llama.cpp adds TENSOR_READ_LAZY, MoE expert tensors no longer need to sit in VRAM
jacek2023 · reddit · 2026-08-27
A new llama.cpp PR #27794 by ngxson introduces a TENSORREADLAZY option in the model loader. The key benefit: for MoE models like Qwen3 Next, expert tensors (engrams) that are only needed when activated no longer have to stay resident in VRAM/RAM — they're loaded lazily on demand, substantially lowering the memory bar for running large sparse models locally.
More from coding & agent
- Musk amplifies dev's verdict: Grok Bot easier to use than Codex and Claude — elonmusk · 2026-10-15
- Blob0 launches: a macOS file-manager-style desktop client for S3 and Cloudflare R2 — yihui_indie · 2026-10-15
- The 6-Layer AI Engineer Skill Stack for 2026: Vectors to Multi-Agent Ops — mdancho84 · 2026-10-15
- Building a Mechanical Engineering Agent Workflow in ChatGPT: 30-Node Canvas Shows Promise — burhop · 2026-10-15
- Open-source Harness Engineering course hits 19k stars, shows $9 vs $200 gap between failing and working agent setups — thisguyknowsai · 2026-10-15
- A 6-person team shipped healthcare prior-auth AI agents in 3 months — here's the playbook — alex_verem · 2026-10-15