nano-vllm/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code when working with this repository.

Overview

Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline LLM inference. Supports Qwen3 models with CPU offload for long-context inference.

GPU Mutex for Multi-Instance Debugging

IMPORTANT: When running multiple Claude instances for parallel debugging, different rules apply based on script type:

Benchmarks (bench*.py) - Exclusive GPU Access Required

Before running any bench*.py script, Claude MUST wait for exclusive GPU access:

# Check and wait for GPU to be free
while [ -n "$(nvidia-smi --query-compute-apps=pid --format=csv,noheader)" ]; do
  echo "GPU busy, waiting 10s..."
  sleep 10
done

Other Scripts (tests, examples) - Port Conflict Check Only

For non-benchmark scripts, exclusive GPU access is NOT required. However, check for distributed port conflicts before running:

# Check if port 29500 (default torch distributed port) is in use
if lsof -i :29500 >/dev/null 2>&1; then
  echo "Port 29500 in use, waiting 10s..."
  sleep 10
fi

Note: nanovllm's distributed port handling is not yet robust - two processes competing for the same port will cause errors. This check prevents that issue.

Multi-Instance Development with PYTHONPATH

IMPORTANT: When running multiple Claude instances on different worktrees, do NOT use pip install -e . globally as it will affect other instances.

Use PYTHONPATH directly - no pip install needed:

# Set PYTHONPATH to point to the project root directory
PYTHONPATH=/path/to/your/worktree:$PYTHONPATH python <script.py>

# Example: running tests
PYTHONPATH=/home/zijie/Code/nano-vllm:$PYTHONPATH python tests/test_needle.py

Benefits:

• No pip install required
• Code changes take effect immediately (no reinstall needed)
• Each worktree is completely isolated

Documentation Index

Document	Purpose
docs/architecture_guide.md	Core components, layer-wise CPU offload design, prefill/decode flows, implementation details
docs/sparse_attention_guide.md	Block sparse attention methods (MInference, FlexPrefill, XAttention, Quest), computation flow
docs/layerwise_offload_memory_analysis.md	Memory allocation analysis with theoretical formulas and empirical validation (< 5% error)
docs/debugging_guide.md	PyTorch hooks for debugging, tensor comparison, memory profiling

Configuration

Parameter	Default	Notes
kvcache_block_size	4096	Tokens per block
max_num_batched_tokens	16384	Set = max_model_len for long context
gpu_memory_utilization	0.9	GPU memory fraction
enable_cpu_offload	False	Enable for long context
num_gpu_blocks	2	GPU blocks for offload mode
num_kv_buffers	4	Ring buffer size for decode pipeline

Benchmarking

Files: bench.py (GPU), bench_offload.py (CPU offload), bench_vllm.py (comparison)

Common Issues:

1. max_num_batched_tokens < max_model_len: Set equal for long context
2. CUDA graph dimension mismatch: Ensure input_len + output_len <= max_model_len
3. RoPE out of bounds: Check model's max_position_embeddings in config.json

Model Limits:

• Qwen3-0.6B/4B: 40960 tokens
• Qwen2.5-7B-Instruct-1M: 1048576 tokens

Performance (Qwen3-0.6B):

• GPU: ~18k tok/s (prefill), ~100 tok/s (decode)
• CPU Offload (16K): ~14k tok/s (prefill)
• CPU Offload (32K): ~13k tok/s (prefill)

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Author: Zijie Tian