NVIDIA RTX 6000 Ada Generation
48 GB · 960 GB/s · 300 W
In short
The NVIDIA RTX 6000 Ada Generation has 48 GB of GDDR6 memory with 960 GB/s of bandwidth, which sets how fast it generates text. At 32k context it runs 65 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 19 tok/s (estimated). We have no current price for the NVIDIA RTX 6000 Ada Generation 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.1 GB | 256k | 19tok/sest. | 661 tok/s | chat, long documents |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | fits | 40.7 GB | 256k | 83tok/sest. | 1550 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 29.7 GB | 256k | 19tok/sest. | 661 tok/s | chat, long documents |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | fits | 46.9 GB | 48k78k with q8 cache | 110tok/sest. | 3737 tok/s | chat, coding, agents, long documents |
| Gemma 4 31B | Q8_0 | fits | 34.6 GB | 184k256k with q8 cache | 17tok/sest. | 589 tok/s | chat |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | fits | 39.0 GB | 332k528k with q8 cache | 114tok/sest. | 1724 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 35.9 GB | 256k | 94tok/sest. | 4721 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 29.7 GB | 256k | 19tok/sest. | 661 tok/s | chat, long documents |
| Gemma 4 26B-A4B | Q8_0 | fits | 26.7 GB | 256k | 71tok/sest. | 4200 tok/s | chat, coding, agents, long documents |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | fits | 46.9 GB | 49k79k with q8 cache | 134tok/sest. | 4446 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 35.9 GB | 256k | 94tok/sest. | 4721 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 32.2 GB | 198k | 51tok/sest. | 2465 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.2 GB | 144k262k with q8 cache | 20tok/sest. | 698 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 33.7 GB | 256k | 100tok/sest. | 5387 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q6_K | fits | 38.9 GB | 662k902k with q8 cache | 129tok/sest. | 4068 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.9 GB | 128k | 110tok/sest. | 3524 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 34.1 GB | 165k256k with q8 cache | 56tok/sest. | 2171 tok/s | chat, coding, agents, long documents |
| Qwen3 32B Alibaba Qwen | Q8_0 | fits | 41.3 GB | 40k | 14tok/sest. | 410 tok/s | chat |
| Gemma 4 12B | Q8_0 | fits | 13.5 GB | 256k | 44tok/sest. | 1533 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.8 GB | 256k | 56tok/sest. | 1911 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.2 GB | 128k | 20tok/sest. | 698 tok/s | chat |
| Gemma 3 27B | Q8_0 | fits | 30.5 GB | 128k | 19tok/sest. | 702 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q8_0 | fits | 41.3 GB | 55k102k with q8 cache | 14tok/sest. | 471 tok/s | chat |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.3 GB | 205k256k with q8 cache | 30tok/sest. | 1061 tok/s | chat, coding, long documents |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.5 GB | 40k | 28tok/sest. | 954 tok/s | chat, coding, long documents |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.5 GB | 128k | 44tok/sest. | 1574 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.5 GB | 40k | 44tok/sest. | 1574 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q8_0 | fits | 21.6 GB | 16k | 27tok/sest. | 959 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 256k | 106tok/sest. | 3575 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.5 GB | 256k | 44tok/sest. | 1520 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.6 GB | 128k | 41tok/sest. | 1448 tok/s | chat, coding, agents, long documents |
| Gemma 4 E4B | Q8_0 | fits | 9.4 GB | 128k | 65tok/sest. | 2400 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.8 GB | 125k | 134tok/sest. | 7852 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.0 GB | 128k | 55tok/sest. | 1663 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.8 GB | 32k | 637tok/sest. | 17100 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 483tok/sest. | 16063 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 321tok/sest. | 7658 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.5 GB | 128k | 210tok/sest. | 6433 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.7 GB | 128k | 146tok/sest. | 3842 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.9 GB | 1024k | 138tok/sest. | 3773 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.4 GB | 128k | 122tok/sest. | 4352 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.4 GB | 32k | 122tok/sest. | 3990 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.4 