llama.cpp Memory Inefficiency with Qwen Context? User Reports
nullc · reddit · 2026-08-14
A user finds llama.cpp is significantly less memory-efficient for Qwen architecture compared to muse glimmer on the same hardware: glimmer supports 24x128k contexts, while Qwen only 3x256k or 6x128k. Despite architectural analysis suggesting Qwen's per-token state is smaller, actual performance is worse, indicating a potential memory inefficiency in llama.cpp for Qwen, impacting batched performance.
More from Infra
- What's the Easiest Way to Host Inference for a Fine-Tuned >1T Open-Weight Model? — maksym_andr · 2026-08-14
- CoreWeave's Losses Double, Cash Burn Soars, but Investors Eye $103.7B Backlog — rohanpaul_ai · 2026-08-14
- What is Prefill-Decode Disaggregation and Why Are Modern Inference Stacks Moving to It? — scareme_please · 2026-08-14
- Namecheap Data Center Cooling Failure Causes Outage, Services Gradually Restoring — evilsocket · 2026-08-14
- OpenAI Releases GPT-5.6 Builder Guide: Slash Agent Bills from $33 to $1.33 — xiaohu · 2026-08-14
- Google Releases Free Masterclass on GPUs — mdancho84 · 2026-08-14