Version pins · AI Engineering

This page lists the versions of vLLM, PyTorch, CUDA, and the images that our runs used.

Use this page to match your setup to ours before you compare a number.

vLLM

Item	Value
Release	0.31.0
Tag commit	`db9527a46873454610df6dbedf79a36d6bf1a7f6`, dated 2026-10-02
GitHub release	published 2026-10-05
PyTorch that the CUDA wheel requires	2.13.0
CUDA of the default image	13.0
Python of the default image	3.12

The machines

Item	NVIDIA L4	DGX Spark
GPU	NVIDIA L4, compute capability 8.9	NVIDIA GB10, compute capability 12.1
Memory	24 GB GDDR6, 22.1 GiB visible	unified LPDDR5x sold as 128 GB, 121.69 GiB visible to the GPU
CPU architecture	x86_64	aarch64
Driver	615.71.09	580.159.03
How vLLM ran	the pip wheel in a virtual environment	the image `vllm/vllm-openai:v0.31.0`
Attention backend	FlashAttention 2, by the selection rule for this GPU	FlashAttention 2, named in the startup log as `FLASH_ATTN`

Models

Model	Machine	Use
`Qwen/Qwen3-8B`	L4	every L4 run except the FP8 weight run
`Qwen/Qwen3-8B-FP8`	L4	the FP8 weight run
`Tengyunw/qwen3_8b_eagle3`	L4	the EAGLE3 draft
`Qwen/Qwen3-30B-A3B-Instruct-2507`	DGX Spark	all GB10 runs

The comparison engine and the client

Item	Value
SGLang	0.5.22, for the head-to-head runs
Benchmark client	`sglang.benchmark.serving` from SGLang 0.5.22, with `--backend vllm` for the vLLM runs
PyTorch of the client environment	2.14.1 with CUDA 13.0

Sources: the vLLM source at v0.31.0 (requirements/cuda.txt, docker/Dockerfile), the GitHub release list of vllm-project/vllm (fetched 2026-10-21), capture/gpu/summary/l4.json and gb10.json (environment), capture/gpu/out-gb10/server-load-vllm.log.