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 32k context it runs 48 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q5_K_M, at about 10 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 it runs
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 | Q5_K_M | fits | 22.6 GB | 40k70k with q8 cache | 10tok/sest. | 477 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 21.3 GB | 59k104k with q8 cache | 11tok/sest. | 477 tok/s | – |
| Gemma 4 31B | Q4_K_M | fits | 21.3 GB | 54k103k with q8 cache | 10tok/sest. | 425 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.4 GB | 62k97k with q8 cache | 48tok/sest. | 3403 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 21.4 GB | 58k102k with q8 cache | 10tok/sest. | 477 tok/s | – |
| Gemma 4 26B-A4B | Q6_K | fits | 23.1 GB | 35k61k with q8 cache | 28tok/sest. | 3027 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.2 GB | 72k113k with q8 cache | 49tok/sest. | 3403 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 22.6 GB | 42k73k with q8 cache | 24tok/sest. | 1777 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q5_K_M | fits | 21.6 GB | 41k76k with q8 cache | 10tok/sest. | 503 tok/s | – |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.0 GB | 128k | 39tok/sest. | 2540 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | fits | 21.3 GB | 51k91k with q8 cache | 25tok/sest. | 1565 tok/s | chat, coding |
| Gemma 4 12B | Q8_0 | fits | 13.6 GB | 256k | 17tok/sest. | 1105 tok/s | chat, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.9 GB | 256k | 21tok/sest. | 1377 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q5_K_M | fits | 21.6 GB | 41k76k with q8 cache | 10tok/sest. | 503 tok/s | – |
| Gemma 3 27B | Q5_K_M | fits | 21.8 GB | 48k89k with q8 cache | 10tok/sest. | 506 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.4 GB | 55k101k with q8 cache | 12tok/sest. | 765 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.6 GB | 40k | 11tok/sest. | 688 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.6 GB | 97k128k with q8 cache | 17tok/sest. | 1135 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.6 GB | 40k | 17tok/sest. | 1135 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 21.7 GB | 16k | 10tok/sest. | 691 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.2 GB | 256k | 41tok/sest. | 2577 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.6 GB | 100k183k with q8 cache | 17tok/sest. | 1096 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.7 GB | 84k128k with q8 cache | 16tok/sest. | 1043 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.5 GB | 128k | 25tok/sest. | 1730 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.9 GB | 125k | 47tok/sest. | 5660 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.1 GB | 124k128k with q8 cache | 21tok/sest. | 1199 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.9 GB | 32k | 244tok/sest. | 12326 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.1 GB | 256k | 185tok/sest. | 11578 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.7 GB | 256k | 123tok/sest. | 5520 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.6 GB | 128k | 81tok/sest. | 4637 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.8 GB | 128k | 56tok/sest. | 2769 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.5 GB | 128k | 47tok/sest. | 3137 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.5 GB | 32k | 47tok/sest. | 2876 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.5 GB | 128k | 40tok/sest. | 4086 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.9 GB | 128k | 36tok/sest. | 2010 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.1 GB | 128k | 35tok/sest. | 2106 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.5 GB | 128k | 29tok/sest. | 3071 tok/s | chat, coding, long documents |
| LFM2.5 24B A2B Liquid AI | Q6_K | fits | 23.2 GB | 32k56k with q8 cache | 28tok/sest. | 3154 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.3 GB | 128k | 23tok/sest. | 1355 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.6 GB | 128k | 20tok/sest. | 943 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.9 GB | 110k128k with q8 cache | 18tok/sest. | 1199 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.2 GB | 64k | 17tok/sest. | 943 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.6 GB | 100k183k with q8 cache | 17tok/sest. | 1096 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.6 GB | 40k73k with q8 cache | 10tok/sest. | 647 tok/s | – |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 21.6 GB | 41k76k with q8 cache | 10tok/sest. | 511 tok/s | – |
| Magistral Small 2506 Mistral AI | Q5_K_M | fits | 21.6 GB | 40k | 10tok/sest. | 511 tok/s | – |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 21.6 GB | 40k | 10tok/sest. | 511 tok/s | – |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 21.6 GB | 41k76k with q8 cache | 10tok/sest. | 503 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.8 GB | – | 12tok/sest. | 821 tok/s | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.0 GB | – | 15tok/sest. | 1858 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.1 GB | – | 17tok/sest. | 931 tok/s | chat |
| GLM 4.5 Air Z.ai | Q2_K | offload | 48.9 GB | – | 4tok/sest. | 282 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.0 GB | – | 18tok/sest. | 2210 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.8 GB | – | 17tok/sest. | 3129 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.3 GB | – | 35tok/sest. | 2555 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.4 GB | 15k28k with q8 cache | 5tok/sest. | 268 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.5 GB | 15k28k with q8 cache | 5tok/sest. | 307 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 25.8 GB | 21k40k with q8 cache | 6tok/sest. | 366 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.9 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.7 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.3 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.9 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.6 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.6 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 50.6 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 380.7 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 380.7 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.3 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.1 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 135.7 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.0 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.3 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.1 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 423.7 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.6 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.1 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.3 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.6 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.6 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.0 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 251.5 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 81.7 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 (Q5_K_M) fits fully in its memory at 32k context and generates at about 10 tok/s, an estimate from its memory bandwidth. 48 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 94 GB at 32k 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 32k context. See the best hardware for local AI at every budget.