Nvidia RTX 3090 vs Intel Arc Pro B70 llama.cpp Benchmarks

Reddit r/LocalLLaMA / 4/23/2026

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Key Points

  • The article compares llama.cpp performance on the same NixOS setup using three paths: RTX 3090 (Vulkan), Intel Arc Pro B70 (Vulkan), and Intel Arc Pro B70 (SYCL via a Docker container).
  • Across multiple GGUF models at pp512 prompt processing, the RTX 3090 generally delivers much higher throughput than the Arc Pro B70 using Vulkan, often by roughly 60–75% depending on the model.
  • For Arc Pro B70 specifically, SYCL execution in Docker is close to or moderately faster than Vulkan for some models, with a few cases showing notable SYCL gains (e.g., one configuration improves by about +50% to +23% in the shown rows).
  • There is at least one apparent failure/instability for the RTX 3090 path on a larger Qwen3-Coder 30B entry (“oom”), while the Arc B70 paths still produce results, indicating capacity/fit differences alongside speed.

Just sharing the results from experimenting with the B70 on my setup....

These results compare three llama.cpp execution paths on the same machine:

  • RTX 3090 (Vulkan) on NixOS host, using main llama.cpp repo (compiled on 4/21/2026)
  • Arc Pro B70 (Vulkan) on NixOS host, using main llama.cpp repo (compiled on 4/21/2026)
  • Arc Pro B70 (SYCL) inside an Ubuntu 24.04 Docker container, using a separate SYCL-enabled llama-bench build from the aicss-genai/llama.cpp fork

Prompt processing (pp512)

model RTX 3090 (Vulkan) Arc Pro B70 (Vulkan) Arc Pro B70 (SYCL) B70 best vs 3090 B70 SYCL vs B70 Vulkan
TheBloke/Llama-2-7B-GGUF:Q4_K_M 4550.27 ± 10.90 1236.65 ± 3.19 1178.54 ± 5.74 -72.8% -4.7%
unsloth/gemma-4-E2B-it-GGUF:Q4_K_XL 9359.15 ± 168.11 2302.80 ± 5.26 3462.19 ± 36.07 -63.0% +50.3%
unsloth/gemma-4-26B-A4B-it-GGUF:Q4_K_M 3902.28 ± 21.37 1126.28 ± 6.17 945.89 ± 17.53 -71.1% -16.0%
unsloth/gemma-4-31B-it-GGUF:Q4_K_XL 991.47 ± 1.73 295.66 ± 0.60 268.50 ± 0.65 -70.2% -9.2%
ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF:Q8_0 4740.04 ± 13.78 1176.34 ± 1.68 1192.99 ± 5.75 -74.8% +1.4%
ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF:Q8_0 oom 990.32 ± 5.34 552.37 ± 5.76 -44.2%
Qwen/Qwen3-8B-GGUF:Q8_0 4195.89 ± 41.31 1048.39 ± 2.66 1098.90 ± 1.02 -73.8% +4.8%
unsloth/Qwen3.5-4B-GGUF:Q4_K_XL 5233.55 ± 8.29 1430.72 ± 9.68 1767.21 ± 21.27 -66.2% +23.5%
unsloth/Qwen3.5-35B-A3B-GGUF:Q4_K_M 3357.03 ± 18.47 886.39 ± 6.14 445.56 ± 7.46 -73.6% -49.7%
unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M 3417.76 ± 17.84 878.15 ± 5.32 442.01 ± 6.51 -74.3% -49.7%
Average (excluding oom) -71.1%

Token generation (tg128)

model RTX 3090 (Vulkan) Arc Pro B70 (Vulkan) Arc Pro B70 (SYCL) B70 best vs 3090 B70 SYCL vs B70 Vulkan
TheBloke/Llama-2-7B-GGUF:Q4_K_M 137.92 ± 0.41 58.61 ± 0.09 92.39 ± 0.30 -33.0% +57.6%
unsloth/gemma-4-E2B-it-GGUF:Q4_K_XL 207.21 ± 2.00 89.33 ± 0.60 70.65 ± 0.84 -56.9% -20.9%
unsloth/gemma-4-26B-A4B-it-GGUF:Q4_K_M 131.33 ± 0.14 42.00 ± 0.01 37.75 ± 0.32 -68.0% -10.1%
unsloth/gemma-4-31B-it-GGUF:Q4_K_XL 31.49 ± 0.05 14.49 ± 0.04 18.30 ± 0.05 -41.9% +26.3%
ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF:Q8_0 98.96 ± 0.56 21.30 ± 0.03 55.37 ± 0.02 -44.1% +160.0%
ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF:Q8_0 oom 37.69 ± 0.03 28.58 ± 0.09 -24.2%
Qwen/Qwen3-8B-GGUF:Q8_0 92.29 ± 0.17 19.78 ± 0.01 50.74 ± 0.02 -45.0% +156.5%
unsloth/Qwen3.5-4B-GGUF:Q4_K_XL 162.58 ± 0.76 60.45 ± 0.06 79.09 ± 0.05 -51.4% +30.8%
unsloth/Qwen3.5-35B-A3B-GGUF:Q4_K_M 148.01 ± 0.38 43.30 ± 0.05 37.93 ± 0.89 -70.7% -12.4%
unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_M 148.64 ± 0.53 43.46 ± 0.02 36.87 ± 0.42 -70.8% -15.2%
Average (excluding oom) -53.5%

Commands used

Host Vulkan runs

For each model, the host benchmark commands were:

llama-bench -hf <MODEL> -dev Vulkan0 llama-bench -hf <MODEL> -dev Vulkan2 

Where:

  • Vulkan0 = RTX 3090
  • Vulkan2 = Arc Pro B70

Container SYCL runs

For each model, the SYCL benchmark was run inside the Docker container with:

./build/bin/llama-bench -hf <MODEL> -dev SYCL0 

Where:

  • SYCL0 = Arc Pro B70

Test machine

  • CPU: AMD Ryzen Threadripper 2970WX 24-Core Processor
    • 24 cores / 48 threads
    • 1 socket
    • 2.2 GHz min / 3.0 GHz max
  • RAM: 128 GiB total
  • GPUs:
    • NVIDIA GeForce RTX 3090, 24 GiB
    • NVIDIA GeForce RTX 3090, 24 GiB
    • Intel Arc Pro B70, 32 GiB
submitted by /u/tovidagaming
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