Promising inference software, written in Zig

ZML released ZML/LLMD, an inference server written in Zig that runs LLaMa, Gemma, Qwen, and Mistral LLMs on five architectures including NVIDIA CUDA, AMD ROCm, Google TPU, Intel oneAPI, and Apple Metal. The server features continuous batching, paged attention, tensor parallel sharding, prefix caching, tool calling, and DFlash speculative decoding for up to 10x speedup, with native HuggingFace, S3, and GCS support.

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: bash $ 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: bash $ 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 https://z-lab.ai/projects/dflash/ and the currently released DFlash drafters https://huggingface.co/collections/z-lab/dflash . 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: bash $ 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 | Platform | Image | Size | |---|---|---| CUDA | zmlai/llmd:cuda | 1.7 GB | ROCm | zmlai/llmd:rocm | 3.9 GB | TPU | zmlai/llmd:tpu | 280 MB | OneAPI | zmlai/llmd:oneapi | 350 MB | Metal | brew install zml/zml/llmd | 140 MB | 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 https://docs.nvidia.com/deploy/cuda-compatibility/latest/why-cuda-compatibility.html 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 | NVIDIA H100x2 | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 127.64 | 70.39 | 162.08 | 141.91 | 142.04 | 142.1 | 143.1 | 158.28 | 161.7 | | ITL ms | 12.03 | 0.01 | 85.58 | 11.67 | 12.28 | 13.14 | 16.35 | 68.24 | 74.42 | | Total tok/s | 1317.93 | | Intel B70x2 | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 426.05 | 117.36 | 1027.4 | 375.38 | 375.4 | 612.75 | 850.48 | 992.01 | 1023.86 | | ITL ms | 81.83 | 0.03 | 499.75 | 81.62 | 82.72 | 83.7 | 84.17 | 84.6 | 279.99 | | Total tok/s | 179.31 | | AMD MI300X | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 101.86 | 36.3 | 145.35 | 109.7 | 109.76 | 110.39 | 116.76 | 139.63 | 144.77 | | ITL ms | 18.52 | 0.02 | 69.33 | 18.4 | 18.82 | 19.12 | 19.26 | 40.41 | 47.75 | | Total tok/s | 858.8 | | M3 Max | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 2364.71 | 1840.79 | 2608.73 | 2411.42 | 2411.5 | 2470.67 | 2539.7 | 2594.92 | 2607.35 | | ITL ms | 67.01 | 25.81 | 196.99 | 67.02 | 70.63 | 74.5 | 77.41 | 85.19 | 104.45 | | Total tok/s | 109.59 | google/gemma-4-31B-it + z-lab/gemma-4-31B-it-DFlash bs=16 | TPU v6ex4 | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 137.64 | 25.36 | 187.67 | 137.79 | 137.82 | 158.75 | 187.17 | 187.57 | 187.66 | | ITL ms | 16.73 | 0.01 | 345.35 | 0.02 | 0.08 | 71.85 | 73.76 | 75.63 | 323.83 | | Total tok/s | 943.7 | | AMD MI300X | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 254.95 | 57.23 | 368.84 | 268.09 | 268.14 | 268.23 | 283.34 | 351.74 | 367.13 | | ITL ms | 21.51 | 0.01 | 140.15 | 0.02 | 0.03 | 91.55 | 93.82 | 109.29 | 139.67 | | Total tok/s | 731.8 | Qwen/Qwen3.6-27B bs=8 | M3 Max | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 6052.4 | 2684.92 | 7285.46 | 6408.14 | 6408.16 | 6671.36 | 6978.41 | 7224.05 | 7279.32 | | ITL ms | 183.96 | 3.33 | 876.88 | 183.83 | 185.05 | 186.34 | 187.62 | 196.88 | 208.71 | | Total tok/s | 33.5 | | AMD MI300X | Avg | Min | Max | P50 | P75 | P90 | P95 | P99 | P99.9 | |---|---|---|---|---|---|---|---|---|---| | TTFT ms | 259.71 | 68.5 | 357.55 | 241.55 | 270.58 | 357.51 | 357.54 | 357.55 | 357.55 | | ITL ms | 30.97 | 0.01 | 115.9 | 31.02 | 31.15 | 31.24 | 31.29 | 31.42 | 34.38 | | Total tok/s | 511.6 | 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