5b81437637
- README.md: project overview, quick start, command reference, workflow - CLAUDE.md: AI safety rules, technical details, conventions - AGENTS.md: agent workflows, file responsibility map, dependency matrix - docs/architecture.md: script layers, data flow, unified memory, JSON schemas - docs/optimization.md: step-by-step optimization walkthrough - docs/benchmarking.md: methodology, test params, result interpretation - docs/troubleshooting.md: common issues and fixes - docs/references.md: centralized external links (single source of truth) - docs/bios-vram-guide.md: add back-link to optimization workflow Cross-linked non-redundantly: each doc owns one layer, others link to it. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
3.2 KiB
3.2 KiB
Benchmarking
What We Measure
All benchmarks use llama-bench (part of llama.cpp) running inside toolbox containers. Two test types:
| Metric | Meaning | Test Params |
|---|---|---|
| pp (prompt processing) | How fast the model ingests input tokens | Default: 512 tokens |
| tg (token generation) | How fast the model produces output tokens | Default: 128 tokens |
Results are in tokens/second (t/s). Higher is better.
Test Parameters
Standard Test
-ngl 99 -mmp 0 -fa 1 -r 5
-ngl 99— all layers on GPU-mmp 0— disable memory mapping (--no-mmap)-fa 1— flash attention enabled-r 5— 5 repetitions for statistical confidence
Long-Context Test
-ngl 99 -mmp 0 -fa 1 -p 2048 -n 32 -d 32768 -ub SIZE -r 3
-p 2048— 2048 prompt tokens-n 32— generate 32 tokens-d 32768— 32K context window-ub SIZE— micro-batch size (512 for Vulkan, 2048 for ROCm)-r 3— 3 repetitions (long-context tests are slow)
The -fa 1 --no-mmap -ngl 999 flags are mandatory on Strix Halo to avoid crashes.
Available Backends
| Backend | Container | Technology | Notes |
|---|---|---|---|
llama-vulkan-radv |
Mesa RADV | Vulkan | Most stable, recommended default |
llama-vulkan-amdvlk |
AMDVLK | Vulkan | Fastest when it works, 2GB buffer limit |
llama-rocm-6.4.4 |
ROCm 6.4.4 | HIP | Proven stable |
llama-rocm-7.2 |
ROCm 7.2 | HIP | Latest, compiler fixes applied |
Containers are from kyuz0/amd-strix-halo-toolboxes. Set up with make benchmark-setup.
Workflow
# 1. Setup (one-time)
make benchmark-setup
# 2. Capture baseline (before optimization)
make benchmark-baseline
# 3. After optimizing, run again
make benchmark # or: bin/benchmark run --tag post-opt
# 4. Compare
make benchmark-compare BEFORE=data/baselines/20260325-120000 AFTER=data/benchmarks/post-opt-20260326-100000
Result Format
Each run produces a directory under data/baselines/ or data/benchmarks/:
TIMESTAMP/
system-state.json # Full system audit snapshot
summary.json # Parsed results (model, backend, test, t/s)
metrics.csv # GPU/CPU metrics during the run
*.log # Raw llama-bench output per backend+model+test
Comparison Output
Backend | Model | Test | Before | After | Delta
vulkan-radv | qwen3-4b | pp512 | 548 t/s | 612 t/s | +11.7%
vulkan-radv | qwen3-4b | tg128 | 13.9 | 15.2 | +9.4%
Configuration changes between runs (VRAM, GTT, kernel params, tuned profile) are shown if system-state.json differs.
Recommended Test Models
| Size | Model | File | Disk | Use Case |
|---|---|---|---|---|
| Small | Qwen3-4B | Q4_K_M.gguf | ~3 GB | Quick smoke tests |
| Medium | Qwen3-14B | Q4_K_M.gguf | ~9 GB | Standard benchmarks |
| Large | Qwen3-32B | Q4_K_M.gguf | ~20 GB | Memory pressure tests |
Place models in data/models/. The VRAM estimator from the toolboxes project (gguf-vram-estimator.py) can help plan which models fit.