OPEN LINK ↗
// 1h agoBENCHMARK RESULT
moe-cache v0.3.5 speeds long prompts
moe-cache v0.3.5 delivers roughly 1.4× faster 34k–128k prompt processing on a tested Windows desktop, while previewed v0.3.6 work targets its 6–10% long-context decode regression. The open-source llama.cpp plugin helps consumer hardware run MoE models larger than available RAM. [Release notes](https://github.com/IbrahimSiddiqui007/moe-cache/releases)
// ANALYSIS
This is a meaningful local-inference optimization, but the headline gain is concentrated in prompt ingestion rather than sustained generation.
- –Paired Qwen3.8-Flash-Next tests show prompt-speed gains of 42% at 34k tokens and 38% at 64k–128k.
- –The trade-off is 6–10% slower decoding after long prompts because larger prompt chunks consume GPU memory.
- –Results also include Qwen3.5-122B and GLM-5.3-Flash, but those comparisons were not paired against v0.3.4 and remain less conclusive.
- –Bundled llama.cpp builds, easier setup, and connectors for Claude Code, OpenCode, and Hermes make the project substantially more practical for local coding agents.
- –Default 128k-plus contexts use q8_0 KV cache, so users needing byte-level parity should enable exact f16 mode.
// TAGS
moeinferencelong-contextbenchmarkopen-sourcelocal-firstcoding-agentmoe-cache
DISCOVERED
1h ago
2026-11-04
PUBLISHED
2h ago
2026-11-04
RELEVANCE
9/ 10
AUTHOR
Ibrummmn