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Nightly (v26.4)

This version is still a work in progress.

MAX models​

  • Added MXFP4 quantization support for MiniMax-M2.

MAX framework​

Inference server​

  • MAX Serve now emits the maxserve.num_requests_queued OTel/Prometheus metric (changed from an UpDownCounter to a synchronous Gauge). The gauge is sampled once per scheduler iteration from BatchMetrics.publish_metrics and reports the depth of the scheduler's CE / prefill queue (the same value as the Pending: N reqs line in scheduler logs). It is published by every text-path scheduler that drives BatchMetrics: TokenGenerationScheduler and PrefillScheduler (via TextBatchConstructor), and DecodeScheduler (via len(pending_reqs) + len(prefill_reqs)). Operators can use this metric to observe queue buildup during overload conditions.

max CLI​

  • Added --devices=gpu:all to use every visible GPU (including MAX Serve).

Python API​

  • CPUMetricsCollector in max.diagnostics.cpu is now used as a context manager instead of start/stop and now exposes get_stats() instead of dump_stats(), matching the interface of GPUDiagContext.

Breaking changes​

  • max/python/max/benchmark/benchmark_throughput.py, deprecated in v0.26.3, has been removed.

Fixes​

  • MODULAR_DEBUG=ir-output-dir=<dir> (and the equivalent [max-debug] ir-output-dir = <dir> config-file entry and InferenceSession.debug.ir_output_dir = <dir> Python setter) now actually dumps per-stage MLIR files to the configured directory. The option was previously parsed but no compiler stage consulted it, so users had to fall back to the legacy MODULAR_MAX_TEMPS_DIR env var. Both spellings are now honored.

Mojo language​

For all the updates to the Mojo language, standard library, and tools, see the Mojo release notes.

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