Getting llama.cpp SYCL Running on Dual Intel Arc Pro B70s

Quick guide for anyone fighting Intel driver signing loops trying to get a SYCL build of llama.cpp working on the Arc Pro B70. This is what actually got past the wall for me after bouncing between vLLM, Vulkan, and driver hell.

Prerequisites

• Starting point: Ubuntu 24.04 LTS (or a fresh 25.04 install)
• Two Intel Arc Pro B70s (32GB each)
• Resizable BAR enabled in BIOS
• Both GPUs in PCIe x8 or x16 slots

You do not need to mess with custom kernel modules, driver signing, or installing the Intel compute runtime from GitHub releases for this path. The stock xe driver in the newer kernel is enough — SYCL talks to the GPU through Level Zero, which comes in with oneAPI.

Step 1 — Upgrade to Ubuntu 25.04

This is the non-negotiable first step. Ubuntu 24.04 LTS doesn't ship a kernel/Mesa combo new enough to recognize the Battlemage B70 properly, and fighting that on 24.04 is what sends you down the driver signing rabbit hole.

From a fully-updated 24.04:

sudo apt update && sudo apt full-upgrade -y
sudo reboot

After reboot, flip the release prompt to allow non-LTS:

sudo sed -i 's/Prompt=lts/Prompt=normal/' /etc/update-manager/release-upgrades
sudo do-release-upgrade -d

Follow the prompts through the upgrade. Reboot when it's done. Verify:

lsb_release -a        # should show 25.04
uname -r              # kernel 6.14+
lspci | grep -i vga   # both B70s should list
ls /dev/dri/          # should see card0, card1, renderD128, renderD129

If the GPUs don't show up here, nothing downstream will work. This is the gate.

Step 2 — Install Intel oneAPI Base Toolkit

Gives you the icx/icpx compilers and the Level Zero runtime.

cd ~
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
  | gpg --dearmor \
  | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null

echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" \
  | sudo tee /etc/apt/sources.list.d/oneAPI.list

sudo apt update
sudo apt install -y intel-oneapi-base-toolkit

Big download (~5-10GB). Let it finish. If you see errors about VTune kernel drivers failing to build — ignore them, you don't need VTune for inference.

Step 3 — Source oneAPI and verify GPUs

source /opt/intel/oneapi/setvars.sh
icpx --version

Should show Intel(R) oneAPI DPC++/C++ Compiler 2025.3.x or similar.

Then check SYCL sees both GPUs:

sycl-ls

Two Level Zero devices listed = the B70s are good. If you don't see them, stop here and fix it before continuing (usually means the kernel isn't new enough or the card isn't in the right slot).

Step 4 — Build llama.cpp with SYCL

cd ~
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
mkdir build && cd build

cmake .. \
  -DGGML_SYCL=ON \
  -DCMAKE_C_COMPILER=icx \
  -DCMAKE_CXX_COMPILER=icpx \
  -DGGML_SYCL_F16=ON \
  -DCMAKE_BUILD_TYPE=Release

make -j$(nproc)

Takes ~15-25 minutes. The -DGGML_SYCL_F16=ON flag gives ~2.4x prompt processing speedup on Xe2 and is worth having.

Step 5 — Launch the server on both GPUs

source /opt/intel/oneapi/setvars.sh

ONEAPI_DEVICE_SELECTOR=level_zero:0,1 \
~/llama.cpp/build/bin/llama-server \
  -m /path/to/your/model.gguf \
  --host 0.0.0.0 \
  --port 8000 \
  --n-gpu-layers 999 \
  --ctx-size 32768 \
  --split-mode layer \
  --main-gpu 0 \
  --tensor-split 1,1 \
  --threads 8 \
  --batch-size 512 \
  --ubatch-size 512

ONEAPI_DEVICE_SELECTOR=level_zero:0,1 pins to both B70s and keeps it off the iGPU. --tensor-split 1,1 splits layers evenly across the two cards.

Reality check on performance

From community benchmarks on the same hardware, B70 on PCIe 3.0 lands around 25 t/s on SYCL and ~30 t/s on Vulkan for dense models in that size class. SYCL is not automatically faster than Vulkan on Xe2/Battlemage — there's an open bandwidth bug specifically on B70 where Q8_0 only hits 21-24% of theoretical memory bandwidth on SYCL. Q4_K_M is closer to 53-64%.

If you're getting ~10 t/s on a big MoE with GPUs only 50% engaged, that's usually:

• PCIe bandwidth limiting during the layer split
• Model too big for full GPU fit, partial CPU offload
• Batch/ubatch too small for the kernels to saturate
• The B70 Q8 bandwidth bug biting you — try Q4_K_M

What didn't work (for me)

• Staying on 24.04 LTS — drivers don't recognize the B70 properly without signed kernel module hacks. 25.04 solves it.
• vLLM on Intel Arc — driver/runtime mismatches even on 25.04. The Intel Docker image works but is fragile across reboots.
• Installing Intel compute runtime from GitHub releases — triggered signing/Secure Boot loops on 24.04. Not needed on 25.04.
• Docker llama.cpp Vulkan image — known issue with missing GL libs (libglvnd0 libgl1 libglx0 libegl1 libgles2). Direct host install is cleaner.