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About

ZML is a production inference stack, purpose-built to decouple AI workloads from proprietary hardware.

Any model, many hardwares, one codebase, peak performance.

Compiled directly to NVIDIA, AMD, INTEL, TPU, Trainium for peak hardware performance on any accelerator. No rewriting.

It is built using the Zig language, MLIR, and Bazel.

Getting Started

Prerequisites

We use bazel to build ZML and its dependencies. The only prerequisite is bazel, which we recommend installing through bazelisk.

macOS

brew install bazelisk

Linux

curl -L -o /usr/local/bin/bazel 'https://github.com/bazelbuild/bazelisk/releases/download/v1.28.0/bazelisk-linux-amd64'
chmod +x /usr/local/bin/bazel

30-Second Smoke Test

Run the MNIST example:

bazel run //examples/mnist

This downloads a small pretrained MNIST model, compiles it, loads the weights, and classifies a random handwritten digit.

LLM Quickstart

The main LLM example is //examples/llm. It currently supports:

• Llama 3.1 / 3.2
• Qwen 3.5
• LFM 2.5

Authenticate with Hugging Face if you want to load gated repos such as Meta Llama:

bazel run //tools/hf -- auth login

Alternatively, set the HF_TOKEN environment variable.

Then run a prompt directly:

bazel run //examples/llm -- --model=hf://meta-llama/Llama-3.2-1B-Instruct --prompt="What is the capital of France?"

Open the interactive chat loop by omitting --prompt:

bazel run //examples/llm -- --model=hf://meta-llama/Llama-3.2-1B-Instruct

You can also load from:

• a local directory: --model=/var/models/meta-llama/Llama-3.2-1B-Instruct
• S3: --model=s3://bucket/path/to/model

Running Models on GPU / TPU

Append one or more platform flags when compiling or running:

• NVIDIA CUDA: --@zml//platforms:cuda=true
• AMD RoCM: --@zml//platforms:rocm=true
• Intel OneAPI: --@zml//platforms:oneapi=true
• Google TPU: --@zml//platforms:tpu=true
• AWS Trainium / Inferentia 2: --@zml//platforms:neuron=true
• Disable CPU compilation: --@zml//platforms:cpu=false

Example on CUDA:

bazel run //examples/llm --@zml//platforms:cuda=true -- --model=hf://meta-llama/Llama-3.2-1B-Instruct --prompt="Write a haiku about Zig"

Example on ROCm:

bazel run //examples/llm --@zml//platforms:rocm=true -- --model=hf://meta-llama/Llama-3.2-1B-Instruct --prompt="Write a haiku about Zig"

Example on Intel OneAPI:

bazel run //examples/llm --@zml//platforms:cpu=false --@zml//platforms:oneapi=true -- --model=hf://meta-llama/Llama-3.2-1B-Instruct --prompt="Write a haiku about Zig"

Run Tests

bazel test //zml:test

Examples

• examples/llm: unified LLM CLI for Llama, Qwen, and LFM
• examples/mnist: smallest end-to-end model run
• examples/sharding: logical mesh, partitioners, shard-local execution, profiler output
• examples/io: inspect and load local, hf://, https://, and s3:// repositories through the VFS layer
• examples/benchmark: measure loading and execution performance

A Taste Of ZML

const Mnist = struct {
    fc1: Layer,
    fc2: Layer,

    const Layer = struct {
        weight: zml.Tensor,
        bias: zml.Tensor,

        pub fn init(store: zml.io.TensorStore.View) Layer {
            return .{
                .weight = store.createTensor("weight", .{ .d_out, .d }, null),
                .bias = store.createTensor("bias", .{.d_out}, null),
            };
        }

        pub fn forward(self: Layer, input: zml.Tensor) zml.Tensor {
            return self.weight.dot(input, .d).add(self.bias).relu().withTags(.{.d});
        }
    };

    pub fn init(store: zml.io.TensorStore.View) Mnist {
        return .{
            .fc1 = .init(store.withPrefix("fc1")),
            .fc2 = .init(store.withPrefix("fc2")),
        };
    }

    pub fn load(
        self: *const Mnist,
        allocator: std.mem.Allocator,
        io: std.Io,
        platform: *const zml.Platform,
        store: *const zml.io.TensorStore,
        shardings: []const zml.Sharding,
    ) !zml.Bufferized(Mnist) {
        return zml.io.load(Mnist, self, allocator, io, platform, store, .{
            .shardings = shardings,
            .parallelism = 1,
            .dma_chunks = 1,
            .dma_chunk_size = 16 * 1024 * 1024,
        });
    }

    pub fn unloadBuffers(self: *zml.Bufferized(Mnist)) void {
        self.fc1.weight.deinit();
        self.fc1.bias.deinit();
        self.fc2.weight.deinit();
        self.fc2.bias.deinit();
    }

    /// just two linear layers + relu activation
    pub fn forward(self: Mnist, input: zml.Tensor) zml.Tensor {
        var x = input.flatten().convert(.f32).withTags(.{.d});
        const layers: []const Layer = &.{ self.fc1, self.fc2 };
        for (layers) |layer| {
            x = layer.forward(x);
        }
        return x.argMax(0).indices.convert(.u8);
    }
};

For a full walkthrough, see:

• Getting Started
• Writing your first model
• ZML Concepts
• Deploying on a server

Where To Go Next

• Run more examples in ./examples
• Read the example-specific notes in examples/llm/README.md
• Learn tagged dimensions in working_with_tensors.md
• Start building a model with write_first_model.md
• Explore deployment in deploy_on_server.md

Contributing

See here.

License

ZML is licensed under the Apache 2.0 license.

Thanks To Our Contributors