Pick an image for your GPU

Miles runs inside a container image that holds Miles, a patched SGLang, the trainer libraries, and prebuilt GPU kernels. The kernels are the part that ties an image to a GPU.

When you finish this section, you can choose a Miles image for your GPU and record exactly which one a run used.

Start from the official image

The Miles team publishes radixark/miles with a tag per release and a latest tag. Each image is built for CUDA 13, for both linux/amd64 and linux/arm64.

At release v0.1.2, the image builds on SGLang v0.5.21 with the Miles patches on top. So the SGLang inside the image is not a stock SGLang release. Read its version from the image, not from the SGLang project.

Check the kernels against your GPU

The image holds FlashAttention 2 and FlashAttention 3. The FlashAttention 3 build targets Hopper only. On any other GPU, a setting that needs FlashAttention 3 fails at start, as section 4.6 shows.

The Miles documentation lists NVIDIA GPUs from A100 to GB300, and AMD MI300X to MI355X. Our GB10 is not on that list. The arm64 image still ran on our GB10, with the FSDP trainer and the default FlashInfer attention.

Record what each run used

latest moves. Two runs a week apart can use different code under the same tag. Record three facts at the start of every run: the image digest, the Miles commit, and the SGLang version. Section 1.1 gives the three commands and our output.

Our latest image held Miles from 2026-10-15 and a development build of SGLang 0.5.22, newer than the v0.1.2 release.

To repeat a run, pull the image by its digest, not by its tag:

docker pull radixark/miles@sha256:c64fa344e3a86715f03c0a1b6a422c474e0035355591a2f6da87bf52a74c52da