Intel Arc Pro B50 for local LLMs
Written by Jakub Rusinowski · Last updated
With 16 GB of GDDR6 at 224 GB/s, the Intel Arc Pro B50 runs 74 catalogued models at Q4_K_M with 8K context. The largest that fits is OLMo 2 13B Instruct (~15.8 GB), and the top pick is GPT-OSS 20B at 28–58 tok/s.
16 GB of GDDR6 on a 128-bit bus at 70 W, powered from the slot. 224 GB/s, so it holds 14B-class models but runs them slowly.
Models that run on the Intel Arc Pro B50
Q4_K_M, 8K context, 16 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 16 GB | VRAM | Speed |
|---|---|---|---|
| GPT-OSS 20B GPT-OSS | 13.8 GB | 28–58 tok/s | |
| Cosmos 3 Nano Cosmos 3 | 10.5 GB | 13–27 tok/s | |
| Phi-4 (14B) Phi-4 Family | 10.9 GB | 8.2–17 tok/s | |
| Gemma 4 12B (Unified) Gemma 4 | 8 GB | 9.6–20 tok/s | |
| Bielik PL 11B v3.0 Instruct Bielik | 7.4 GB | 10–21 tok/s | |
| Llama 3.2 Vision 11B Llama 3.2 Vision | 8.5 GB | 11–22 tok/s | |
| Falcon 3 10B Instruct Falcon 3 | 8.4 GB | 11–22 tok/s | |
| Qwen 3.5 9B Qwen 3.5 | 6.2 GB | 12–25 tok/s | |
| Ministral 3 14B Ministral 3 | 9.3 GB | 8.4–18 tok/s | |
| Cogito v1 14B Cogito v1 | 10.9 GB | 8.2–17 tok/s | |
| Gemma 4 E2B Gemma 4 | 3.9 GB | 19–39 tok/s | |
| Gemma 4 E4B Gemma 4 | 5.6 GB | 13–28 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
or compare on Vast.ai from $0.35/hr (typical low · varies)
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-10-07.
- Memory
- 16 GB GDDR6
- Memory bandwidth
- 224 GB/s
- Memory bus
- 128-bit
- Architecture
- Xe2 Battlemage BMG-G21
- Series
- Arc Pro B-series
- Board power
- 70 W
- Release year
- 2025
- Launch price
- $349
- Compute backends
- SYCL, VULKAN
- Usable for models
- 16 GB
Similar GPUs
Frequently asked questions
Can the Intel Arc Pro B50 run local LLMs?
Yes. With 16 GB (16 GB usable by a model) it runs 74 of the catalogued models at Q4_K_M with 8K context; the largest is OLMo 2 13B Instruct, needing about 15.8 GB.
How fast is the Intel Arc Pro B50 for AI inference?
It is estimated to run Llama 3.1 8B at 15–31 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 16 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 16 GB?
Among the best that fit: GPT-OSS 20B, Cosmos 3 Nano, Phi-4 (14B), Gemma 4 12B (Unified), Bielik PL 11B v3.0 Instruct.