HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd

HIP runtime returned Out of Memory error. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent non-nvidia failures.

Symptom

HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd

Root cause

HIP runtime returned Out of Memory error. The decisive evidence is the first log line that precedes "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd" and differs from a healthy run. A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

Recommended fix

Clear PyTorch cache using torch.cuda.empty_cache().

How Denpex helps

Denpex investigates HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd 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 "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd". It is a hardware failure associated with AMD ROCm, RCCL, Intel Gaudi, and HCCL. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

HIP runtime returned Out of Memory error. 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 "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd".
• 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.

• AMD ROCm, RCCL, Intel Gaudi, and HCCL
• 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 "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓Compare the failing rank, node, input, or configuration with one known-good control.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from latest checkpoint only after the control passes.

• HIP runtime returned Out of Memory error.
• The decisive evidence is the first log line that precedes "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd

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.

Clear PyTorch cache using torch.cuda.empty_cache(). This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from latest checkpoint.

# 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 -- "HIP error: hipErrorOutOfMemory Out of device memory AMD Instinct /dev/kfd" <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.