# llama.cpp 1.0 + Qwen3.8-27B: Vulkan vs SYCL on Intel Arc B390

> Source: <https://grigio.org/llama-cpp-1-0-qwen3-8-27b-vulkan-vs-sycl-on-intel-arc-b390/>
> Published: 2026-11-14 23:14:05+00:00

# llama.cpp 1.0 + Qwen3.8-27B: Vulkan vs SYCL on Intel Arc B390

**llama.cpp version:** `a956a852c` (commit: "Fix ctz64 on 32bit (#32273)", build 1386)**Model:** Qwen3.8-27B-thinkingcap-abliterated-mtp-Q4_K_M (15.65 GiB, 27.32B params)**Hardware:** Xiaomi Book Pro 14 — Intel Core Ultra X7 358H + **Intel Arc B390 GPU** (96 compute units, 30.7 GB shared memory)**OS:** CachyOS (Arch-based), Linux 6.12+, Mesa 24.3+, Intel oneAPI 2026.0

## Executive Summary

| Test | **Vulkan** | **SYCL** | **Winner** | 
|---|---|---|---|
| **Prompt Processing (pp512)** | **271.35 t/s** | 98.21 t/s | **Vulkan 2.76× faster** | 
| **Token Generation (tg128)** | 5.89 t/s | **6.06 t/s** | SYCL ~3% faster | 

**Vulkan dominates prompt processing** — critical for long-context workloads, RAG, and batch inference.**SYCL edges out token generation** — marginal advantage for interactive chat.

## Hardware & Software Stack

| Component | Version | 
|---|---|
| **GPU** | Intel Arc B390 (Battlemage, 96 EUs) | 
| **CPU** | Intel Core Ultra X7 358H (16c/16t, Panther Lake) | 
| **RAM** | 30 GB LPDDR5x + zram swap | 
| **Vulkan Driver** | Mesa 24.3 (open-source, `intel_hasvk` ) | 
| **SYCL Runtime** | Intel oneAPI 2026.0 (Level Zero 1.34, oneDNN 2026.0) | 
| **Compiler (Vulkan)** | Clang 23.1.4 | 
| **Compiler (SYCL)** | Intel icx/icpx 2026.0 | 
| **llama.cpp** | a956a852c (v1.0.0-dev, build 1386) | 

## Build Configurations

### Vulkan (Mesa / Open Source)

```
cmake -B build-vulkan \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_VULKAN=ON \
  -DGGML_CUDA=OFF -DGGML_HIP=OFF -DGGML_SYCL=OFF -DGGML_METAL=OFF -DGGML_OPENCL=OFF \
  -DBUILD_SHARED_LIBS=ON -DGGML_NATIVE=ON -DGGML_OPENMP=ON \
  -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
```

- Uses **Mesa's `hasvk` driver** with`KHR_cooperative_matrix` (matrix cores)
- No proprietary Intel stack required
- Supports fp16, bf16, int8 dot products

### SYCL (Intel oneAPI / Proprietary)

```
source /opt/intel/oneapi/setvars.sh
cmake -B build-sycl-linux \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_SYCL=ON -DGGML_SYCL_DNN=ON -DGGML_SYCL_GRAPH=ON \
  -DGGML_SYCL_TARGET=INTEL -DGGML_SYCL_F16=OFF \
  -DGGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON -DGGML_SYCL_SUPPORT_XMX=ON \
  -DGGML_SYCL_HOST_MEM_FALLBACK=ON \
  -DGGML_OPENCL=OFF -DGGML_CUDA=OFF -DGGML_HIP=OFF -DGGML_VULKAN=OFF -DGGML_METAL=OFF \
  -DBUILD_SHARED_LIBS=ON -DGGML_NATIVE=ON -DGGML_OPENMP=ON \
  -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
```

- Uses **Intel Level Zero** +**oneDNN** +**XMX (DPAS) matrix instructions**
- Requires proprietary oneAPI toolkit
- Enables: flash attention, graph capture, XMX ESIMD kernels, host-pinned memory

## Benchmark Methodology

```
# SYCL (requires oneAPI env)
source /opt/intel/oneapi/setvars.sh
./llama-bench -m Qwen3.8-27B-thinkingcap-abliterated-mtp-Q4_K_M.gguf -p 512 -n 128 -ngl 99

# Vulkan (runs directly)
./llama-bench -m Qwen3.8-27B-thinkingcap-abliterated-mtp-Q4_K_M.gguf -p 512 -n 128 -ngl 99
```

- **Model fully offloaded to GPU** (`-ngl 99` )
- **5 warmup runs + 5 measured runs** (default)
- **Single-threaded CPU** (`-t 1` ) to isolate GPU performance
- **Cache types:** K=f16, V=f16 (default)
- **Batch size:** 2048,**Ubatch:** 512 (default)

## Results

### Prompt Processing (pp512) — 512 token prompt ingestion

| Backend | Throughput | StdDev | Relative | 
|---|---|---|---|
| **Vulkan** | **271.35 t/s** | ±1.21 | **1.00× (baseline)** | 
| SYCL | 98.21 t/s | ±0.70 | 0.36× | 

