Intel Arc Pro B60
24 GB · 456 GB/s · 200 W
In short
The Intel Arc Pro B60 has 24 GB of GDDR6 memory with 456 GB/s of bandwidth, which sets how fast it generates text. At 64k context it runs 55 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 7 tok/s (estimated). The best new price for the Intel Arc Pro B60 in Canada is $985 at Amazon.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Canada, cheapest first. Buy links may earn us a commission.
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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 32.9 GB | 256k | 7tok/sest. | 432 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | fits | 42.3 GB | 204k256k with q8 cache | 25tok/sest. | 899 tok/s | chat, coding, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 32.4 GB | 256k | 7tok/sest. | 432 tok/s | – |
| Gemma 4 31B | Q8_0 | fits | 37.9 GB | 165k256k with q8 cache | 6tok/sest. | 394 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | fits | 40.5 GB | 275k436k with q8 cache | 32tok/sest. | 883 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 37.4 GB | 256k | 28tok/sest. | 2599 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 32.4 GB | 256k | 7tok/sest. | 432 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 28.1 GB | 256k | 22tok/sest. | 2661 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 37.4 GB | 256k | 28tok/sest. | 2599 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 34.6 GB | 198k | 14tok/sest. | 1289 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 35.0 GB | 134k244k with q8 cache | 6tok/sest. | 425 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 34.7 GB | 256k | 33tok/sest. | 3205 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q6_K | fits | 39.9 GB | 544k742k with q8 cache | 41tok/sest. | 2088 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.5 GB | 128k | 31tok/sest. | 1989 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 37.9 GB | 149k256k with q8 cache | 13tok/sest. | 930 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q6_K | fits | 42.7 GB | 40k | 5tok/sest. | 220 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 14.8 GB | 256k | 16tok/sest. | 1023 tok/s | chat, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.6 GB | 256k | 19tok/sest. | 1252 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q8_0 | fits | 33.8 GB | 128k | 7tok/sest. | 484 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 6tok/sest. | 425 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q6_K | fits | 42.7 GB | 78k128k with q8 cache | 5tok/sest. | 272 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 25.0 GB | 195k256k with q8 cache | 9tok/sest. | 599 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 26.3 GB | 40k | 9tok/sest. | 524 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 18.8 GB | 128k | 12tok/sest. | 828 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 18.8 GB | 40k | 12tok/sest. | 828 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 28.7 GB | 16k | 8tok/sest. | 526 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.9 GB | 256k | 34tok/sest. | 2170 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 18.6 GB | 256k | 13tok/sest. | 819 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 20.4 GB | 128k | 11tok/sest. | 757 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 11.1 GB | 128k | 23tok/sest. | 1640 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 13.1 GB | 125k | 29tok/sest. | 3500 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 15.8 GB | 128k | 15tok/sest. | 889 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.9 GB | 32k | 171tok/sest. | 7763 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.2 GB | 256k | 139tok/sest. | 8801 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.8 GB | 256k | 101tok/sest. | 4798 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.2 GB | 128k | 60tok/sest. | 3215 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.8 GB | 128k | 42tok/sest. | 1812 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.6 GB | 128k | 34tok/sest. | 3244 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 8.1 GB | 128k | 33tok/sest. | 2197 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 8.1 GB | 32k | 33tok/sest. | 2066 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 10.0 GB | 128k | 25tok/sest. | 1380 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 10.3 GB | 128k | 25tok/sest. | 1425 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 20.2 GB | 128k | 24tok/sest. | 2231 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.7 GB | 128k | 19tok/sest. | 1041 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.5 GB | 128k | 18tok/sest. | 703 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 30.4 GB | 128k | 17tok/sest. | 2005 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 15.1 GB | 64k | 16tok/sest. | 703 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 17.6 GB | 128k | 13tok/sest. | 889 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 18.6 GB | 256k | 13tok/sest. | 819 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 28.3 GB | 128k | 8tok/sest. | 479 tok/s | – |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 6tok/sest. | 431 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 35.0 GB | 40k | 6tok/sest. | 431 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 35.0 GB | 40k | 6tok/sest. | 431 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 6tok/sest. | 425 tok/s | – |
| granite 4.2 30b IBM | Q6_K | fits | 40.5 GB | 87k128k with q8 cache | 6tok/sest. | 318 tok/s | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 48.4 GB | – | 27tok/sest. | 2028 tok/s | chat, coding, agents, long documents |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.2 GB | – | 15tok/sest. | 961 tok/s | chat |
| gpt-oss 120B OpenAI | Q4_K_M | offload | 62.4 GB | – | 12tok/sest. | 1174 tok/s | chat |
| GLM 4.5 Air Z.ai | Q2_K | offload | 55.4 GB | 15k28k with q8 cache | 4tok/sest. | 246 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 48.4 GB | – | 32tok/sest. | 2313 tok/s | chat, coding, agents, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 61.3 GB | 17k31k with q8 cache | 2tok/sest. | 111 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 61.3 GB | 17k31k with q8 cache | 2tok/sest. | 111 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.7 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.7 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.3 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 383.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.7 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.5 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 147.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 72.4 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 427.1 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 441.0 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.5 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 584.1 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 255.9 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 93.4 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
Compare with
Questions
- How much memory does the Intel Arc Pro B60 have for AI models?
- The Intel Arc Pro B60 has 24 GB of GDDR6 memory with 456 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the Intel Arc Pro B60 can run locally?
- Qwen 3.8 27B (Q8_0) fits fully in its memory at 64k context and generates at about 7 tok/s, an estimate from its memory bandwidth. 55 of the 113 models we track fit at that context.
- Can the Intel Arc Pro B60 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 97 GB at 64k context, more than the Intel Arc Pro B60 and system RAM can hold.
- How much does the Intel Arc Pro B60 cost in Canada?
- The best new price for the Intel Arc Pro B60 in Canada is $985 at Amazon.
Speeds on this page are for 64k context. See the best hardware for local AI at every budget.