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 Germany.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Germany, 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 Germany?
- We have no current price for the Intel Arc Pro B70 in Germany.
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.