NVIDIA GeForce RTX 4070
12 GB · 504.2 GB/s · 200 W
In short
The NVIDIA GeForce RTX 4070 has 12 GB of GDDR6X memory with 504.2 GB/s of bandwidth, which sets how fast it generates text. At 8k context it runs 59 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at UD-Q6_K, at about 14 tok/s (estimated). We have no current price for the NVIDIA GeForce RTX 4070 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 two of them run
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 | 22.3 GB | 15k27k with q8 cache | 14tok/sest. | 229 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q6_K | fits | 22.8 GB | 8k14k with q8 cache | 14tok/sest. | 229 tok/s | – |
| Gemma 4 31B | Q5_K_M | fits | 22.7 GB | 8k23k with q8 cache | 14tok/sest. | 200 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q4_K_M | fits | 22.1 GB | 34k54k with q8 cache | 90tok/sest. | 1966 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q6_K | fits | 22.7 GB | 9k16k with q8 cache | 14tok/sest. | 229 tok/s | – |
| Gemma 4 26B-A4B | Q6_K | fits | 22.8 GB | 8k18k with q8 cache | 48tok/sest. | 1498 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | fits | 21.9 GB | 44k70k with q8 cache | 91tok/sest. | 1966 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 21.5 GB | 30k52k with q8 cache | 47tok/sest. | 1100 tok/s | chat, coding, agents |
| Devstral Small 2 24B Mistral AI | Q6_K | fits | 20.4 GB | 22k41k with q8 cache | 15tok/sest. | 259 tok/s | chat |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.6 GB | 128k | 71tok/sest. | 1423 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q5_K_M | fits | 22.1 GB | 14k26k with q8 cache | 64tok/sest. | 1422 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 21.6 GB | 12k23k with q8 cache | 14tok/sest. | 177 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.4 GB | 256k | 24tok/sest. | 522 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.3 GB | 256k | 32tok/sest. | 661 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q5_K_M | fits | 20.1 GB | 39k74k with q8 cache | 15tok/sest. | 233 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q6_K | fits | 20.4 GB | 22k41k with q8 cache | 15tok/sest. | 259 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 21.6 GB | 12k23k with q8 cache | 14tok/sest. | 185 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 15.8 GB | 51k93k with q8 cache | 20tok/sest. | 429 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 17.0 GB | 40k | 18tok/sest. | 399 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.4 GB | 92k128k with q8 cache | 32tok/sest. | 698 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.4 GB | 40k | 32tok/sest. | 698 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q8_0 | fits | 17.2 GB | 16k | 18tok/sest. | 401 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.6 GB | 256k | 65tok/sest. | 1331 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.6 GB | 95k173k with q8 cache | 31tok/sest. | 652 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 11.1 GB | 80k128k with q8 cache | 29tok/sest. | 647 tok/s | chat, coding |
| Gemma 4 E4B | Q8_0 | fits | 9.0 GB | 128k | 37tok/sest. | 801 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.9 GB | 125k | 128tok/sest. | 4675 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 8.3 GB | 118k128k with q8 cache | 41tok/sest. | 720 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 32k | 495tok/sest. | 9785 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 336tok/sest. | 6732 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 201tok/sest. | 2762 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.1 GB | 128k | 147tok/sest. | 3080 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.0 GB | 128k | 103tok/sest. | 2036 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.3 GB | 128k | 93tok/sest. | 2049 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.3 GB | 32k | 93tok/sest. | 1808 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.3 GB | 458k767k with q8 cache | 92tok/sest. | 1449 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q6_K | fits | 21.5 GB | 23k42k with q8 cache | 77tok/sest. | 2455 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 5.1 GB | 128k | 73tok/sest. | 1356 tok/s | chat, coding, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.4 GB | 170k256k with q8 cache | 69tok/sest. | 1389 tok/s | chat, coding, agents, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 5.4 GB | 128k | 69tok/sest. | 1458 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.3 GB | 128k | 67tok/sest. | 2253 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.7 GB | 256k | 63tok/sest. | 1331 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 17.2 GB | 128k | 56tok/sest. | 1899 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.0 GB | 128k | 53tok/sest. | 1899 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 9.1 GB | 128k | 37tok/sest. | 777 tok/s | chat, coding, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.1 GB | 32k | 36tok/sest. | 611 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 10.1 GB | 105k128k with q8 cache | 33tok/sest. | 720 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.6 GB | 95k173k with q8 cache | 31tok/sest. | 652 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.8 GB | 128k | 30tok/sest. | 564 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.4 GB | 64k | 26tok/sest. | 564 tok/s | chat, coding |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 15.8 GB | 51k93k with q8 cache | 20tok/sest. | 429 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 17.3 GB | 36k67k with q8 cache | 18tok/sest. | 391 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 18.0 GB | 37k68k with q8 cache | 17tok/sest. | 263 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 18.0 GB | 37k40k with q8 cache | 17tok/sest. | 263 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 18.0 GB | 37k68k with q8 cache | 17tok/sest. | 259 tok/s | chat |
| Muse Glimmer 30B meta-models | Q5_K_M | fits | 20.0 GB | 128k | 15tok/sest. | 213 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q6_K | fits | 20.4 GB | 22k40k with q8 cache | 15tok/sest. | 263 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 21.0 GB | 33k59k with q8 cache | 15tok/sest. | 229 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 22.7 GB | 8k15k with q8 cache | 14tok/sest. | 209 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.4 GB | – | 16tok/sest. | 443 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 46.6 GB | – | 20tok/sest. | 1051 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.8 GB | – | 24tok/sest. | 591 tok/s | chat |
| GLM 4.5 Air Z.ai | Q2_K | offload | 44.8 GB | – | 8tok/sest. | 252 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 46.6 GB | – | 26tok/sest. | 1322 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.9 GB | 3k4k with q8 cache | 80tok/sest. | 2045 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 46tok/sest. | 1611 tok/s | chat, coding, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 43.2 GB | – | 2tok/sest. | 61 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 43.2 GB | – | 2tok/sest. | 61 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 92.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.9 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 73.2 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.3 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 156.2 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 68.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 384.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 748.2 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.8 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4070
- Memory
- 12 GB GDDR6X
- Memory bus
- 192-bit
- Memory bandwidth
- 504.2 GB/s
- FP16 compute
- 29.146 TFLOPS
- Software
- CUDA
- Board power
- 200 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4070 have for AI models?
- The NVIDIA GeForce RTX 4070 has 12 GB of GDDR6X memory with 504.2 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4070 can run locally?
- Qwen 3.8 27B (UD-Q6_K) fits fully in its memory at 8k context and generates at about 14 tok/s, an estimate from its memory bandwidth. 59 of the 125 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4070 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 92 GB at 8k context, more than the NVIDIA GeForce RTX 4070 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4070 cost in Germany?
- We have no current price for the NVIDIA GeForce RTX 4070 in Germany.
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.