**Vulkan is 2.76× faster** for prompt processing.

This is the **most impactful metric** for:

- Long-context ingestion (RAG, document analysis)
- Batch/parallel request processing
- Time-to-first-token in server deployments

### Token Generation (tg128) — 128 token continuation

| Backend | Throughput | StdDev | Relative | 
|---|---|---|---|
| SYCL | **6.06 t/s** | ±0.01 | **1.00× (baseline)** | 
| Vulkan | 5.89 t/s | ±0.06 | 0.97× | 

**SYCL is ~3% faster** for token generation — essentially a tie within noise.

This metric matters for:

- Interactive chat latency
- Streaming response speed
- Single-user throughput

## Performance Histogram

*Chart generated from 5-run averages per backend*

## Why the Difference?

| Factor | Vulkan Advantage | SYCL Advantage | 
|---|---|---|
| **Matrix cores** | `KHR_cooperative_matrix` (Mesa) | XMX/DPAS via oneDNN + ESIMD | 
| **Memory model** | Unified Memory (UMA) — zero-copy | USM device + host-pinned staging | 
| **Kernel fusion** | Limited (shader-based) | oneDNN graph fusion + flash attention | 
| **Driver overhead** | Lower (Mesa userspace) | Higher (Level Zero + JIT) | 
| **Compiler** | Clang (mature, optimized) | icx/icpx (aggressive vectorization) | 
| **Flash Attention** | Not yet in Vulkan backend | Enabled via oneDNN ( `GGML_SYCL_FA_ONEDNN=1` ) | 

### Prompt Processing (Memory-Bound)

Vulkan wins because:

- **Lower driver overhead** for large batched matmuls
- **Direct UMA access** — no staging copies for prompt ingestion
- **Mesa's cooperative matrix** efficiently maps to Xe² cores

### Token Generation (Compute-Bound, Small Matmuls)

SYCL wins marginally because:

- **oneDNN optimized kernels** for small GEMMs (1×128, 32×32)
- **XMX/DPAS instructions** better utilized at low batch sizes
- **Graph capture** reduces launch overhead for repeated decode steps

## Practical Recommendations

| Use Case | Recommended Backend | 
|---|---|
| **RAG / Document QA** | **Vulkan** (2.76× faster prompt ingestion) | 
| **Batch inference server** | **Vulkan** (higher throughput) | 
| **Interactive chat (single user)** | **Either** (tg128 within noise) | 
| **Long-context (>32k)** | **Vulkan** (better memory scaling) | 
| **No proprietary drivers wanted** | **Vulkan** (Mesa only) | 
| **Maximum flash-attn optimization** | **SYCL** (oneDNN FA enabled) | 

## Raw Data

```
# llama.cpp version
build: a956a852c (1386)

# SYCL
| qwen35 27B Q4_K - Medium | 15.65 GiB | 27.32 B | SYCL | 99 | pp512 | 98.21 ± 0.70 |
| qwen35 27B Q4_K - Medium | 15.65 GiB | 27.32 B | SYCL | 99 | tg128 | 6.06 ± 0.01 |

# Vulkan
| qwen35 27B Q4_K - Medium | 15.65 GiB | 27.32 B | Vulkan | 99 | pp512 | 271.35 ± 1.21 |
| qwen35 27B Q4_K - Medium | 15.65 GiB | 27.32 B | Vulkan | 99 | tg128 | 5.89 ± 0.06 |
```

## Reproducing

```
# Clone llama-bench (llama.cpp fork with bench tooling)
git clone https://github.com/ggml-org/llama.cpp llama-bench
cd llama-bench

# Build Vulkan
cmake -B build-vulkan -DGGML_VULKAN=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ...
cmake --build build-vulkan -j$(nproc)

# Build SYCL (requires Intel oneAPI)
source /opt/intel/oneapi/setvars.sh
cmake -B build-sycl-linux -DGGML_SYCL=ON -DGGML_SYCL_DNN=ON ... -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build build-sycl-linux -j$(nproc)

# Run benchmarks
./build-vulkan/bin/llama-bench -m <model> -p 512 -n 128 -ngl 99
source /opt/intel/oneapi/setvars.sh && ./build-sycl-linux/bin/llama-bench -m <model> -p 512 -n 128 -ngl 99
```

## Conclusion

**For Intel Arc B390 on Linux today: Vulkan is the better default.**

- **2.76× faster prompt processing** — huge for real-world workloads
- **No proprietary stack** — Mesa works out of the box
- **Token generation parity** — SYCL's 3% edge is negligible

SYCL remains valuable for:

- Flash Attention workloads (oneDNN integration)
- Maximum compute utilization on Xe² matrix cores
- Developers already invested in oneAPI ecosystem

As Mesa's `hasvk` driver matures and adds cooperative matrix fp16/bf16 accumulation, Vulkan's lead will likely grow.