| # Performance Optimization Guide |
|
|
| Maximize llama.cpp inference speed and efficiency. |
|
|
| ## CPU Optimization |
|
|
| ### Thread tuning |
| ```bash |
| # Set threads (default: physical cores) |
| ./llama-cli -m model.gguf -t 8 |
| |
| # For AMD Ryzen 9 7950X (16 cores, 32 threads) |
| -t 16 # Best: physical cores |
| |
| # Avoid hyperthreading (slower for matrix ops) |
| ``` |
|
|
| ### BLAS acceleration |
| ```bash |
| # OpenBLAS (faster matrix ops) |
| make LLAMA_OPENBLAS=1 |
| |
| # BLAS gives 2-3× speedup |
| ``` |
|
|
| ## GPU Offloading |
|
|
| ### Layer offloading |
| ```bash |
| # Offload 35 layers to GPU (hybrid mode) |
| ./llama-cli -m model.gguf -ngl 35 |
| |
| # Offload all layers |
| ./llama-cli -m model.gguf -ngl 999 |
| |
| # Find optimal value: |
| # Start with -ngl 999 |
| # If OOM, reduce by 5 until fits |
| ``` |
|
|
| ### Memory usage |
| ```bash |
| # Check VRAM usage |
| nvidia-smi dmon |
| |
| # Reduce context if needed |
| ./llama-cli -m model.gguf -c 2048 # 2K context instead of 4K |
| ``` |
|
|
| ## Batch Processing |
|
|
| ```bash |
| # Increase batch size for throughput |
| ./llama-cli -m model.gguf -b 512 # Default: 512 |
| |
| # Physical batch (GPU) |
| --ubatch 128 # Process 128 tokens at once |
| ``` |
|
|
| ## Context Management |
|
|
| ```bash |
| # Default context (512 tokens) |
| -c 512 |
| |
| # Longer context (slower, more memory) |
| -c 4096 |
| |
| # Very long context (if model supports) |
| -c 32768 |
| ``` |
|
|
| ## Benchmarks |
|
|
| ### CPU Performance (Llama 2-7B Q4_K_M) |
|
|
| | Setup | Speed | Notes | |
| |-------|-------|-------| |
| | Apple M3 Max | 50 tok/s | Metal acceleration | |
| | AMD 7950X (16c) | 35 tok/s | OpenBLAS | |
| | Intel i9-13900K | 30 tok/s | AVX2 | |
|
|
| ### GPU Offloading (RTX 4090) |
|
|
| | Layers GPU | Speed | VRAM | |
| |------------|-------|------| |
| | 0 (CPU only) | 30 tok/s | 0 GB | |
| | 20 (hybrid) | 80 tok/s | 8 GB | |
| | 35 (all) | 120 tok/s | 12 GB | |
|
|
