ZML/LLMD alpha – cross platform LLM server

Today we’re releasing ZML/LLMD. It’s a self-contained inference server that runs LLaMa, Gemma, Qwen and Mistral LLMs transparently on 5 architectures: NVIDIA CUDA, AMD ROCm, Google TPU, Intel oneAPI and Apple Metal.

Modern serving features

ZML/LLMD supports modern serving features: continuous batching, paged attention, tensor parallel sharding, prefix caching, tool calling and does so on all platforms.

Metrics are also exposed in the Prometheus format via the /metrics endpoint.

Supported models

ZML/LLMD alpha ships with support for the following models:

• Qwen 2, 3, 3.5 and 3.6 series (dense and MoE)
• Gemma 3 and 4 series (dense and MoE)
• LFM2.5 series
• Mistral 3 and Ministral series
• LLaMa 2 and 3 series

DeepSeek, Kimi, GLM, MiniMax and StepFun models are coming soon.

Native HuggingFace, S3 and GCS support

Built on ZML, ZML/LLMD has native support for ZML’s VFS subsystem, which allows for zero-copy loading of models from HuggingFace (hf://), S3 (s3://), and GCS (gs://). This means that you can load models directly from these sources without having to download them first, saving time and storage space. Authentication is handled with the standard environment or paths for each backend.

For instance, loading from HuggingFace is as simple as using the hf:// prefix on any flag that expects a path:

$ docker run -p 8000:8000 --shm-size=256GB --gpus=all -e HF_TOKEN -it zmlai/llmd:cuda \
    --model=hf://Qwen/Qwen3-8B

Notice we’re also passing the HF_TOKEN environment variable to authenticate.

DFlash speculative decoding

ZML/LLMD alpha ships with native support for DFlash on Gemma 4 series and soon Qwen series models. DFlash is a new speculative decoding algorithm that can speed up tok/s/user by up to 10x on supported models.

To use it, just pass the --dflash-model flag when launching LLMD:

$ docker run -p 8000:8000 --shm-size=256GB --gpus=all -e HF_TOKEN -it zmlai/llmd:cuda \
    --model=hf://google/gemma-4-31B-it \
    --dflash-model=hf://z-lab/gemma-4-31B-it-DFlash

One nice benefit of ZML’s programming model is the compounding of features across platforms. For instance, DFlash transparently runs very well on Intel and Apple GPUs.

Read about DFlash on the Z-Lab blog and the currently released DFlash drafters.

Optimized Docker image

On each platform, ZML/LLMD ships the platform runtime as part of the image in a sandboxed manner. Per file optimization reduces image sizes by 5-10x. On top of that careful ordering of layers and custom compression improves docker pull time by using download/extraction overlapping.

The result is a small image that can be pulled and run in seconds:

$ time docker pull zmlai/llmd:cuda
cuda: Pulling from zmlai/llmd
...
Status: Downloaded newer image for zmlai/llmd:cuda
docker.io/zmlai/llmd:cuda

real    0m13.766s
user    0m0.021s
sys     0m0.022s

Each image ships with everything it needs to run. For instance on Metal the Apple Metal compiler is shipped in the archive, or ROCm itself in the AMD image.

Built-in CUDA Compatibility

On the CUDA platform, ZML/LLMD ships with the CUDA Compatibility layer transparently built-in that enables ZML/LLMD to run on a wide range of driver versions without intervention.

This feature doesn’t require any special configuration as the probe is done automatically at runtime. It is also fully sandboxed and doesn’t require any system libraries to be installed.

Automatic sharding support

ZML/LLMD supports tensor parallel inference on all platforms. It automatically shards the model across multiple devices and handles the communication between them transparently.

Currently only tensor or expert parallel sharding is supported. More control will be added in the near future, for instance pipeline parallelism.

Python-free Execution Path

ZML/LLMD runs on ZML’s own ML framework, ZML, built with Zig, MLIR, OpenXLA. It compiles binaries and runtimes ahead of time into a single, hermetic artifact. Since the ZML programming model is explicit compilation passes, once compiled, there are no hidden compilation happening in the hot path. Latencies are flat and predictable.

Performance

google/gemma-4-26B-A4B-it (bs=16)

google/gemma-4-31B-it + z-lab/gemma-4-31B-it-DFlash (bs=16)

Qwen/Qwen3.6-27B (bs=8)

Fast cold start

ZML/LLMD has been designed from the ground up to have fast cold start times, as shown here with Qwen 3.6-27B BF16:

Give it a try

Run ZML/LLMD and point your favorite harness to it.

NVIDIA

docker run -p 8000:8000 --shm-size=256GB --gpus=all -e HF_TOKEN -it zmlai/llmd:cuda \
    --model=hf://Qwen/Qwen3.6-27B

AMD

docker run -p 8000:8000 --device=/dev/kfd --device=/dev/dri -e HF_TOKEN -it zmlai/llmd:rocm \
    --model=hf://Qwen/Qwen3.6-27B

Intel

docker run -p 8000:8000 --device=/dev/dri -e HF_TOKEN -it zmlai/llmd:oneapi \
    --model=hf://Qwen/Qwen3.6-27B

Google TPU

docker run --net=host --privileged -e HF_TOKEN -it zmlai/llmd:tpu \
    --model=hf://Qwen/Qwen3.6-27B

Apple Metal

brew install zml/zml/llmd
llmd --model=hf://Qwen/Qwen3.6-27B