Donato Capitella
126 lines
3.6 KiB
Markdown
126 lines
3.6 KiB
Markdown
# AMD Strix Halo — llama.cpp Toolboxes (Benchmarks)
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**Live results:** [https://kyuz0.github.io/amd-strix-halo-toolboxes/](https://kyuz0.github.io/amd-strix-halo-toolboxes/)
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- Filter by model name, size, and quantization
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- Select backends with or without **Flash Attention (FA)**
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- Compare pp512 and tg128 side-by-side
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- Winners are computed with an error-aware tolerance rule.
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---
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## Benchmark methodology
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* **pp512** — prompt processing throughput (tokens/sec)
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* **tg128** — text generation throughput (tokens/sec)
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* Each backend tested twice:
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* FA off: `-fa 0`
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* FA on: `-fa 1`
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* Winners determined per model using pooled ± error from both results; multiple winners are possible.
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Tested backends:
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* Vulkan RADV
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* Vulkan AMDVLK
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* ROCm 6.4.2
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* ROCm 6.4.2 + rocWMMA
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* ROCm 7.x (beta / rc)
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All runs built from the same llama.cpp commit.
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---
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## Running benchmarks
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Place `.gguf` models in `models/` (for sharded models, include only the first shard: `*-00001-of-*.gguf`).
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Run:
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```bash
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benchmark/run_benchmarks.sh
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```
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This will:
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* Detect models
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* Execute each backend twice (FA off / FA on)
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* Save logs in `benchmark/results/`
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Generate `results.json` for analysis:
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```bash
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python benchmark/parse_results_to_json.py
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```
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Optional: print summary statistics:
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```bash
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python benchmark/summarize_results.py
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```
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---
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## Summary of current dataset
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### pp512 (prompt processing)
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* **Vulkan AMDVLK** leads in average throughput and most frequent wins.
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* Winner count: AMDVLK (FA on) – 11 models; AMDVLK (FA off) – 3 models.
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* Average t/s: AMDVLK (FA off) – 422.46; AMDVLK (FA on) – 388.68.
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* **Vulkan RADV** is competitive and shows wins on multiple models.
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* Winner count: RADV (FA on) – 3 models.
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* Average t/s: RADV (FA on) – 279.95; RADV (FA off) – 273.54.
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* **ROCm 6.4.2 + rocWMMA** is strong in some cases.
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* Winner count: 2 models (FA on).
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* Average t/s: rocWMMA (FA on) – 335.44.
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* ROCm 7.x variants trail in pp512 averages.
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**Conclusion:** AMDVLK is generally fastest for prompt processing. RADV is close on certain models and is less prone to instability. ROCm+rocWMMA can match or exceed in select cases but is inconsistent.
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---
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### tg128 (text generation)
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* **Vulkan RADV** shows the most frequent wins.
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* Winner count: RADV (FA off) – 6 models; RADV (FA on) – 5 models.
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* Average t/s: RADV (FA off) – 23.73; RADV (FA on) – 23.45.
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* **Vulkan AMDVLK** wins in some cases but is less dominant than in pp512.
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* Winner count: AMDVLK (FA off) – 4 models.
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* Average t/s: AMDVLK (FA off) – 25.91; AMDVLK (FA on) – 23.85.
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* **ROCm 6.4.2 + rocWMMA** achieves the highest average t/s.
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* Average t/s: rocWMMA (FA on) – 32.51; rocWMMA (FA off) – 31.96.
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* ROCm 7.x and ROCm 6.4.2 also appear among winners in several models.
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**Conclusion:** RADV is the most consistent for text generation wins. ROCm+rocWMMA delivers the highest averages but with potential stability issues. AMDVLK is competitive but not consistently ahead.
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---
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## Flash Attention (FA)
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FA effects vary:
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* In pp512 averages, AMDVLK performs better without FA.
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* In tg128, the effect depends on backend and model.
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FA should be treated as a per-model tuning parameter rather than enabled or disabled globally.
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---
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## Recommendations
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* **Stability priority:** Vulkan RADV.
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* **Maximum pp512 throughput:** Vulkan AMDVLK, validate per model.
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* **High tg128 averages:** ROCm 6.4.2 + rocWMMA, test stability.
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* **FA setting:** Evaluate per model/backend using side-by-side comparison.
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---
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## Winner calculation
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A backend is a winner if its mean throughput is within the best backend’s pooled ± error margin for that model and test type.
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