Intel Arc Pro B60
24 GB · 456 GB/s · 200 W
Prices
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What two of them run
Every model in the catalogue, with the best quant for this hardware. Estimates, not measurements.
| Model | Quant | Fits | Memory | Max context | Decode @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.7 GB | 256k | 7tok/sest. | 477 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | fits | 41.3 GB | 204k256k with q8 cache | 29tok/sest. | 1118 tok/s | chat, coding, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 256k | 7tok/sest. | 477 tok/s | – |
| Gemma 4 31B | Q8_0 | fits | 35.2 GB | 165k256k with q8 cache | 6tok/sest. | 425 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | fits | 39.6 GB | 275k436k with q8 cache | 40tok/sest. | 1242 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.5 GB | 256k | 33tok/sest. | 3403 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 256k | 7tok/sest. | 477 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.3 GB | 256k | 25tok/sest. | 3027 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.5 GB | 256k | 33tok/sest. | 3403 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 32.8 GB | 198k | 18tok/sest. | 1777 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 134k244k with q8 cache | 8tok/sest. | 503 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 34.3 GB | 256k | 35tok/sest. | 3883 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q6_K | fits | 39.5 GB | 544k742k with q8 cache | 45tok/sest. | 2933 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.5 GB | 128k | 39tok/sest. | 2540 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 34.7 GB | 149k256k with q8 cache | 20tok/sest. | 1565 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q8_0 | fits | 41.9 GB | 40k | 5tok/sest. | 296 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 14.1 GB | 256k | 17tok/sest. | 1105 tok/s | chat, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.4 GB | 256k | 21tok/sest. | 1377 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 128k | 8tok/sest. | 503 tok/s | – |
| Gemma 3 27B | Q8_0 | fits | 31.1 GB | 128k | 7tok/sest. | 506 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q8_0 | fits | 41.9 GB | 49k91k with q8 cache | 5tok/sest. | 340 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.9 GB | 195k256k with q8 cache | 12tok/sest. | 765 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 21.1 GB | 40k | 11tok/sest. | 688 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 14.1 GB | 128k | 17tok/sest. | 1135 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 14.1 GB | 40k | 17tok/sest. | 1135 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.2 GB | 16k | 10tok/sest. | 691 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.7 GB | 256k | 41tok/sest. | 2577 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 14.1 GB | 256k | 17tok/sest. | 1096 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.2 GB | 128k | 16tok/sest. | 1043 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 10.0 GB | 128k | 25tok/sest. | 1730 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.4 GB | 125k | 47tok/sest. | 5660 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.6 GB | 128k | 21tok/sest. | 1199 tok/s | chat, coding |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 36tok/sest. | 2655 tok/s | chat, coding, agents, long documents |
| gpt-oss 120B OpenAI | MXFP4 | offload | 61.7 GB | – | 16tok/sest. | 1531 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.8 GB | – | 16tok/sest. | 1046 tok/s | chat |
| GLM 4.5 Air Z.ai | Q2_K | offload | 49.4 GB | 15k28k with q8 cache | 8tok/sest. | 400 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 44tok/sest. | 3159 tok/s | chat, coding, agents, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 51.1 GB | 17k31k with q8 cache | 3tok/sest. | 151 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.4 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.8 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.4 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- Arc Pro B60
- Memory
- 24 GB GDDR6
- Memory bus
- 192-bit
- Memory bandwidth
- 456 GB/s
- FP16 compute
- 98.5 TFLOPS
- Software
- SYCL / oneAPI
- Board power
- 200 W
- PCIe generation
- Gen 5
- PCIe lanes
- x8