이용한 이기종 시스템 연결과 로컬 모델 실행 환경 구축 흐름을 보여주는 짧은 업데이트입니다.

https://x.com/paulyoung/status/2044765172930322847

#macos #linux #localllms #exolabs #modelserving

Paul 🇦🇺 (@paulyoung) on X

Finally got MacOS and Linux to talk to each other. Now to load some models....@exolabs

X (formerly Twitter)

Peter Corbett (@corbett3000)

Exolabs와 M5, Mac mini M3 조합으로 Qwen3.5-35B-A3B-4B를 로컬 실행해 48.6 tok/s 성능을 확인한 사용기입니다. RDMA는 아직 없지만, 로컬 LLM 환경에서 애플 실리콘 기반 멀티 디바이스 추론 성능과 exolabs 활용 가능성을 보여줍니다.

https://x.com/corbett3000/status/2044838754335223824

#qwen #localllms #macos #m5 #exolabs

Peter Corbett (@corbett3000) on X

It's @exolabs day! Trying it out with my M5 and mac mini m3. Getting 48.6 tok/s with Qwen3.5-35B-A3B-4B. Is this good? #qwen #localllms No RDMA yet as I need a new cable.

X (formerly Twitter)
Just a note for parallel universe me: flash attention is bad for #LocalLLMs

whatcani.run의 실사용 데이터(22,914,944 토큰·4,479회·191명)를 바탕으로 M1 Max(64GB)에서 로컬로 돌릴 수 있는 모델 성능을 정리했습니다. llama.cpp·mlx_lm 등으로 측정한 결과, 1B–4B급 모델은 메모리 0.6–4.6GB로 'runs great/well', 4–13GB대 모델은 'runs well/ok', 20–26B급(예: gpt-oss-20b, Gemma 26B)은 11–13GB로 간헐적 실행. Qwen 계열과 Liquid AI 모델이 소형 환경에서 특히 우수했습니다.

https://www.whatcani.run/

#macos #m1max #localllms #benchmarks #qwen

whatcani.run

Find the best models and how to run them locally.

whatcani.run

So I have been trying the new #Gemma4 models on my M1 macbook pro, specifically the gemma4:26b which is 17gb in size.

Obviously not the most challenging coding challenge and tasks but...

Much much faster response times than local models 6-12 months ago. Previously qwen, deepseek, and even Gemm3 simply took too long to be practical.

I find it incredible this can run on just my 5.5 year old laptop.

#ai #llm #ollama #localllms #llms

Just so we are clear: #LocalLLMs are an asset if trained and used well. But please be aware that many projects are pretending to be open source but their releases contain closed source components where it's not transparent what is going on.

Go to the source. Llama.cpp, PyTorch, etc.

If you are running #LocalLLMs you may be using LM Studio. Just a fair warning.... While this is practical, it's also proxying everything through their infrastructure. It's a privacy nightmare.

#lmstudio

Could anyone please tell me how to get a local llm set up on my Synology NAS Home Assistant install under Docker? I'd like to point it to a folder on my Linux desktop, and have it process the audio files I make into text. Even better if I could schedule this to happen automatically. Double plus good if it could do different things with my voice files depending on a verbal subject heading in each file. Appreciate it! #homeassistant #privacy #LocalLLMs #synologynas #assistivetechnology #lowvision

Find out which AI models your machine can actually run.

CanIRun.ai — Can your machine run AI models? https://www.canirun.ai/

ht @researchbuzz.bsky.social

#LocalLLMs

CanIRun.ai — Can your machine run AI models?

Detect your hardware and find out which AI models you can run locally. GPU, CPU, and RAM analysis in your browser.

CanIRun.ai

This week on The Servitor:

Getting set up for local roleplay with Silly Tavern! These folks take their roleplay companion AI seriously.

https://theservitor.com/sillytavern-local-llm-setup-guide/

#AI #roleplay #uncensored #SillyTavern #localLLMs

SillyTavern + Local LLM Setup Guide

Step-by-step guide to setting up SillyTavern with KoboldCpp for private, uncensored AI roleplay.

The Servitor