GB | 128k | 113tok/sest. | 5669 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.2 GB | 256k | 104tok/sest. | 3575 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.8 GB | 128k | 94tok/sest. | 2788 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.0 GB | 128k | 91tok/sest. | 2922 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 17.6 GB | 128k | 87tok/sest. | 4260 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.4 GB | 128k | 84tok/sest. | 4260 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.5 GB | 256k | 83tok/sest. | 2793 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.1 GB | 128k | 74tok/sest. | 4376 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.8 GB | 32k | 62tok/sest. | 1388 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.2 GB | 128k | 60tok/sest. | 1880 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.5 GB | 128k | 53tok/sest. | 1309 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.8 GB | 128k | 46tok/sest. | 1663 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.1 GB | 64k | 45tok/sest. | 1309 tok/s | chat, coding, long documents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.5 GB | 256k | 44tok/sest. | 1520 tok/s | chat, coding, agents, long documents |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.3 GB | 205k256k with q8 cache | 30tok/sest. | 1061 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.5 GB | 128k | 27tok/sest. | 898 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.3 GB | 256k | 26tok/sest. | 661 tok/s | chat, coding, long documents |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.2 GB | 128k | 20tok/sest. | 708 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.2 GB | 40k | 20tok/sest. | 708 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.2 GB | 40k | 20tok/sest. | 708 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.2 GB | 128k | 20tok/sest. | 698 tok/s | chat |
| Muse Glimmer 30B meta-models | Q8_0 | fits | 31.5 GB | 128k | 18tok/sest. | 637 tok/s | chat, long documents |
| granite 4.2 30b IBM | Q8_0 | fits | 37.9 GB | 69k127k with q8 cache | 15tok/sest. | 541 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.2 GB | – | 32tok/sest. | 1480 tok/s | chat, coding, agents, long documents |
| gpt-oss 120B OpenAI | Q4_K_M | offload | 60.5 GB | – | 26tok/sest. | 2175 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 48.8 GB | 24k44k with q8 cache | 23tok/sest. | 629 tok/s | chat, coding |
| Llama 3.3 70B Meta | Q4_K_M | offload | 50.5 GB | 22k41k with q8 cache | 7tok/sest. | 214 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 50.5 GB | 22k41k with q8 cache | 7tok/sest. | 214 tok/s | – |
| GLM 4.5V Z.ai | Q4_K_M | offload | 65.9 GB | – | 6tok/sest. | 518 tok/s | – |
| GLM 4.6V Z.ai | Q4_K_M | offload | 72.2 GB | – | 5tok/sest. | 494 tok/s | – |
| Kimi Dev 72B Moonshot AI | Q6_K | offload | 70.8 GB | – | 2tok/sest. | 174 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.8 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 | 75.2 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.8 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.2 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 81.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.0 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 423.6 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.5 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.0 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 123.9 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 81.6 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- RTX 6000 Ada Generation
- Memory
- 48 GB GDDR6
- Memory bus
- 384-bit
- Memory bandwidth
- 960 GB/s
- FP16 compute
- 91.1 TFLOPS
- Software
- CUDA
- Board power
- 300 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the NVIDIA RTX 6000 Ada Generation have for AI models?
- The NVIDIA RTX 6000 Ada Generation has 48 GB of GDDR6 memory with 960 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA RTX 6000 Ada Generation can run locally?
- Qwen 3.8 27B (Q8_0) fits fully in its memory at 32k context and generates at about 19 tok/s, an estimate from its memory bandwidth. 65 of the 125 models we track fit at that context.
- Can the NVIDIA RTX 6000 Ada Generation run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 94 GB at 32k context, more than the NVIDIA RTX 6000 Ada Generation and system RAM can hold.
- How much does the NVIDIA RTX 6000 Ada Generation cost in Germany?
- We have no current price for the NVIDIA RTX 6000 Ada Generation in Germany.
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.