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The pyx GPU indexes consist of curated, pre-built wheels for PyTorch, vLLM, DeepSpeed, Flash Attention, SageAttention, and other hardware-accelerated libraries. pyx includes a dedicated Simple API-compatible index for every supported CUDA version: Once authenticated, you can install, e.g., PyTorch, vLLM, and Flash Attention from the pyx GPU indexes with: 
# Use the CUDA 12.6 index.
uv pip install torch flash-attn vllm --torch-backend cu126

# Infer the latest compatible CUDA version based on your GPU.
uv pip install torch flash-attn vllm --torch-backend auto
Alternatively, you can specify an index URL directly: 
uv pip install torch flash-attn vllm --index https://api.pyx.dev/simple/astral-sh/cu126
Or, configure the index in your pyproject.toml: 
[[tool.uv.index]]
name = "cu126"
url = "https://api.pyx.dev/simple/astral-sh/cu126"
At present, the GPU indexes include builds for the following packages: Packages in the GPU index are typically built for Python 3.9 to Python 3.14, CUDA 12.4 to CUDA 13.0, and both x86_64 and ARM64 architectures (barring exceptions for packages with more limited Python or CUDA support). (Missing something? Let us know.) Every wheel in the index is built against a specific version of PyTorch and CUDA, then modified to make the metadata available to the dependency resolver. As such, installing from, e.g., https://api.pyx.dev/simple/astral-sh/cu126 ensures that uv installs consistent, compatible versions of PyTorch, CUDA, and the other GPU-accelerated dependencies. You can browse the pyx GPU indexes in the pyx dashboard, or by querying the Simple API: 
export PYX_TOKEN=$(uv auth token pyx.dev)
curl https://api.pyx.dev/simple/astral-sh/cu126 --header "Authorization: Bearer $PYX_TOKEN"
You can also visit the Simple API URLs directly in your browser after logging in to the pyx dashboard.