Intel Arc Pro B70
32 GB · 608 GB/s · 230 W
In short
The Intel Arc Pro B70 has 32 GB of GDDR6 memory with 608 GB/s of bandwidth, which sets how fast it generates text. At 32k context it runs 61 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 10 tok/s (estimated). The best new price for the Intel Arc Pro B70 in Canada is $2,884 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.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| AmazonIntel Arc Pro B70 Graphic Card | New | $2,884 | In stock | 51 min ago | Buy |
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 | Q8_0 | fits | 30.2 GB | 46k82k with q8 cache | 10tok/sest. | 222 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 29.8 GB | 53k92k with q8 cache | 10tok/sest. | 222 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 27.7 GB | 73k137k with q8 cache | 11tok/sest. | 197 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q6_K | fits | 30.5 GB | 59k93k with q8 cache | 50tok/sest. | 1582 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 29.8 GB | 53k92k with q8 cache | 10tok/sest. | 222 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 26.8 GB | 204k256k with q8 cache | 33tok/sest. | 1408 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 28.5 GB | 136k213k with q8 cache | 53tok/sest. | 1582 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 25.6 GB | 128k198k with q8 cache | 29tok/sest. | 826 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.3 GB | 43k79k with q8 cache | 10tok/sest. | 234 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 29.4 GB | 189k246k with q8 cache | 54tok/sest. | 1805 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.3 GB | 174k237k with q8 cache | 78tok/sest. | 1364 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.0 GB | 128k | 52tok/sest. | 1181 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 27.4 GB | 70k126k with q8 cache | 29tok/sest. | 728 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q5_K_M | fits | 30.6 GB | 34k40k with q8 cache | 10tok/sest. | 138 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.6 GB | 256k | 22tok/sest. | 514 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.9 GB | 256k | 29tok/sest. | 640 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.3 GB | 43k79k with q8 cache | 10tok/sest. | 234 tok/s | – |
| Gemma 3 27B | Q8_0 | fits | 30.6 GB | 38k72k with q8 cache | 10tok/sest. | 235 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q5_K_M | fits | 30.7 GB | 34k62k with q8 cache | 10tok/sest. | 158 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.4 GB | 105k191k with q8 cache | 15tok/sest. | 356 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.6 GB | 40k | 14tok/sest. | 320 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.6 GB | 128k | 22tok/sest. | 528 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.6 GB | 40k | 22tok/sest. | 528 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q8_0 | fits | 21.7 GB | 16k | 14tok/sest. | 322 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.2 GB | 256k | 54tok/sest. | 1198 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.6 GB | 158k256k with q8 cache | 22tok/sest. | 510 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.7 GB | 128k | 21tok/sest. | 485 tok/s | chat, coding |
| Gemma 4 E4B | Q8_0 | fits | 9.5 GB | 128k | 33tok/sest. | 805 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.9 GB | 125k | 63tok/sest. | 2632 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.1 GB | 128k | 28tok/sest. | 557 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.9 GB | 32k | 326tok/sest. | 5731 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.1 GB | 256k | 247tok/sest. | 5384 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.7 GB | 256k | 164tok/sest. | 2567 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.6 GB | 128k | 107tok/sest. | 2156 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.8 GB | 128k | 74tok/sest. | 1288 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.0 GB | 670k1024k with q8 cache | 71tok/sest. | 1264 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.5 GB | 128k | 62tok/sest. | 1459 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.5 GB | 32k | 62tok/sest. | 1337 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.3 GB | 256k | 53tok/sest. | 1198 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.5 GB | 128k | 53tok/sest. | 1900 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.9 GB | 128k | 48tok/sest. | 934 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.1 GB | 128k | 46tok/sest. | 979 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.6 GB | 251k256k with q8 cache | 43tok/sest. | 936 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 17.7 GB | 128k | 41tok/sest. | 1428 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.5 GB | 128k | 39tok/sest. | 1428 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.2 GB | 79k128k with q8 cache | 35tok/sest. | 1467 tok/s | chat, coding, agents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.9 GB | 32k | 32tok/sest. | 465 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.3 GB | 128k | 31tok/sest. | 630 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.6 GB | 128k | 27tok/sest. | 439 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.9 GB | 128k | 24tok/sest. | 557 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.2 GB | 64k | 23tok/sest. | 439 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.6 GB | 158k256k with q8 cache | 22tok/sest. | 510 tok/s | chat, coding |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.4 GB | 105k191k with q8 cache | 15tok/sest. | 356 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.6 GB | 81k128k with q8 cache | 14tok/sest. | 301 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.4 GB | 161k256k with q8 cache | 13tok/sest. | 222 tok/s | – |
| Muse Glimmer 30B meta-models | Q6_K | fits | 23.3 GB | 128k | 13tok/sest. | 214 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 28.6 GB | 42k78k with q8 cache | 10tok/sest. | 181 tok/s | – |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.3 GB | 43k79k with q8 cache | 10tok/sest. | 237 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.3 GB | 40k | 10tok/sest. | 237 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.3 GB | 40k | 10tok/sest. | 237 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.3 GB | 43k79k with q8 cache | 10tok/sest. | 234 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.8 GB | – | 18tok/sest. | 444 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.0 GB | – | 20tok/sest. | 994 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.3 GB | – | 10tok/sest. | 396 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.1 GB | – | 27tok/sest. | 506 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 48.9 GB | – | 5tok/sest. | 167 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.0 GB | – | 24tok/sest. | 1183 tok/s | chat, coding |
| Llama 3.3 70B Meta | Q4_K_M | offload | 50.6 GB | – | 2tok/sest. | 57 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 50.6 GB | – | 2tok/sest. | 57 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 | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.6 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 Zero DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 379.8 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 V3.1 Terminus 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 | Q4_K_M | won't run | 81.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.1 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.0 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.3 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 Z.ai | MXFP4 | won't run | 386.1 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 Dev 72B Moonshot AI | Q6_K | won't run | 70.9 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 | MXFP4 | won't run | 244.4 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 B70
- Memory
- 32 GB GDDR6
- Memory bus
- 256-bit
- Memory bandwidth
- 608 GB/s
- FP16 compute
- 45.8 TFLOPS
- Software
- SYCL / oneAPI
- Board power
- 230 W
- PCIe generation
- Gen 5
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the Intel Arc Pro B70 have for AI models?
- The Intel Arc Pro B70 has 32 GB of GDDR6 memory with 608 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the Intel Arc Pro B70 can run locally?
- Qwen 3.8 27B (Q8_0) fits fully in its memory at 32k context and generates at about 10 tok/s, an estimate from its memory bandwidth. 61 of the 125 models we track fit at that context.
- Can the Intel Arc Pro B70 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 B70 and system RAM can hold.
- How much does the Intel Arc Pro B70 cost in Canada?
- The best new price for the Intel Arc Pro B70 in Canada is $2,884 at Amazon.
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.