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 128k context it runs 50 of the 125 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). We have no current price for the Intel Arc Pro B70 in the UK.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the UK, cheapest first. Buy links may earn us a commission.
No retailer in this market lists it right now.
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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q6_K | fits | 30.2 GB | 143k250k with q8 cache | 10tok/sest. | 169 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q6_K | fits | 30.7 GB | 135k237k with q8 cache | 10tok/sest. | 169 tok/s | – |
| Gemma 4 31B | Q4_K_M | fits | 29.4 GB | 149k256k with q8 cache | 10tok/sest. | 160 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q5_K_M | fits | 28.7 GB | 224k256k with q8 cache | 34tok/sest. | 821 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q6_K | fits | 30.6 GB | 136k239k with q8 cache | 10tok/sest. | 169 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 29.3 GB | 204k256k with q8 cache | 24tok/sest. | 997 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 31.0 GB | 136k213k with q8 cache | 32tok/sest. | 821 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 31.2 GB | 128k198k with q8 cache | 15tok/sest. | 387 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 30.5 GB | 189k246k with q8 cache | 45tok/sest. | 1104 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 30.6 GB | 174k237k with q8 cache | 57tok/sest. | 616 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 15.8 GB | 128k | 30tok/sest. | 645 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | fits | 30.8 GB | 131k235k with q8 cache | 11tok/sest. | 239 tok/s | chat, coding, agents |
| Gemma 4 12B | Q8_0 | fits | 15.7 GB | 256k | 20tok/sest. | 414 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.5 GB | 256k | 22tok/sest. | 493 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q5_K_M | fits | 29.9 GB | 128k | 10tok/sest. | 207 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 29.9 GB | 40k | 10tok/sest. | 165 tok/s | chat, coding |
| Ministral 3 14B Mistral AI | Q5_K_M | fits | 30.5 GB | 132k241k with q8 cache | 10tok/sest. | 194 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 27.7 GB | 128k | 11tok/sest. | 250 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 27.7 GB | 40k | 11tok/sest. | 250 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 9.8 GB | 256k | 34tok/sest. | 767 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 27.0 GB | 158k256k with q8 cache | 11tok/sest. | 253 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 30.3 GB | 128k | 10tok/sest. | 227 tok/s | chat, coding |
| Gemma 4 E4B | Q8_0 | fits | 12.7 GB | 128k | 25tok/sest. | 691 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 15.9 GB | 125k | 22tok/sest. | 923 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 23.7 GB | 128k | 13tok/sest. | 273 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.5 GB | 32k | 142tok/sest. | 2074 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 256k | 125tok/sest. | 2766 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.4 GB | 256k | 99tok/sest. | 1769 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.8 GB | 128k | 54tok/sest. | 927 tok/s | chat, coding, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 8.9 GB | 670k1024k with q8 cache | 38tok/sest. | 759 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.3 GB | 128k | 37tok/sest. | 498 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.4 GB | 128k | 37tok/sest. | 1068 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 9.9 GB | 256k | 34tok/sest. | 767 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 11.7 GB | 128k | 28tok/sest. | 639 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 11.7 GB | 32k | 28tok/sest. | 615 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 21.2 GB | 128k | 23tok/sest. | 670 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 21.9 GB | 128k | 23tok/sest. | 670 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.2 GB | 32k | 22tok/sest. | 223 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 14.9 GB | 128k | 21tok/sest. | 394 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 15.2 GB | 128k | 21tok/sest. | 402 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 15.9 GB | 128k | 20tok/sest. | 216 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.1 GB | 128k | 20tok/sest. | 331 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.5 GB | 64k | 18tok/sest. | 216 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 18.0 GB | 251k256k with q8 cache | 17tok/sest. | 386 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q5_K_M | fits | 28.8 GB | 128k | 14tok/sest. | 539 tok/s | chat, coding, agents |
| Muse Glimmer 30B meta-models | Q6_K | fits | 25.1 GB | 128k | 12tok/sest. | 184 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 25.5 GB | 128k | 12tok/sest. | 273 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 27.0 GB | 158k256k with q8 cache | 11tok/sest. | 253 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 28.9 GB | 161k256k with q8 cache | 10tok/sest. | 169 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q5_K_M | fits | 30.5 GB | 132k241k with q8 cache | 10tok/sest. | 194 tok/s | chat, coding |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 43.7 GB | – | 11tok/sest. | 247 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 49.8 GB | – | 11tok/sest. | 512 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.6 GB | – | 9tok/sest. | 313 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 41.8 GB | – | 14tok/sest. | 219 tok/s | chat, coding |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 49.8 GB | – | 12tok/sest. | 559 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 105k191k with q8 cache | 6tok/sest. | 141 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 52.0 GB | 40k | 2tok/sest. | 51 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 34.9 GB | 105k128k with q8 cache | 6tok/sest. | 141 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 52.0 GB | 46k85k with q8 cache | 2tok/sest. | 68 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 34.8 GB | 16k | 6tok/sest. | 155 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 33.9 GB | 113k128k with q8 cache | 6tok/sest. | 139 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 105k128k with q8 cache | 6tok/sest. | 142 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 6tok/sest. | 142 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 34.9 GB | 40k | 6tok/sest. | 142 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 34.9 GB | 105k128k with q8 cache | 6tok/sest. | 141 tok/s | chat |
| granite 4.2 30b IBM | Q4_K_M | offload | 50.3 GB | 53k98k with q8 cache | 2tok/sest. | 83 tok/s | chat |
| 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 | – | – | – | – |
| 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 | – | – | – | – |
| 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 R1 Zero DeepSeek | Q4_K_M | won't run | 386.8 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 386.8 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 V3.1 Terminus 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 | Q4_K_M | won't run | 115.3 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 249.1 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 248.3 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 Z.ai | MXFP4 | won't run | 394.9 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 Dev 72B Moonshot AI | Q6_K | won't run | 101.5 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 | MXFP4 | won't run | 256.2 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.3 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 (UD-Q6_K) fits fully in its memory at 128k context and generates at about 10 tok/s, an estimate from its memory bandwidth. 50 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 102 GB at 128k context, more than the Intel Arc Pro B70 and system RAM can hold.
- How much does the Intel Arc Pro B70 cost in the UK?
- We have no current price for the Intel Arc Pro B70 in the UK.
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.