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 128k context it runs 23 of the 113 open models we track fully in memory; the most capable is Gemma 4 26B-A4B at Q5_K_M, at about 21 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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Gemma 4 26B-A4B | Q5_K_M | fits | 22.2 GB | 165k256k with q8 cache | 21tok/sest. | 2143 tok/s | chat, coding, agents, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 15.8 GB | 128k | 22tok/sest. | 1388 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 15.7 GB | 256k | 15tok/sest. | 891 tok/s | chat, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.5 GB | 256k | 16tok/sest. | 1059 tok/s | chat, coding, long documents |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 9.8 GB | 256k | 26tok/sest. | 1650 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 12.7 GB | 128k | 19tok/sest. | 1485 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 15.9 GB | 125k | 17tok/sest. | 1985 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q5_K_M | fits | 22.9 GB | 128k | 10tok/sest. | 586 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.5 GB | 32k | 107tok/sest. | 4460 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 256k | 93tok/sest. | 5948 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.4 GB | 256k | 74tok/sest. | 3803 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.8 GB | 128k | 40tok/sest. | 1993 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.4 GB | 128k | 27tok/sest. | 2296 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.3 GB | 128k | 27tok/sest. | 1071 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 11.7 GB | 128k | 21tok/sest. | 1374 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 11.7 GB | 32k | 21tok/sest. | 1322 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 21.9 GB | 128k | 17tok/sest. | 1442 tok/s | chat, coding, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 14.9 GB | 128k | 16tok/sest. | 848 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 15.2 GB | 128k | 16tok/sest. | 865 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 15.9 GB | 128k | 15tok/sest. | 465 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.1 GB | 128k | 15tok/sest. | 711 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.5 GB | 64k | 13tok/sest. | 465 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q5_K_M | fits | 22.9 GB | 128k | 10tok/sest. | 586 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.0 GB | 101k177k with q8 cache | 7tok/sest. | 346 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 43.7 GB | – | 7tok/sest. | 457 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.4 GB | 96k168k with q8 cache | 7tok/sest. | 344 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.4 GB | 54k103k with q8 cache | 4tok/sest. | 299 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 41.8 GB | – | 9tok/sest. | 405 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.3 GB | 83k130k with q8 cache | 24tok/sest. | 1713 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.3 GB | 97k170k with q8 cache | 7tok/sest. | 344 tok/s | – |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.6 GB | 72k113k with q8 cache | 23tok/sest. | 1701 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.2 GB | 92k160k with q8 cache | 10tok/sest. | 790 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 3tok/sest. | 257 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.9 GB | – | 14tok/sest. | 1874 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.6 GB | – | 23tok/sest. | 1123 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 30.8 GB | 51k91k with q8 cache | 5tok/sest. | 433 tok/s | chat, coding |
| Gemma 3 27B | Q4_K_M | offload | 27.4 GB | 78k128k with q8 cache | 5tok/sest. | 402 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 3tok/sest. | 257 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.2 GB | 90k165k with q8 cache | 5tok/sest. | 363 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 29.9 GB | 40k | 4tok/sest. | 305 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.2 GB | 120k128k with q8 cache | 8tok/sest. | 522 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.2 GB | 40k | 8tok/sest. | 522 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q4_K_M | offload | 34.8 GB | 16k | 3tok/sest. | 281 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.4 GB | 126k229k with q8 cache | 10tok/sest. | 541 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q4_K_M | offload | 26.7 GB | 106k128k with q8 cache | 6tok/sest. | 447 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.4 GB | 126k229k with q8 cache | 10tok/sest. | 541 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 25.8 GB | 96k128k with q8 cache | 8tok/sest. | 1084 tok/s | chat, coding, agents |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 33.9 GB | 72k128k with q8 cache | 3tok/sest. | 252 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 3tok/sest. | 259 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 3tok/sest. | 259 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 3tok/sest. | 259 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 34.9 GB | 55k101k with q8 cache | 3tok/sest. | 257 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.5 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.3 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.1 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 102.7 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 49.8 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.5 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 66.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 49.8 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 52.0 GB | 15k28k with q8 cache | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 52.0 GB | 15k28k with q8 cache | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.2 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 386.8 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.3 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.2 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 386.8 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 387.7 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 387.7 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 157.9 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 166.7 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 169.3 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.1 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 758.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.3 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 588.7 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.4 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.4 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 147.8 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 263.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.3 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.3 GB | 21k40k with q8 cache | – | – | – |
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?
- Gemma 4 26B-A4B (Q5_K_M) fits fully in its memory at 128k context and generates at about 21 tok/s, an estimate from its memory bandwidth. 23 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 102 GB at 128k 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 128k context. See the best hardware for local AI at every budget.