AMD Radeon RX 9070
16 GB · 640 GB/s · 220 W
In short
The AMD Radeon RX 9070 has 16 GB of GDDR6 memory with 640 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 Q6_K, at about 14 tok/s (estimated). We have no current price for the AMD Radeon RX 9070 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q6_K | fits | 25.9 GB | 98k172k with q8 cache | 14tok/sest. | 204 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 34k59k with q8 cache | 11tok/sest. | 204 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 28.2 GB | 57k110k with q8 cache | 12tok/sest. | 182 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q6_K | fits | 29.5 GB | 68k107k with q8 cache | 66tok/sest. | 1455 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 34k59k with q8 cache | 11tok/sest. | 204 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.3 GB | 153k245k with q8 cache | 42tok/sest. | 1294 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 29.0 GB | 85k133k with q8 cache | 67tok/sest. | 1455 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 26.1 GB | 106k183k with q8 cache | 36tok/sest. | 760 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 12tok/sest. | 215 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 29.9 GB | 78k101k with q8 cache | 68tok/sest. | 1660 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.8 GB | 78k107k with q8 cache | 99tok/sest. | 1254 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.5 GB | 128k | 65tok/sest. | 1086 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 27.9 GB | 57k102k with q8 cache | 37tok/sest. | 669 tok/s | chat, coding, agents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 27.9 GB | 40k | 12tok/sest. | 127 tok/s | chat |
| Gemma 4 12B | Q8_0 | fits | 14.1 GB | 256k | 26tok/sest. | 473 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.4 GB | 256k | 33tok/sest. | 589 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q6_K | fits | 25.0 GB | 95k128k with q8 cache | 14tok/sest. | 216 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 28.0 GB | 41k76k with q8 cache | 12tok/sest. | 145 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 12tok/sest. | 215 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.9 GB | 97k176k with q8 cache | 18tok/sest. | 327 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 21.1 GB | 40k | 17tok/sest. | 294 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 14.1 GB | 128k | 26tok/sest. | 485 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 14.1 GB | 40k | 26tok/sest. | 485 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.2 GB | 16k | 16tok/sest. | 296 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.7 GB | 256k | 63tok/sest. | 1102 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 14.1 GB | 149k256k with q8 cache | 26tok/sest. | 469 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.2 GB | 125k128k with q8 cache | 24tok/sest. | 446 tok/s | chat, coding |
| Gemma 4 E4B | Q8_0 | fits | 10.0 GB | 128k | 38tok/sest. | 740 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.4 GB | 125k | 79tok/sest. | 2420 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.6 GB | 128k | 32tok/sest. | 513 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 32k | 377tok/sest. | 5270 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.6 GB | 256k | 285tok/sest. | 4951 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.2 GB | 256k | 190tok/sest. | 2360 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.1 GB | 128k | 124tok/sest. | 1983 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.3 GB | 128k | 86tok/sest. | 1184 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.5 GB | 639k1024k with q8 cache | 82tok/sest. | 1163 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 6.0 GB | 128k | 72tok/sest. | 1341 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 6.0 GB | 32k | 72tok/sest. | 1230 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.0 GB | 128k | 67tok/sest. | 1747 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.8 GB | 256k | 61tok/sest. | 1102 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.4 GB | 128k | 56tok/sest. | 859 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.6 GB | 128k | 54tok/sest. | 901 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 18.2 GB | 128k | 51tok/sest. | 1313 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 19.0 GB | 128k | 49tok/sest. | 1313 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 8.1 GB | 239k256k with q8 cache | 49tok/sest. | 861 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.7 GB | 63k113k with q8 cache | 44tok/sest. | 1349 tok/s | chat, coding, agents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.4 GB | 32k | 37tok/sest. | 428 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.8 GB | 128k | 35tok/sest. | 579 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 12.1 GB | 128k | 31tok/sest. | 403 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.4 GB | 128k | 27tok/sest. | 513 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.7 GB | 64k | 27tok/sest. | 403 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 14.1 GB | 149k256k with q8 cache | 26tok/sest. | 469 tok/s | chat, coding |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.9 GB | 97k176k with q8 cache | 18tok/sest. | 327 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 22.1 GB | 74k128k with q8 cache | 16tok/sest. | 277 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.9 GB | 142k248k with q8 cache | 15tok/sest. | 204 tok/s | chat |
| Muse Glimmer 30B meta-models | Q6_K | fits | 23.8 GB | 128k | 15tok/sest. | 196 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 29.1 GB | 37k68k with q8 cache | 12tok/sest. | 167 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 12tok/sest. | 218 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.8 GB | 35k40k with q8 cache | 12tok/sest. | 218 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 35k40k with q8 cache | 12tok/sest. | 218 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.8 GB | 35k64k with q8 cache | 12tok/sest. | 215 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 41.3 GB | – | 20tok/sest. | 399 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 21tok/sest. | 895 tok/s | chat, coding |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.8 GB | – | 11tok/sest. | 357 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.6 GB | – | 29tok/sest. | 454 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 49.4 GB | – | 6tok/sest. | 150 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.5 GB | – | 26tok/sest. | 1065 tok/s | chat, coding |
| Llama 3.3 70B Meta | Q4_K_M | offload | 51.1 GB | – | 2tok/sest. | 52 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 51.1 GB | – | 2tok/sest. | 52 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.4 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.8 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.4 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.6 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 82.2 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.5 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.6 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.2 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 438.1 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.6 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 71.4 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.5 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.9 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 82.2 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- Radeon RX 9070
- Memory
- 16 GB GDDR6
- Memory bus
- 256-bit
- Memory bandwidth
- 640 GB/s
- FP16 compute
- 36.1 TFLOPS
- Software
- ROCm
- Board power
- 220 W
- PCIe generation
- Gen 5
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the AMD Radeon RX 9070 have for AI models?
- The AMD Radeon RX 9070 has 16 GB of GDDR6 memory with 640 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the AMD Radeon RX 9070 can run locally?
- Qwen 3.8 27B (Q6_K) fits fully in its memory at 32k context and generates at about 14 tok/s, an estimate from its memory bandwidth. 61 of the 125 models we track fit at that context.
- Can the AMD Radeon RX 9070 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 94 GB at 32k context, more than the AMD Radeon RX 9070 and system RAM can hold.
- How much does the AMD Radeon RX 9070 cost in Germany?
- We have no current price for the AMD Radeon RX 9070 in Germany.
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.