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wire 2026-08-26
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$ the-wire · showcase

OLLAMA'S MLX RUNNER NOW SPEAKS STRUCTURED OUTPUT

By RepoJournal · Filed · About Local LLMs · Composed from the cited sources · methodology

Jesse Gross Kyozzz github-actions[bot] JonathanC-ARM njhill shikicloud hoyyeva
18 people shipped this
Jesse Gross @jessegross 2 cited Kyozzz @kyo-zzz 1 cited github-actions[bot] @github-actions[bot] 1 cited JonathanC-ARM @JonathanC-ARM 1 cited njhill @njhill 1 cited shikicloud @shikicloud 1 cited hoyyeva @hoyyeva pd95 @pd95 Ryan C @ryan5rdx allozaur @allozaur yewentao256 @yewentao256 mgoin @mgoin gau-nernst @gau-nernst JiataiWang @JiataiWang michaelzhang-ai @michaelzhang-ai ch-wan @ch-wan KnightYao @KnightYao cctry @cctry

Ollama closed a silent correctness gap on Apple Silicon, and the LLM inference stack shipped a wave of fixes that touch everything from tool calls to native ARM support.

Ollama's MLX runner finally enforces the `format` field, using xgrammar to mask logits so every generated token satisfies the requested JSON or JSON Schema constraint [1]. It's the fix that makes structured output trustworthy on Apple Silicon, and it lands alongside a cleanup that removes the dead Grammar completion-request field, a leftover from the CGO engine removal [2]. Meanwhile, llama.cpp closed the hole where tool calls vanished when `--prefill-assistant` was active, forcing auto-continuation to skip trailing assistant messages with tool_calls and throwing a clear error instead [3]. And SGLang removed the fusion barrier on DeepSeek-V4, routing the shared expert through the same MoE kernel to cut kernel launches and stream syncs per layer [4]. For infrastructure teams, vLLM's cudagraph memory profiling fix prevents a nasty allocator assert by running profiling in a throwaway graph pool [5], and llama.cpp v0.3.0 shipped with the KleidiAI build rework that lifts manual kernel-source and `-march` requirements [6][7].

Action items

References

  1. [1] mlxrunner: add structured output support ↗ ollama/ollama
  2. [2] llm: remove the unused Grammar completion-request field ↗ ollama/ollama
  3. [3] server : reject prefilled assistant messages with tool calls (#27626) ↗ ggml-org/llama.cpp
  4. [4] [Deepseek-V4] Enable shared-experts fusion on the flashinfer_mxfp4 (trtllm-gen) MoE path ↗ sgl-project/sglang
  5. [5] [Bugfix][MRV2] Run cudagraph memory profiling in a throwaway graph pool ↗ vllm-project/vllm
  6. [6] v0.3.0 ↗ ggml-org/llama.cpp
  7. [7] kleidiai: Rework KleidiAI Build System/Integration ↗ ggml-org/llama.cpp

$ ls local-llm/reviews/ # the step-back reads

the week in review vLLM v0.32.0 lands systemone, security fixes behind it the month in review vLLM 0.30.0 lands and nineteen engine-fatal flaws close
Full archive → Weekly reviews →

Quick answers

What shipped in Local LLMs on August 26, 2026?
Ollama closed a silent correctness gap on Apple Silicon, and the LLM inference stack shipped a wave of fixes that touch everything from tool calls to native ARM support. In total, 116 commits, 113 pull requests, and 10 releases landed.
Who contributed to Local LLMs on August 26, 2026?
18 developers shipped this update, including Jesse Gross, hoyyeva, pd95, Kyozzz, Ryan C, github-actions[bot], allozaur, and JonathanC-ARM, and 10 more.
What were the notable Local LLMs updates?
mlxrunner: add structured output support, llm: remove the unused Grammar completion-request field, and server : reject prefilled assistant messages with tool calls (#27626).
♥

$ log Local LLMs --recent

Ollama preps Phonon voice input, vLLM drops deprecated IR knob 269 shipped Ollama fixes cloud metadata for every launcher, MLX speeds 6-16 row decode ~3x 298 shipped Ollama streams real errors, llama.cpp forwards sampling params on /v1/messages 274 shipped Five vLLM crash and forgery paths, realtime transcription lands in llama.cpp 295 shipped

$ log --all # elsewhere on the wire

Alignment & Post-training MILES ships a 25-benchmark eval panel and simpler config presets Interpretability J-space direction loss and Jacobian lens joint ablation fixed in v4.3.0

$ ls showcases/

Safety Evals AI Red Teaming Agent Security Interpretability Alignment & Post-training

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