Intel Arc Pro B70 — Local LLM Performance & Compatibility

作者: Jakub Rusinowski · 最后更新: 2026年9月19日

32 GB GDDR6、608 GB/s,官方定价949美元——最便宜的全新32 GB显卡,约为同容量NVIDIA产品的三分之一。问题在软件:llama.cpp的SYCL/Vulkan路径可用,但落后于CUDA,所以这是为容量而买,不是为速度而买。上市以来实际售价一直明显高于定价。

Technical Specifications

VRAM32 GB
Memory Bandwidth608 GB/s
TDP225 W
ArchitectureXe2 Battlemage BMG-G31
Release Year2026
MSRP at Launch$949
Inference Speed (Llama 3.1 8B Q4_K_M)35–72 tok/s (estimated)
Inference Speed (Llama 3.3 70B Q4_K_M)Does not fit — needs ~44 GB of 32 GB usable
购买此硬件 Apple Mac mini M4 (16GB) — 32 GB VRAM · 22 W board power立即云端部署 RunPod 上的 NVIDIA A40 — 低至 $0.44/小时 · 价格核实于 2026-08

或在 Vast.ai 比较

作为亚马逊联盟成员,我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金,您无需额外付费。

LLMs Compatible with 32 GB VRAM

All models below run comfortably in 32 GB VRAM with Q4_K_M quantization.

Command R FamilyCommand R (35B) · 22 GB VRAM · Q4_K_M · ollama run command-r
Qwen 3.5Qwen 3.5 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.5:35b-a3b
Qwen 3.6Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b
Nex-N2Nex-N2 mini · 22 GB VRAM · Q4_K_M · nex-n2
Yi 1.5 FamilyYi 1.5 34B Chat · 22 GB VRAM · Q4_K_M · ollama run yi:34b
Qwen 3Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b
Aya ExpanseAya Expanse 32B · 20 GB VRAM · Q4_K_M · ollama run aya-expanse:32b
DeepSeek R1DeepSeek R1 Distill Qwen 32B · 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b

56 more families also fit 32 GB — browse the full model library.

Best Use Cases

FAQ

Can the Intel Arc Pro B70 run local LLMs?

Yes — the Intel Arc Pro B70 has 32 GB VRAM and runs 32 GB GDDR6、608 GB/s,官方定价949美元——最便宜的全新32 GB显卡,约为同容量NVIDIA产品的三分之一。问题在软件:llama.cpp的SYCL/Vulkan路径可用,但落后于CUDA,所以这是为容量而买,不是为速

How fast is the Intel Arc Pro B70 for AI inference?

The Intel Arc Pro B70 is estimated to run Llama 3.1 8B at 35–72 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 32 GB usable. These are modelled estimates, not measurements — see /en/methodology.

What LLMs can I run on 32 GB VRAM?

With 32 GB you can run: Command R Family, Qwen 3.5, Qwen 3.6, Nex-N2, Yi 1.5 Family. Use Ollama for the easiest setup: ollama run llama3.1:8b.

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