NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded

The GPU's hardware video decoder ran out of concurrent sessions. NVDEC session count is a fixed hardware/driver limit, and consumer boards are capped far below data-center parts regardless of available memory. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent cuda-graphs failures.

Symptom

NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded

Root cause

The GPU's hardware video decoder ran out of concurrent sessions. NVDEC session count is a fixed hardware/driver limit, and consumer boards are capped far below data-center parts regardless of available memory. The decisive evidence is the first log line that precedes "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded" and differs from a healthy run.

Recommended fix

cap concurrent decoders in the dataloader to below the board's limit, or reuse a decoder pool instead of creating one per worker.

How Denpex helps

Denpex investigates NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded 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 "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded". It is a environment failure associated with CUDA Graphs, custom kernels, and vision pipelines. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The GPU's hardware video decoder ran out of concurrent sessions. NVDEC session count is a fixed hardware/driver limit, and consumer boards are capped far below data-center parts regardless of available memory. 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 "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded".
• 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.

• CUDA Graphs, custom kernels, and vision pipelines
• 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 "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓count how many decode contexts are actually open, it is usually num_workers × videos-in-flight, which multiplies faster than people expect. GeForce boards are limited well below A100/L40S class.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from no checkpoint action; reduce decoder concurrency and restart only after the control passes.

• The GPU's hardware video decoder ran out of concurrent sessions. NVDEC session count is a fixed hardware/driver limit, and consumer boards are capped far below data-center parts regardless of available memory.
• The decisive evidence is the first log line that precedes "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded

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.

cap concurrent decoders in the dataloader to below the board's limit, or reuse a decoder pool instead of creating one per worker. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from no checkpoint action; reduce decoder concurrency and restart.

# 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 -- "NVDEC hardware decoder stream allocation failed maximum concurrent decode sessions exceeded" <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.