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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.
Miles added ROCm support for AMD Instinct clusters and reported GRPO post-training gains on Qwen3-30B-A3B, including AIME rising from 0.665 to 0.729. It matters if you are evaluating rollout-heavy RL jobs off NVIDIA and want concrete throughput and step-time numbers before porting.
A pure C and Metal engine streams 209GB of MoE weights from SSD and reports tool-calling support in 4-bit mode on a laptop-class Mac. It is a concrete benchmark for teams exploring expert streaming, quantization, and page-cache tricks on consumer hardware.
OpenAI says Responses API requests can reuse warm containers for skills, shell, and code interpreter, cutting startup times by about 10x. Faster execution matters more now that Codex is spreading to free users, students, and subagent-heavy workflows.
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.
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