bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device

The installed bitsandbytes CUDA binary does not contain a 4-bit kernel for the GPU compute capability in use. This is an architecture or binary compatibility failure, not a model-memory shortage. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent quantization failures.

Symptom

bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device

Root cause

The installed bitsandbytes CUDA binary does not contain a 4-bit kernel for the GPU compute capability in use. This is an architecture or binary compatibility failure, not a model-memory shortage. The decisive evidence is the first log line that precedes "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device" and differs from a healthy run.

Recommended fix

record the GPU compute capability, bitsandbytes version, PyTorch CUDA version, and loaded library path. Install a build that explicitly supports that architecture.

How Denpex helps

Denpex investigates bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

The literal signature is "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device". It is a environment failure associated with AWQ, GPTQ, bitsandbytes, GGUF, and torchao. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The installed bitsandbytes CUDA binary does not contain a 4-bit kernel for the GPU compute capability in use. This is an architecture or binary compatibility failure, not a model-memory shortage. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• AWQ, GPTQ, bitsandbytes, GGUF, and torchao
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

• ✓Find the first occurrence of "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓run the bitsandbytes diagnostic and confirm the process is not loading an older libbitsandbytes CUDA library from another environment.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from job start with a compatible bitsandbytes binary only after the control passes.

• The installed bitsandbytes CUDA binary does not contain a 4-bit kernel for the GPU compute capability in use. This is an architecture or binary compatibility failure, not a model-memory shortage.
• The decisive evidence is the first log line that precedes "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

record the GPU compute capability, bitsandbytes version, PyTorch CUDA version, and loaded library path. Install a build that explicitly supports that architecture. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from job start with a compatible bitsandbytes binary.

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "bitsandbytes matmul_4bit CUDA error: no kernel image is available for execution on the device" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.