#6363rd of 73 in Model serving

zml

Zig and MLIR inference stack compiling models for GPUs, TPUs and Trainium

Stars
4.2k
License
Apache-2.0
Last commit
Nov 2026
Last release
Apr 2026
Language
Zig

Missed the latest GitHub check; these facts may be older.

Overview

ZML is an inference stack written in Zig on top of MLIR and built with Bazel. Models are written in Zig with tagged tensors and compiled for CUDA, ROCm, Intel OneAPI, Google TPU and AWS Trainium/Inferentia 2 from one codebase. The bundled LLM example runs Llama 3.1/3.2, Qwen 3.5 and LFM 2.5 from Hugging Face, local directories or S3.

Who it is for: Engineers deploying inference across NVIDIA, AMD, TPU and Trainium hardware

Strengths

  • One Zig codebase targets CUDA, ROCm, OneAPI, TPU and Trainium via build flags
  • Loads weights from hf://, local paths, https:// and s3:// through a VFS layer
  • Bazel is the only prerequisite; examples run with a single bazel run command
  • Includes sharding, profiler and benchmark examples for multi-device use

Weaknesses

  • LLM example supports only Llama 3.1/3.2, Qwen 3.5 and LFM 2.5
  • Models must be written in Zig; much smaller ecosystem than PyTorch
  • Examples are CLIs; README shows no OpenAI-compatible HTTP server
  • Bazel build adds setup and build-time overhead for casual use

What it needs

  • GPU optional
  • Needs Bazel (via bazelisk), Hugging Face token for gated models
  • Models: Llama 3.1, Llama 3.2, Qwen 3.5, LFM 2.5, MNIST

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