Cache hit rate, offloading, routing, and cache-aware systems.
New discussion around TurboQuant focuses on its 2.5-bit mixed-precision setup and working PyTorch and llama.cpp implementations. The technique is moving from a research claim into deployable KV-cache compression with concrete porting details.
Google Research said TurboQuant can shrink KV cache storage to 3 bits with roughly 6x less memory, and early implementations already surfaced in PyTorch, llama.cpp, and Atomic Chat. The work targets a core inference bottleneck for long-context serving on local and server hardware.
TurboQuant claims 6x KV-cache memory reduction and up to 8x faster attention on H100s without retraining or quality loss on long-context tasks. If those results hold in serving stacks, teams should revisit long-context cost, capacity, and vector-search design.
Flash-MoE now shows SSD-streamed expert weights pushing a 397B Qwen3.5 variant onto an iPhone at 0.6 tokens per second, extending its earlier laptop demos. Treat it as a memory-tiering prototype rather than a deployable mobile serving target, because speed, heat, and context headroom remain tight.
H Company launched Holotron-12B, an open multimodal model for computer-use agents built on a hybrid SSM-attention stack that targets KV-cache bottlenecks. Benchmark it if you need high-concurrency browser agents and want better throughput without giving up web-task accuracy.
oMLX now supports local Claude Code setups on Apple Silicon with tiered KV cache and an Anthropic Messages API-compatible endpoint, with one setup reporting roughly 10x faster performance than mlx_lm-style serving. If you want private on-device coding agents, point Claude Code at a local compatible endpoint and disable the attribution header to preserve cache reuse.
