TensorRT-LLM KVCacheManager Available blocks exhausted

TensorRT-LLM ran out of paged KV-cache blocks. The block pool is fixed at engine build/startup, so concurrent sequences × their lengths exceeded what was reserved, commonly triggered by beam search or speculative decoding, which multiply the blocks a single request consumes. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent inference failures.

The literal signature is "TensorRT-LLM KVCacheManager Available blocks exhausted". 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.

TensorRT-LLM ran out of paged KV-cache blocks. The block pool is fixed at engine build/startup, so concurrent sequences × their lengths exceeded what was reserved, commonly triggered by beam search or speculative decoding, which multiply the blocks a single request consumes. 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 "TensorRT-LLM KVCacheManager Available blocks exhausted".
• 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

Apply the following checklist to a small reproduction: each box below is a positive signal that you are looking at this exact failure rather than a sibling in the same taxonomy.

• ✓Find the first occurrence of "TensorRT-LLM KVCacheManager Available blocks exhausted" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓compute the requirement, blocks ≈ (max_batch_size × max_seq_len) / tokens_per_block, multiplied by beam width. Compare with kv_cache_free_gpu_mem_fraction. If free memory is available, raise the fraction; if not, the model plus cache genuinely does not fit.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from no checkpoint action; reduce concurrency and retry only after the control passes.

TensorRT-LLM KVCacheManager Available blocks exhausted

Timestamps and exact values vary across runs, but the pattern. An info-level start, an early WARN, an ERROR carrying the symptom. Is the actual fingerprint you should alert on. The Denpex platform flags this combination automatically.

reduce concurrency or the per-request block cost. Beam width and speculative draft length both multiply KV usage; halving either frees capacity immediately. 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 concurrency and retry.

# 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 -- "TensorRT-LLM KVCacheManager Available blocks exhausted" <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.

• TensorRT-LLM ran out of paged KV-cache blocks. The block pool is fixed at engine build/startup, so concurrent sequences × their lengths exceeded what was reserved, commonly triggered by beam search or speculative decoding, which multiply the blocks a single request consumes.
• The decisive evidence is the first log line that precedes "TensorRT-LLM KVCacheManager Available blocks exhausted" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.