Updated benchmakrs, removed old toolboxes and results
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Donato Capitella
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# AMD Strix Halo — llama.cpp Toolboxes (Benchmarks)
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**Interactive results:** [https://kyuz0.github.io/amd-strix-halo-toolboxes/](https://kyuz0.github.io/amd-strix-halo-toolboxes/)
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**Interactive results:** 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**
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* Compare pp512 and tg128 side-by-side
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* Winners are computed using an **error-aware tolerance rule** — if two results overlap within their ± error margins, both are counted as winners.
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## Table of Contents
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- [Benchmark methodology](#benchmark-methodology)
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- [Summary of current dataset (Flash Attention ON)](#summary-of-current-dataset-flash-attention-on)
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- [Placement counts](#placement-counts)
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- [Pairwise head-to-head wins](#pairwise-head-to-head-wins)
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- [Average ranks](#average-ranks)
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- [Analyses by feature](#analyses-by-feature)
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- [Impact of Flash Attention](#impact-of-flash-attention)
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- [Impact of ROCWMMA](#impact-of-rocwmma)
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- [Impact of hipBLASLt](#impact-of-hipblaslt)
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- [Vulkan: AMDVLK vs RADV](#vulkan-amdvlk-vs-radv)
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- [Recommendations](#recommendations)
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- [Winner calculation](#winner-calculation)
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---
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## Benchmark methodology
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* **pp512** — prompt processing throughput (tokens/sec, prefill)
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* **tg128** — token generation throughput (tokens/sec, interactive)
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* Each backend tested twice per model:
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- **pp512** — prompt processing throughput (tokens/sec, prefill)
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- **tg128** — token generation throughput (tokens/sec, interactive)
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- Each backend tested twice per model: `-fa 0` and `-fa 1`
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- Winners per model/test are **margin-aware**; multiple winners are possible when mean±σ overlap
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- Built from the same llama.cpp commit for consistency
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* **Flash Attention OFF:** `-fa 0`
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* **Flash Attention ON:** `-fa 1`
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* Winners are determined per model using pooled ± error from all relevant runs; multiple winners are possible.
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* All runs were built from the same `llama.cpp` commit for consistency.
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**Backends in this dataset:** ROCm 7 RC + ROCWMMA + hipBLASLt, ROCm 7 RC (hipBLASLt), ROCm 7 RC (hipBLASLt OFF), ROCm 7 RC + ROCWMMA (hipBLASLt OFF), ROCm 6.4.3 (hipBLASLt), ROCm 6.4.3 (hipBLASLt OFF), ROCm 6.4.3 + ROCWMMA (hipBLASLt), ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF), Vulkan AMDVLK, Vulkan RADV
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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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* ROCm 7.x + ROCWMMA + hipBLASLt
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**Note on ROCm 7 hipBLASLt:**
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All ROCm 7 toolboxes ship with **hipBLASLt enabled by default** (`ROCBLAS_USE_HIPBLASLT=1`) because it improves performance and stability in most cases.
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However, the benchmark script also includes runs with **hipBLASLt disabled** (`-hblt0`) so we can measure the impact directly.
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**ROCm 7 hipBLASLt policy:** Toolboxes ship with **hipBLASLt enabled** by default (`ROCBLAS_USE_HIPBLASLT=1`). The benchmark script also runs **hipBLASLt OFF** variants (`-hblt0`) to measure its effect.
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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 (margin-aware, Flash Attention ON)
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### Prompt Processing (pp512)
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* **ROCm 7 RC + ROCWMMA + hipBLASLt** dominates — **15 wins/ties** out of 22 models.
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* **Vulkan AMDVLK** is second most frequent winner (**4 wins/ties**) but can’t load certain architectures due to the ≤ 2 GiB single-buffer limit.
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* **Vulkan RADV** rarely wins in PP but is highly stable.
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### Token Generation (tg128)
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* **Vulkan RADV** leads — **13 wins/ties** out of 15 possible.
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* **Vulkan AMDVLK** is a strong second, usually just behind RADV in TG.
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* **ROCm 7 RC + ROCWMMA + hipBLASLt** generally lags in TG but still posts competitive results for some models.
