Alexandar Bonin
f179611a6f
Real-world benchmarks, driver configurations, and working/broken matrix from hands-on llama.cpp testing with Qwen3.5-35B-A3B MoE on an Arc A770 (SYCL) + RX 580 (Vulkan) dual-GPU setup. Key findings: xe driver mandatory (i915 hangs), Vulkan compute broken on Arc, RX 580 Vulkan beats Arc SYCL with --cpu-moe, generation is DDR4 bandwidth-bound at ~20 t/s.
57 lines
3.6 KiB
Markdown
57 lines
3.6 KiB
Markdown
# Intel Arc GPU — LLM Inference Diagnosis
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Research into why Intel Arc GPUs (Alchemist / Xe1 and Battlemage / Xe2) severely underperform on quantized LLM inference, often achieving only **21–40% of theoretical memory bandwidth** during token generation — compared to 80–95% on equivalent NVIDIA and AMD hardware.
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## The Problem
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Intel Arc GPUs look great on paper for LLM inference: ample VRAM, wide memory buses, dedicated XMX matrix engines. In practice, community benchmarks consistently show:
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- **Q8_0 quantized models running 4–5× slower** than Q4_K_M despite only moving 1.7× more data
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- Token generation achieving only **21% of peak bandwidth** on some quantization types
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- Wildly inconsistent performance across SYCL, Vulkan, OpenVINO, and IPEX-LLM backends
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- Architecture-specific regressions on Xe2 (Battlemage) that don't exist on Xe1 (Alchemist)
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The root causes are multi-layered: missing kernel optimizations in `llama.cpp`, a fragmented Intel software stack (five semi-independent efforts that don't interoperate), quantization-specific dispatch path bugs, and an overall underinvestment in open-source kernel development for Intel GPU architectures.
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## Empirical Findings
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- **[Empirical Findings](empirical_findings.md)** — Real-world benchmarks and configurations from an Arc A770 + RX 580 system running llama.cpp with Qwen3.5-35B-A3B MoE. Includes driver setup (xe vs i915), SYCL/Vulkan status, performance tables, and working/broken configuration matrix.
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## Overviews
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Each overview was independently produced by a different LLM, analyzing community issues, kernel source code, driver stacks, and benchmark data:
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- **[Kimi's Overview](overview_kimi.md)** — Focuses on driver/runtime stack mapping, quantization kernel inefficiencies (DMMV vs. MMVQ paths), and the missing reorder optimization for Q8_0.
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- **[GLM's Overview](overview_glm.md)** — Broadest scope: full stack architecture diagram, version compatibility matrix, fragmentation analysis across five Intel inference stacks, and the Battlemage regression class.
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- **[MiniMax's Overview](overview_minimax.md)** — Hardware landscape, per-GPU status table, critical issue triage (Q8_0 catastrophe, iGPU misdetection), and kernel-level root cause analysis.
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## Research
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Supporting deep-dives in [`research/`](research/):
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- [`research/kernels/kernel_analysis_minimax.md`](research/kernels/kernel_analysis_minimax.md) — Detailed kernel dispatch path analysis
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- [`research/community_issues/issues_and_discourse_minimax.md`](research/community_issues/issues_and_discourse_minimax.md) — Curated community issue reports and discourse
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## Repo Map
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The `repos/` directory contains source clones of the relevant Intel GPU and LLM inference projects for offline analysis (not tracked in this repository):
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| Repository | Purpose |
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| `llama.cpp` | SYCL & Vulkan backends, GGUF quantization kernels |
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| `ipex-llm` | Intel's former PyTorch integration layer (archived Jan 2026) |
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| `intel-extension-for-pytorch` | PyTorch XPU extension (deprecated) |
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| `compute-runtime` | Intel Level Zero / OpenCL driver (NEO) |
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| `intel-graphics-compiler` | JIT compiler (SYCL → Xe ISA) |
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| `oneDNN` | Deep-learning primitive library |
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| `vllm` | vLLM mainline (XPU backend in flux) |
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| `vllm-xpu-kernels` | Dedicated Intel kernel repo for vLLM |
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| `level-zero` | Level Zero loader and headers |
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| `llvm` | DPC++ / SYCL compiler toolchain |
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| `openvino` | Intel's inference optimizer/runtime |
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| `sycl-tla` | SYCL abstraction layer |
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## License
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This research documentation is released under [CC0](https://creativecommons.org/publicdomain/zero/1.0/). Referenced repositories carry their own licenses.
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