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Donato Capitella
2025-08-03 13:59:01 +01:00
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@@ -117,7 +117,7 @@ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download unsloth/Qwen3-Coder-30B-A3B
## 3. Performance Benchmarks (Key Results) ## 3. Performance Benchmarks (Key Results)
Below are some results from real runs on Strix Halo hardware of `llama-bench`. For full tables and model-by-model breakdowns (including both prompt processing and token generation speeds), see docs/benchmarks.md. Below are some results from real runs on Strix Halo hardware of `llama-bench`. For full tables and model-by-model breakdowns (including both prompt processing and token generation speeds), see [docs/benchmarks.md](docs/benchmarks.md).
| Model | Vulkan (AMDVLK) | Vulkan (RADV) | ROCm 6.4.2 | ROCm 7.0 Beta | ROCm 7.0 RC | 🏆 Best PP | 🏆 Best TG | | Model | Vulkan (AMDVLK) | Vulkan (RADV) | ROCm 6.4.2 | ROCm 7.0 Beta | ROCm 7.0 RC | 🏆 Best PP | 🏆 Best TG |
| ------------------------------ | ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------------- | -------------- | | ------------------------------ | ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------------- | -------------- |
@@ -179,7 +179,7 @@ $ gguf-vram-estimator.py models/qwen3-235B-Q3_K-XL/UD-Q3_K_XL/Qwen3-235B-A22B-In
For Qwen3-235B, **128GB RAM allows you to run with context up to \~130k tokens.** For Qwen3-235B, **128GB RAM allows you to run with context up to \~130k tokens.**
* The estimator lets you plan ahead and avoid out-of-memory errors when loading or using models. * The estimator lets you plan ahead and avoid out-of-memory errors when loading or using models.
* For more examples and a breakdown of VRAM components, see docs/vram-estimator.md. * For more examples and a breakdown of VRAM components, see [docs/vram-estimator.md](docs/vram-estimator.md).
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@@ -187,7 +187,7 @@ For Qwen3-235B, **128GB RAM allows you to run with context up to \~130k tokens.*
Pre-built toolbox container images are published on Docker Hub for immediate use. If you wish to build the containers yourself (for example, to customize packages or rebuild with a different llama.cpp version), see: Pre-built toolbox container images are published on Docker Hub for immediate use. If you wish to build the containers yourself (for example, to customize packages or rebuild with a different llama.cpp version), see:
Full instructions: docs/building.md Full instructions: [docs/building.md](docs/building.md).
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@@ -235,9 +235,9 @@ Follow this guide by TechnigmaAI for a working configuration on Ubuntu 24.04:
## 7. More Documentation ## 7. More Documentation
* docs/benchmarks.md: Full benchmark logs, model list, parsed results * [docs/benchmarks.md](docs/benchmarks.md): Full benchmark logs, model list, parsed results
* docs/vram-estimator.md: Memory planning, practical example runs * [docs/vram-estimator.md](docs/vram-estimator.md): Memory planning, practical example runs
* docs/building.md: Local build, toolbox customization, advanced use * [docs/building.md](docs/building.md): Local build, toolbox customization, advanced use
## 8. References ## 8. References