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---
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### Placement counts (margin-aware, Flash Attention ON)
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## Summary of current dataset (Flash Attention ON)
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### Placement counts
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**Prompt Processing (pp512)**
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| Backend | 1st | 2nd | 3rd |
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| ------------------------------- | -----: | --: | --: |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | **15** | 2 | 1 |
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| Vulkan AMDVLK | 4 | 5 | 1 |
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| Vulkan RADV | 0 | 2 | 2 |
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| Backend | 1st | 2nd | 3rd |
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| --- | ---: | ---: | ---: |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt) | 9 | 5 | 0 |
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| ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 3 | 3 | 8 |
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| Vulkan AMDVLK | 3 | 0 | 2 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 1 | 8 | 4 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 0 | 0 | 1 |
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| Vulkan RADV | 0 | 0 | 1 |
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**Token Generation (tg128)**
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| Backend | 1st | 2nd | 3rd |
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| --- | ---: | ---: | ---: |
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| Vulkan RADV | 13 | 0 | 0 |
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| ROCm 6.4.3 (hipBLASLt) | 3 | 0 | 1 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt) | 1 | 4 | 3 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 1 | 2 | 4 |
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| ROCm 6.4.3 (hipBLASLt OFF) | 1 | 1 | 1 |
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| ROCm 7 RC (hipBLASLt OFF) | 1 | 1 | 1 |
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| ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 1 | 1 | 1 |
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| ROCm 7 RC (hipBLASLt) | 1 | 0 | 4 |
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| Vulkan AMDVLK | 0 | 10 | 0 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 0 | 1 | 2 |
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| Backend | 1st | 2nd | 3rd |
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| ------------------------------- | -----: | --: | --: |
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| Vulkan RADV | **13** | 1 | 1 |
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| Vulkan AMDVLK | 1 | 10 | 1 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 1 | 1 | 6 |
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### Pairwise head-to-head wins
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For any model+quant where both backends succeeded, this counts who was faster (ties when equal).
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| Comparison | Test | A wins | B wins | Ties | Total |
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| --- | --- | ---: | ---: | ---: | ---: |
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| ROCm 7 RC + ROCWMMA + hipBLASLt vs Vulkan AMDVLK | pp512 | 11 | 4 | 0 | 15 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt vs Vulkan AMDVLK | tg128 | 4 | 10 | 1 | 15 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt vs Vulkan RADV | pp512 | 14 | 2 | 0 | 16 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt vs Vulkan RADV | tg128 | 3 | 13 | 0 | 16 |
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| Vulkan AMDVLK vs Vulkan RADV | pp512 | 13 | 2 | 0 | 15 |
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| Vulkan AMDVLK vs Vulkan RADV | tg128 | 2 | 13 | 0 | 15 |
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### Average ranks
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**Prompt Processing (pp512)**
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| Backend | Avg Rank (↓ is better) |
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| --- | ---: |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt) | 1.36 |
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| Vulkan AMDVLK | 1.8 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 2.23 |
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| ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 2.36 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 3.0 |
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| Vulkan RADV | 3.0 |
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**Token Generation (tg128)**
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| Backend | Avg Rank (↓ is better) |
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| --- | ---: |
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| Vulkan RADV | 1.0 |
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| ROCm 6.4.3 (hipBLASLt) | 1.5 |
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| Vulkan AMDVLK | 2.0 |
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| ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 2.0 |
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| ROCm 7 RC (hipBLASLt OFF) | 2.0 |
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| ROCm 6.4.3 (hipBLASLt OFF) | 2.0 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt) | 2.25 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 2.43 |
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| ROCm 7 RC (hipBLASLt) | 2.6 |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 2.67 |
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---
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## Flash Attention
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## Analyses by feature
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* **ROCm 7 RC + ROCWMMA + hipBLASLt** benefits noticeably from Flash Attention ON in prompt processing, with no stability penalties recorded.
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* **Vulkan AMDVLK** and **Vulkan RADV** show mixed changes — some models improve with FA, others slow down slightly.
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* FA should be enabled or disabled **per model/backend** based on measured performance.
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### Impact of Flash Attention
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Median % change when **Flash Attention ON vs OFF**, paired by model+quant, per backend:
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| Backend | pp512 Δ% (median, min..max, n) | tg128 Δ% (median, min..max, n) |
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| --- | --- | --- |
