AI Briefing#vram
The latest AI and developer news about #vram, with the original source and a short summary.
The latest AI and developer news about #vram, with the original source and a short summary.
The free Windows tool uses a shared queue to stop CUDA OOM crashes when multiple apps access the GPU simultaneously.
Ollama's default settings for context length, parallel requests, and resident model limits significantly increase memory usage, causing GPU offloading failures.
A new optimizer called SkewAdam reduces optimizer state memory for MoE training from 50.6GB to 1.29GB, enabling 6.78B MoE training on a single 40GB GPU.
Intel has unveiled 'Crescent Island,' a next-generation GPU for AI workloads equipped with up to 480GB of VRAM.
OWC has announced a Thunderbolt 5 device that enables local execution of large AI models on Windows and Linux PCs.
An analysis of Hugging Face user data found that AI builders tend to prioritize VRAM capacity over compute performance.
AMD's next-generation APU, the Ryzen AI Max+ 495, supports up to 192GB of large-capacity unified memory.
Ran Qwen3.6-35B-A3B at 23 t/s on a 5-year-old laptop with 6GB VRAM.
A VRAM estimation tool that runs llama.cpp's fit algorithm in the browser has been released.
Qwen3.6-27B was run up to a 100K context on an A5000 16GB using IQ4_XS.
Even with 32GB VRAM, llama.cpp's `--fit` ran Qwen3.6 Q8 at a 256k context at 57 t/s.
Shared a setup for running Qwen3.6-35B-A3B with llama-server on an RTX 4060 8GB VRAM, along with a thinking budget pitfall.
A 9GB VRAM variant has been mentioned for NVIDIA's RTX 5060 and 5060 Ti.
A dynamic expert cache in llama.cpp boosted Qwen3.5-122B-A10B token generation speed by 27%.
For 96GB VRAM setups, Qwen 3.5 122B and 27B have emerged as the practical choices.
The terminal is fast, but a local LLM trap emerged where hooking it up to an app took as long as 13 minutes.