CUDA error: an illegal memory access was encountered paged_attention

A vLLM paged-attention kernel accessed an invalid GPU address. The immediate owner can be corrupted block-table metadata, an unsupported shape or dtype, or a binary compatibility defect in the selected attention backend. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent inference failures.

Symptom

CUDA error: an illegal memory access was encountered paged_attention

Root cause

A vLLM paged-attention kernel accessed an invalid GPU address. The immediate owner can be corrupted block-table metadata, an unsupported shape or dtype, or a binary compatibility defect in the selected attention backend. The decisive evidence is the first log line that precedes "CUDA error: an illegal memory access was encountered paged_attention" and differs from a healthy run.

Recommended fix

CUDA_LAUNCH_BLOCKING=1.

How Denpex helps

Denpex investigates CUDA error: an illegal memory access was encountered paged_attention 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 "CUDA error: an illegal memory access was encountered paged_attention". It is a infrastructure failure associated with vLLM, Triton, and TensorRT-LLM servers. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

A vLLM paged-attention kernel accessed an invalid GPU address. The immediate owner can be corrupted block-table metadata, an unsupported shape or dtype, or a binary compatibility defect in the selected attention backend. 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 "CUDA error: an illegal memory access was encountered paged_attention".
• 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.

• vLLM, Triton, and TensorRT-LLM servers
• 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 "CUDA error: an illegal memory access was encountered paged_attention" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓test the same model and request with the fallback attention backend, then verify vLLM, PyTorch, CUDA, and kernel binary compatibility for the GPU architecture.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from request retry in a fresh worker after the attention control passes only after the control passes.

• A vLLM paged-attention kernel accessed an invalid GPU address. The immediate owner can be corrupted block-table metadata, an unsupported shape or dtype, or a binary compatibility defect in the selected attention backend.
• The decisive evidence is the first log line that precedes "CUDA error: an illegal memory access was encountered paged_attention" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

CUDA error: an illegal memory access was encountered paged_attention

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.

restart the worker to clear the invalid CUDA context and reproduce one request with CUDA_LAUNCH_BLOCKING=1. Preserve the first failing request shape and block-table state. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from request retry in a fresh worker after the attention control passes.

# 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 -- "CUDA error: an illegal memory access was encountered paged_attention" <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.

vLLM failures cross request, model, KV cache, worker, kernel and host boundaries. The hub compares common literal signatures and the first control that separates their causal owners.