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| ROCm 7 RC + ROCWMMA + hipBLASLt | 8.4% (3.6..65.6), n=14 | -1.1% (-8.2..-0.3), n=14 |
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| ROCm 7 RC (hipBLASLt) | -20.2% (-27.8..6.5), n=10 | -1.4% (-8.5..3.0), n=10 |
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| ROCm 7 RC (hipBLASLt OFF) | -20.4% (-28.2..-16.1), n=9 | -1.9% (-8.6..0.1), n=9 |
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| ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 5.8% (1.3..24.1), n=16 | -1.1% (-7.4..15.1), n=16 |
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| ROCm 6.4.3 (hipBLASLt) | -19.5% (-25.7..-11.9), n=12 | -1.2% (-6.9..0.8), n=12 |
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| ROCm 6.4.3 (hipBLASLt OFF) | -10.3% (-22.3..3.6), n=9 | -1.6% (-11.1..0.0), n=9 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt) | 10.9% (3.9..25.7), n=15 | -0.4% (-7.5..3.0), n=15 |
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| ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 6.4% (1.8..12.3), n=10 | -0.6% (-6.5..2.3), n=10 |
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| Vulkan AMDVLK | 1.1% (-45.4..20.2), n=15 | -1.5% (-28.6..0.1), n=15 |
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| Vulkan RADV | 3.4% (-2.6..12.5), n=16 | 0.0% (-5.8..2.4), n=16 |
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### Impact of ROCWMMA
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| Context | Test | Compared Envs | Pairs | Median Δ% |
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| --- | --- | --- | ---: | ---: |
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| ROCm 7 RC (hipBLASLt) | pp512 | ROCm 7 RC + ROCWMMA + hipBLASLt vs ROCm 7 RC (hipBLASLt) | 16 | 16.3% |
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| ROCm 7 RC (hipBLASLt) | tg128 | ROCm 7 RC + ROCWMMA + hipBLASLt vs ROCm 7 RC (hipBLASLt) | 16 | -0.7% |
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| ROCm 7 RC (hipBLASLt OFF) | pp512 | ROCm 7 RC + ROCWMMA (hipBLASLt OFF) vs ROCm 7 RC (hipBLASLt OFF) | 15 | 14.6% |
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| ROCm 7 RC (hipBLASLt OFF) | tg128 | ROCm 7 RC + ROCWMMA (hipBLASLt OFF) vs ROCm 7 RC (hipBLASLt OFF) | 15 | -0.7% |
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| ROCm 6.4.3 (hipBLASLt) | pp512 | ROCm 6.4.3 + ROCWMMA (hipBLASLt) vs ROCm 6.4.3 (hipBLASLt) | 15 | 17.4% |
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| ROCm 6.4.3 (hipBLASLt) | tg128 | ROCm 6.4.3 + ROCWMMA (hipBLASLt) vs ROCm 6.4.3 (hipBLASLt) | 15 | -0.3% |
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| ROCm 6.4.3 (hipBLASLt OFF) | pp512 | ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) vs ROCm 6.4.3 (hipBLASLt OFF) | 9 | 10.2% |
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| ROCm 6.4.3 (hipBLASLt OFF) | tg128 | ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) vs ROCm 6.4.3 (hipBLASLt OFF) | 9 | 0.3% |
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### Impact of hipBLASLt
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| Context | Test | Compared Envs | Pairs | Median Δ% |
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| --- | --- | --- | ---: | ---: |
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| ROCm 7 RC (no ROCWMMA) | pp512 | ROCm 7 RC (hipBLASLt) vs ROCm 7 RC (hipBLASLt OFF) | 15 | -0.2% |
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| ROCm 7 RC (no ROCWMMA) | tg128 | ROCm 7 RC (hipBLASLt) vs ROCm 7 RC (hipBLASLt OFF) | 15 | -0.1% |
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| ROCm 7 RC + ROCWMMA | pp512 | ROCm 7 RC + ROCWMMA + hipBLASLt vs ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 16 | 1.4% |
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| ROCm 7 RC + ROCWMMA | tg128 | ROCm 7 RC + ROCWMMA + hipBLASLt vs ROCm 7 RC + ROCWMMA (hipBLASLt OFF) | 16 | 0.0% |
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| ROCm 6.4.3 (no ROCWMMA) | pp512 | ROCm 6.4.3 (hipBLASLt) vs ROCm 6.4.3 (hipBLASLt OFF) | 9 | 155.5% |
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| ROCm 6.4.3 (no ROCWMMA) | tg128 | ROCm 6.4.3 (hipBLASLt) vs ROCm 6.4.3 (hipBLASLt OFF) | 9 | 0.0% |
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| ROCm 6.4.3 + ROCWMMA | pp512 | ROCm 6.4.3 + ROCWMMA (hipBLASLt) vs ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 13 | 116.9% |
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| ROCm 6.4.3 + ROCWMMA | tg128 | ROCm 6.4.3 + ROCWMMA (hipBLASLt) vs ROCm 6.4.3 + ROCWMMA (hipBLASLt OFF) | 13 | -0.0% |
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### Vulkan: AMDVLK vs RADV
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Head-to-head wins with selected Flash Attention filter:
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| Test | AMDVLK wins | RADV wins | Ties | Total |
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| --- | ---: | ---: | ---: | ---: |
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| pp512 | 13 | 2 | 0 | 15 |
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| tg128 | 2 | 13 | 0 | 15 |
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---
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## Recommendations
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* **Fastest prompt processing:** ROCm 7 RC + ROCWMMA + hipBLASLt (Flash Attention ON)
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* **Fastest token generation:** Vulkan RADV (Flash Attention ON)
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* **Balanced performance:** Vulkan AMDVLK (fast PP & decent TG, but ≤ 2 GiB buffer limit)
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* **BF16 models:** ROCm 7 RC + ROCWMMA + hipBLASLt (best ROCm PP/TG combo, stable with FA ON)
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* **Maximum stability:** Vulkan RADV
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- **Fastest prompt processing:** ROCm 6.4.3 + ROCWMMA (hipBLASLt) (most 1st-place finishes with selected Flash Attention filter).
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- **Fastest token generation:** Vulkan RADV (most 1st-place finishes with selected Flash Attention filter).
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- **Balanced choice:** ROCm 6.4.3 + ROCWMMA (hipBLASLt) (consistently near the top across PP/TG).
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---
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## Winner calculation
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A backend is counted as a winner if its mean throughput is within the best backend’s pooled ± error margin for that model/test type. This ensures results within measurement noise are treated as ties, not false losses.
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A backend is counted as a winner if its mean throughput is within the best backend’s pooled ± error margin for that model/test type. This treats results within measurement noise as ties instead of false losses.
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