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 8k context it runs 51 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at UD-Q6_K, 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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q6_K | fits | 22.0 GB | 26k45k with q8 cache | 10tok/sest. | 517 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q6_K | fits | 22.5 GB | 18k32k with q8 cache | 10tok/sest. | 517 tok/s | – |
| Gemma 4 31B | Q5_K_M | fits | 22.4 GB | 17k38k with q8 cache | 10tok/sest. | 451 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q4_K_M | fits | 21.8 GB | 62k97k with q8 cache | 60tok/sest. | 4430 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q6_K | fits | 22.4 GB | 19k34k with q8 cache | 10tok/sest. | 517 tok/s | – |
| Gemma 4 26B-A4B | UD-Q6_K | fits | 22.8 GB | 24k44k with q8 cache | 32tok/sest. | 3375 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | fits | 21.6 GB | 72k113k with q8 cache | 61tok/sest. | 4430 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 21.2 GB | 42k73k with q8 cache | 31tok/sest. | 2479 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q6_K | fits | 20.1 GB | 27k49k with q8 cache | 11tok/sest. | 583 tok/s | – |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.3 GB | 128k | 48tok/sest. | 3206 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q5_K_M | fits | 21.8 GB | 21k38k with q8 cache | 43tok/sest. | 3205 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 21.3 GB | 15k28k with q8 cache | 10tok/sest. | 399 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.1 GB | 256k | 17tok/sest. | 1176 tok/s | chat, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.0 GB | 256k | 23tok/sest. | 1489 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q6_K | fits | 20.1 GB | 27k49k with q8 cache | 11tok/sest. | 583 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 21.3 GB | 15k28k with q8 cache | 10tok/sest. | 417 tok/s | – |
| Gemma 3 27B | Q6_K | fits | 22.5 GB | 16k32k with q8 cache | 10tok/sest. | 524 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 15.5 GB | 55k101k with q8 cache | 15tok/sest. | 967 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 16.7 GB | 40k | 13tok/sest. | 898 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.1 GB | 97k128k with q8 cache | 23tok/sest. | 1572 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 40k | 23tok/sest. | 1572 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 16.9 GB | 16k | 13tok/sest. | 904 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.3 GB | 256k | 48tok/sest. | 2998 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.3 GB | 100k183k with q8 cache | 22tok/sest. | 1469 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 10.8 GB | 84k128k with q8 cache | 21tok/sest. | 1458 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 8.7 GB | 128k | 27tok/sest. | 1805 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.6 GB | 125k | 86tok/sest. | 10534 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 8.0 GB | 124k128k with q8 cache | 30tok/sest. | 1623 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.4 GB | 32k | 361tok/sest. | 22046 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 256k | 245tok/sest. | 15168 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.3 GB | 256k | 147tok/sest. | 6222 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.8 GB | 128k | 107tok/sest. | 6939 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.7 GB | 128k | 75tok/sest. | 4587 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.0 GB | 128k | 68tok/sest. | 4616 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.0 GB | 32k | 68tok/sest. | 4074 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.8 GB | 128k | 53tok/sest. | 3055 tok/s | chat, coding, long documents |
| LFM2.5 24B A2B Liquid AI | Q6_K | fits | 21.2 GB | 32k56k with q8 cache | 52tok/sest. | 5532 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.1 GB | 128k | 50tok/sest. | 3284 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.0 GB | 128k | 45tok/sest. | 5076 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 17.7 GB | 128k | 36tok/sest. | 4279 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 8.8 GB | 128k | 27tok/sest. | 1751 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 9.8 GB | 110k128k with q8 cache | 24tok/sest. | 1623 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.3 GB | 100k183k with q8 cache | 22tok/sest. | 1469 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.5 GB | 128k | 22tok/sest. | 1270 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.1 GB | 64k | 19tok/sest. | 1270 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 17.0 GB | 40k73k with q8 cache | 13tok/sest. | 880 tok/s | – |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 17.7 GB | 41k76k with q8 cache | 13tok/sest. | 593 tok/s | – |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 17.7 GB | 40k | 13tok/sest. | 593 tok/s | – |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 17.7 GB | 41k76k with q8 cache | 13tok/sest. | 583 tok/s | – |
| Magistral Small 2506 Mistral AI | Q6_K | fits | 20.1 GB | 27k40k with q8 cache | 11tok/sest. | 593 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 22.4 GB | 11k20k with q8 cache | 10tok/sest. | 470 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.1 GB | – | 14tok/sest. | 1014 tok/s | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 46.3 GB | – | 18tok/sest. | 2401 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.5 GB | – | 21tok/sest. | 1352 tok/s | chat |
| GLM 4.5 Air Z.ai | Q2_K | offload | 44.5 GB | – | 6tok/sest. | 482 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 46.3 GB | – | 23tok/sest. | 3019 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.5 GB | – | 18tok/sest. | 3740 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.9 GB | – | 39tok/sest. | 3694 tok/s | chat, coding, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 42.9 GB | – | 2tok/sest. | 140 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 42.9 GB | – | 2tok/sest. | 140 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 91.7 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.3 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 72.9 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.0 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.6 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.0 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.5 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.0 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 378.9 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 378.9 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 155.9 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 127.3 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.5 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 67.9 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 747.9 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.5 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.6 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.6 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.1 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 248.5 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.4 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 (UD-Q6_K) fits fully in its memory at 8k context and generates at about 10 tok/s, an estimate from its memory bandwidth. 51 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 92 GB at 8k 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 8k context. See the best hardware for local AI at every budget.