CUDA & PyTorch Compatibility

Pick a CUDA build that matches the card you rented — the one setting that breaks more Clore.ai deployments than anything else

If you rent an RTX 50-series card and run a CUDA 12.4 image, nothing will work. You will get no kernel image is available for execution on the device. Blackwell needs CUDA 12.8 or newer and PyTorch 2.7 or newer. This is the single most common failed deployment on Clore.ai, and it is a one-line fix.

Every guide in this collection ships a base image. If you change it, or if you install PyTorch yourself, the CUDA build has to match the architecture of the GPU you rented. Clore.ai is a marketplace, not a uniform cluster: the fleet spans Pascal mining cards from 2017 to Blackwell workstation cards from 2025, and no single image covers all of them.

The matrix

Architecture	Compute cap.	Cards on Clore.ai	Use this
Blackwell	sm_120	RTX 5090, 5080, 5070 Ti, 5070, 5060 Ti, RTX PRO 6000	PyTorch ≥ 2.7 on cu128 or cu13x
Ada	sm_89	RTX 4090, 4080, 4070 Ti, 4060 Ti	PyTorch ≥ 2.1 on cu121 … cu13x (cu128 recommended)
Ampere	sm_86 / sm_80	RTX 3090, 3080, 3070, 3060, A6000, A2000, CMP 90HX	PyTorch ≥ 1.13 on cu118 … cu13x (cu128 recommended)
Turing	sm_75	RTX 2080 Ti, GTX 1660, CMP 30HX/40HX/50HX	Anything cu118 … cu13x
Volta	sm_70	Tesla V100 (16GB and 32GB SXM2)	PyTorch ≤ 2.10 on cu128, or any cu126 build
Pascal	sm_61	P104-100 and similar mining cards	PyTorch ≤ 2.7 on cu124 or older

Two deprecations catch people out:

• cu128 builds from PyTorch 2.8 dropped Maxwell and Pascal (sm_50, sm_60).

• PyTorch 2.11 dropped Volta from its cu128 and cu129 binaries (cuDNN 9.15.1 is not Volta-compatible). V100 renters should pin 2.10.0-cuda12.8 or use a cu126 build.

• CUDA 13.0 itself starts at Turing (sm_75). Any cuda13x image excludes Volta and Pascal by construction.

Base images that work

# Default for anything from Ampere to Blackwell — this is what the guides use
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel     # build tools, compilers, nvcc
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime   # smaller, no nvcc

# Newest, Turing and up only (no V100, no Pascal)
pytorch/pytorch:2.13.0-cuda13.2-cudnn9-devel

# Tesla V100 (sm_70)
pytorch/pytorch:2.10.0-cuda12.8-cudnn9-devel

# Pascal mining cards (sm_61)
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel

Installing PyTorch into your own image:

# Ampere / Ada / Blackwell
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# Volta (V100)
pip install "torch<=2.10" torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

Check what you actually rented

# Driver and the CUDA version it exposes
nvidia-smi

# Compute capability of the card, straight from torch
python -c "import torch; print(torch.cuda.get_device_name(0), torch.cuda.get_device_capability(0))"
# RTX 5090  -> (12, 0)   RTX 4090 -> (8, 9)   RTX 3090 -> (8, 6)   V100 -> (7, 0)

# Which architectures this build actually has kernels for
python -c "import torch; print(torch.cuda.get_arch_list())"

If get_device_capability returns (12, 0) and get_arch_list() has no sm_120, you are on the wrong image. Nothing else you change will help.

Filter before you rent. The marketplace exposes each host's CUDA version. About 65% of Clore.ai GPUs currently sit on a 13.x driver and 35% on 12.x, so a cu128 image is the safest default: CUDA minor-version compatibility lets it run on both.

Error messages, decoded

What you see	What it means	Fix
no kernel image is available for execution on the device	The build has no kernels for your card's architecture	Match the image to the matrix above
CUDA error: no kernel image ... sm_120	Blackwell card, pre-2.7 or cu124 build	Move to cu128 + PyTorch ≥ 2.7
CUDA driver version is insufficient for CUDA runtime version	Image needs a newer driver than the host has	Pick a lower CUDA image, or rent a host on a newer driver
torch.cuda.is_available() == False	Container started without GPU access	Redeploy; make sure the order actually attached a GPU
undefined symbol: __cudaPopCallConfiguration	Mixed CUDA versions in one environment	Rebuild from one clean base image; do not pip install a second torch
Works on the 4090, fails on the 5090	Classic architecture gap	The image is Ada-era; move to cu128

Flash Attention and friends

Compiled extensions carry the same constraint as PyTorch, and usually lag a release behind:

# Build against the torch you already have, not a fresh one
pip install flash-attn --no-build-isolation

# vLLM and SGLang ship their own CUDA builds — use their images rather than
# installing them into a PyTorch image, or you will end up with two torches
docker pull vllm/vllm-openai:latest
docker pull lmsysorg/sglang:latest

vllm/vllm-openai:latest and lmsysorg/sglang:latest both carry Blackwell kernels. If you pin an older tag for reproducibility, check that it postdates the RTX 50-series, otherwise you inherit exactly the problem this page is about.

Next Steps

• Docker Images Catalog — ready-to-deploy images per workload

• GPU Comparison — which card for which model

• GPU Pricing — live marketplace ranges

• Troubleshooting — the rest of the failure modes

Links

• PyTorch previous versions and CUDA builds

• pytorch/pytorch on Docker Hub

• CUDA GPU compute capabilities

• Rent a GPU: RTX 5090 32GB · RTX 4090 24GB · Marketplace