Fix vLLM ValueError: The model's max seq len is larger than the maximum number of tokens in KV cache

Root Cause & Memory Allocation Mechanics

During engine initialization, vLLM loads the model weights, executes a synthetic profiling run to measure peak intermediate activation tensors, captures CUDA Graphs across batch sizes (consuming 0.8 GiB–2.2 GiB of extra workspace), and assigns the remaining budget inside `gpu_memory_utilization` to PagedAttention KV blocks. If you load a 19.2 GB `Qwen2.5-Coder-32B-Instruct-AWQ` checkpoint on a 24GB RTX 4090 at `--gpu-memory-utilization 0.90` (`21.6 GiB` ceiling), only ~1.4 GiB remains for KV blocks—enough for roughly 5,600 FP16 tokens. Because `config.json` specifies `131072`, vLLM refuses to boot unless you clamp `--max-model-len` or reclaim VRAM via FP8 KV-cache and eager execution.

Exact CLI Remediation Commands

# Equivalent context cap in Ollama
export OLLAMA_KV_CACHE_TYPE=q8_0
ollama run qwen2.5-coder:32b

# Equivalent context cap in llama-server
./llama-server -m qwen2.5-coder-32b-q4_k_m.gguf -c 16384 -fa on -ctk q8_0 -ctv q8_0

# Fix 1: Clamp --max-model-len and double KV capacity with FP8
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
  --max-model-len 16384 \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.95 \
  --max-num-seqs 16

# Fix 2: Reclaim ~1.5 GB of CUDA Graph memory on tight 16GB/24GB cards
vllm serve model --max-model-len 8192 --enforce-eager --gpu-memory-utilization 0.95

Frequently Asked Questions