vLLM refuses to start with No available memory for the cache blocks

vLLM aborts during engine initialisation because, after loading model weights and profiling the activation peak, nothing is left inside its memory budget to allocate a single paged KV cache block. The GPU is frequently not full: the free memory sits outside the fraction vLLM was told it may use.

A vLLM startup failure in which the engine, having reserved a fixed fraction of total GPU memory and spent it on model weights and the profiled activation peak, has nothing left to allocate paged KV cache blocks from and exits rather than serving.

vLLM sizes its KV cache from what is left over after weights and activations, inside a budget it fixes up front as gpu_memory_utilization times total device memory. It never grows into memory outside that budget, even when the card is idle. So a card with 20 GiB free and a budget already consumed by weights reports no available memory, and lowering the fraction, which is the instinct an out-of-memory error trains, removes the last of the headroom.

• vLLM exits at startup instead of serving, before any request is accepted
• nvidia-smi shows several gigabytes still free on the card while vLLM reports it has no memory
• Lowering gpu_memory_utilization makes the error appear sooner rather than fixing it
• A second model launched onto a GPU that already hosts one fails while the first succeeded

• vLLM V1 engine (the V0 engine often succeeds at a utilization value where V1 fails)
• Two or more engines co-located on one GPU, where each fraction is taken of TOTAL memory rather than free memory
• Large models on modest cards, where the window between out-of-memory and no-cache-blocks closes entirely
• Deployments using --cpu-offload-gb, which changes the profiling peak

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.

• ✓Read the memory profiling line vLLM logs immediately before the failure and add model weights to the activation peak. If that sum is at or above gpu_memory_utilization multiplied by total memory, the budget is the cause.
• ✓Run nvidia-smi while vLLM is starting and check whether another process holds memory on the same device.
• ✓Re-launch with the utilization raised by 0.05. If the failure becomes CUDA out of memory, the budget was genuinely too small and the model does not fit at this context length.

ERROR 08-14 09:12:44 engine.py:132] ValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.
  File "vllm/v1/core/kv_cache_utils.py", line --, in check_enough_kv_cache_memory
INFO 08-14 09:12:41 worker.py:--] Memory profiling results: total_gpu_memory=79.15GiB model_weights=27.92GiB peak_activation=51.30GiB

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.

Raising the utilization fraction enlarges the budget that the KV cache is carved from, so the leftover becomes positive. Lowering max-model-len or max-num-seqs works from the other side: both shrink the profiled activation peak, which is subtracted from the same budget. Either way the arithmetic that produced zero leftover is what changes.

// Typical pattern:
import torch.optim as optim
optimizer = optim.AdamW(model.parameters(), lr=3e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_training_steps)
for step in range(num_training_steps):
    optimizer.zero_grad()
    loss = model(batch)
    loss.backward()
    optimizer.step()
    scheduler.step()

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.