Benchmarking llama.cpp Backends on Intel Panther Lake: Vulkan vs SYCL vs OpenVINO vs CPU

I benchmarked the Qwen3.6-35B-A3B-MTP-UD-Q4_K_M.gguf model (a 35B Mixture-of-Experts model with 3B active parameters, 22 GiB quantized) across all available llama.cpp compute backends on an Intel Panther Lake laptop. The goal: find out which backend delivers the best inference performance for large MoE models on integrated Intel GPU hardware.

Hardware

The key architectural detail here is UMA (Unified Memory Architecture). The Intel Arc B390 shares system RAM with the CPU — there's no dedicated VRAM. This means the 22 GiB model lives in the same 30 GiB pool as the KV cache and OS, making context size and cache quantization critical to avoid OOM.

Software Setup

• llama.cpp v0.4.1-dev (commit 83078fe), cloned fresh from master
• Model: Qwen3.6-35B-A3B-MTP-UD-Q4_K_M.gguf — 35B MoE (A3B active), 256 experts x 8, 41 blocks, hybrid Attention (full attn every 4 layers) + Mamba2 SSM, MTP nextn=1
• Builds: 4 separate CMake builds in isolated directories:
  • build-vulkan/ — -DGGML_VULKAN=ON -DGGML_NATIVE=ON
  • build-sycl/ — -DGGML_SYCL=ON -DGGML_SYCL_F16=ON, compiled with Intel icpx
  • build-openvino/ — -DGGML_OPENVINO=ON
  • build-cpu/ — -DGGML_NATIVE=ON (pure CPU, no GPU offload)
• Benchmark: llama-bench with pp512 (prompt processing 512 tokens), tg128 (token generation 128 tokens), KV cache q4_0, flash-attn on, 5 repetitions, 16 threads

Issues Encountered

1. OpenVINO: Model Incompatibility

The OpenVINO backend failed with two distinct errors:

1. quantized V cache requires flash_attn to be enabled — When using q4_0 KV cache without flash-attn, OpenVINO can't create the context because it doesn't support flash attention for this model architecture.
2. test_prompt: failed to decode prompt batch, res = -3 — When flash-attn is enabled (which would allow quantized KV), OpenVINO still fails because it doesn't support the hybrid Attention + Mamba2 SSM + MTP architecture of Qwen3.6-35B.

The root cause: OpenVINO's llama.cpp backend is primarily validated on dense models under 8B parameters. MoE routing, recurrent state models (Mamba2/SSM), and multi-token prediction heads are outside its current scope. With f16 KV cache, the 22 GiB model + ~10 GiB KV cache exceeds the 30 GiB RAM limit, causing OOM.

Verdict: OpenVINO is not viable for this model class. It would require significant upstream work to support MoE offloading and SSM state management.

2. SYCL: Level Zero API Missing

During SYCL build configuration, CMake warned:

CMake Warning: Level Zero loader or development headers not found,
Level Zero API support disabled.

Level Zero is Intel's low-level GPU API that provides additional performance features. The SYCL backend compiled and works without it, but operates in a degraded mode:

• Device-to-device memory copies use SYCL API instead of Level Zero
• Memory allocation uses SYCL API instead of VMM
• Some optimization paths are disabled

This means the SYCL results represent a "good but not optimal" configuration. Installing the Level Zero SDK could improve SYCL performance further.

3. OneAPI Installation

Intel's oneAPI toolkit is 2.1 GiB (offline installer) and requires specific silent install syntax:

sudo sh ./intel-oneapi-toolkit-2026.1.1.33_offline.sh \
  -a -s --eula accept --install-dir /opt/intel/oneapi

The --eula flag must come after -a (argument passthrough), not as a top-level flag. The installer also warns about missing VTune GUI dependencies (NSS, XCB, DRM, GTK3, etc.) which are harmless on a headless/server setup but noisy in logs.

4. OpenCL Headers

OpenVINO build initially failed with CL/cl2.hpp: No such file or directory. The fix was installing both opencl-headers and opencl-clhpp packages.

Results

Prompt Processing (pp512)

Vulkan and SYCL are neck-and-neck, with Vulkan holding a slight edge:

• Vulkan: 624.53 tok/s (best case)
• SYCL: 609.36 tok/s (-2.4%)
• CPU: 101.57 tok/s (-83.7%)

The 6x speedup over CPU confirms that GPU offload is essential for this model. The gap between Vulkan and SYCL is small (~2.5%), suggesting both backends are well-optimized for the compute-bound prompt processing phase.

Token Generation (tg128)

The gap widens slightly in token generation:

• Vulkan: 32.75 tok/s
• SYCL: 30.62 tok/s (-6.5%)
• CPU: 19.44 tok/s

Token generation is memory-bandwidth bound (each step reads the full model weights). On UMA, GPU and CPU share the same memory bus, so the 1.7x speedup over CPU is smaller than the 6x for prompt processing. The Vulkan backend's slightly better tg performance likely comes from more efficient memory access patterns in the Mesa ANV driver.

Why Vulkan Beats SYCL

Several factors contribute:

1. Mesa ANV maturity: The open-source Mesa Vulkan driver for Intel GPUs has years of optimization. SYCL's Level Zero backend, while functional, is newer.
2. Level Zero disabled: Without Level Zero, SYCL falls back to less efficient memory management. Enabling it could close the gap.
3. F16 vs native precision: SYCL was built with GGML_SYCL_F16=ON (half-precision compute), which trades some accuracy for throughput. Vulkan uses the driver's native precision selection.
4. KHR_coopmat: Both backends use cooperative matrix extensions, but Vulkan's implementation may have better-tuned tile sizes for this specific hardware.

Key Takeaways

1. For Panther Lake with MoE models, use Vulkan. Zero setup, best performance, no oneAPI required.
2. SYCL is competitive but needs tuning. The Level Zero SDK would help, and future oneAPI releases may close the gap.
3. OpenVINO is not ready for MoE+SSM models. It's a capable framework for smaller dense models but lacks the operator support for modern architectures.
4. GPU offload is non-negotiable on UMA. A 6x speedup on prompt processing and 1.7x on token generation makes the difference between usable and sluggish for a 35B model.
5. Context size is the hidden bottleneck. With 30 GiB shared between model (22 GiB) and KV cache, even q4_0 quantization limits context to ~35k tokens. Users who need long contexts must reduce offloaded layers or accept smaller context windows.

Reproduction

All builds, scripts, and results are in /home/g/Code/llama-bench/:

# Run the full benchmark suite
./bench.sh

# Or run individual backends
build-vulkan/bin/llama-bench -m models/Qwen3.6-35B-A3B-MTP-UD-Q4_K_M.gguf -p 512 -n 128 -ngl 99 -t 16 -r 5 -fa on -ctk q4_0 -ctv q4_0 -o md

source /opt/intel/oneapi/setvars.sh && build-sycl/bin/llama-bench -m models/Qwen3.6-35B-A3B-MTP-UD-Q4_K_M.gguf -p 512 -n 128 -ngl 99 -t 16 -r 5 -fa on -ctk q4_0 -ctv q4_0 -o md

build-cpu/bin/llama-bench -m models/Qwen3.6-35B-A3B-MTP-UD-Q4_K_M.gguf -p 512 -n 128 -ngl 0 -t 16 -r 5 -fa on -ctk q4_0 -ctv q4_0 -o md