NVIDIA RTX A6000
48 GB · 768 GB/s · 300 W
In short
The NVIDIA RTX A6000 has 48 GB of GDDR6 memory with 768 GB/s of bandwidth, which sets how fast it generates text. At 128k context it runs 60 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 13 tok/s (estimated). We have no current price for the NVIDIA RTX A6000 in the US.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the US, 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 | Q8_0 | fits | 36.6 GB | 256k | 13tok/sest. | 214 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | fits | 43.6 GB | 256k | 43tok/sest. | 381 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 36.2 GB | 256k | 13tok/sest. | 214 tok/s | chat |
| Gemma 4 31B | Q8_0 | fits | 42.6 GB | 184k256k with q8 cache | 11tok/sest. | 203 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | fits | 41.7 GB | 332k528k with q8 cache | 53tok/sest. | 330 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 38.4 GB | 256k | 49tok/sest. | 1041 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 36.2 GB | 256k | 13tok/sest. | 214 tok/s | chat |
| Gemma 4 26B-A4B | Q8_0 | fits | 29.2 GB | 256k | 41tok/sest. | 1263 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 38.4 GB | 256k | 49tok/sest. | 1041 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 37.7 GB | 198k | 23tok/sest. | 491 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 44.8 GB | 144k262k with q8 cache | 10tok/sest. | 192 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 34.8 GB | 256k | 68tok/sest. | 1400 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q6_K | fits | 40.2 GB | 662k902k with q8 cache | 80tok/sest. | 781 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 15.7 GB | 128k | 51tok/sest. | 818 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 43.7 GB | 165k256k with q8 cache | 18tok/sest. | 303 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 15.6 GB | 256k | 31tok/sest. | 525 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.4 GB | 256k | 34tok/sest. | 624 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q8_0 | fits | 38.6 GB | 128k | 12tok/sest. | 262 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 44.8 GB | 128k | 10tok/sest. | 192 tok/s | chat |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 34.8 GB | 205k256k with q8 cache | 13tok/sest. | 246 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 36.1 GB | 40k | 13tok/sest. | 209 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 27.6 GB | 128k | 17tok/sest. | 317 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 27.6 GB | 40k | 17tok/sest. | 317 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q8_0 | fits | 41.0 GB | 16k | 11tok/sest. | 210 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 9.7 GB | 256k | 54tok/sest. | 973 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 26.9 GB | 256k | 18tok/sest. | 321 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q8_0 | fits | 30.2 GB | 128k | 15tok/sest. | 288 tok/s | chat, coding, agents |
| Gemma 4 E4B | Q8_0 | fits | 12.6 GB | 128k | 40tok/sest. | 876 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 15.8 GB | 125k | 38tok/sest. | 1170 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 23.6 GB | 128k | 20tok/sest. | 346 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.4 GB | 32k | 222tok/sest. | 2629 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.7 GB | 256k | 195tok/sest. | 3506 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.3 GB | 256k | 155tok/sest. | 2242 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.7 GB | 128k | 84tok/sest. | 1175 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.3 GB | 128k | 62tok/sest. | 1354 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 8.8 GB | 1024k | 60tok/sest. | 962 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.2 GB | 128k | 57tok/sest. | 632 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 9.8 GB | 256k | 53tok/sest. | 973 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 11.6 GB | 128k | 44tok/sest. | 810 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 11.6 GB | 32k | 44tok/sest. | 779 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 21.1 GB | 128k | 40tok/sest. | 850 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 21.8 GB | 128k | 39tok/sest. | 850 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.1 GB | 32k | 35tok/sest. | 282 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 14.8 GB | 128k | 33tok/sest. | 500 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 15.1 GB | 128k | 33tok/sest. | 510 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 15.8 GB | 128k | 31tok/sest. | 274 tok/s | chat, coding, agents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.0 GB | 128k | 31tok/sest. | 419 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.4 GB | 64k | 28tok/sest. | 274 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 17.9 GB | 256k | 27tok/sest. | 489 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 35.2 GB | 128k | 22tok/sest. | 684 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 25.4 GB | 128k | 19tok/sest. | 346 tok/s | chat, coding, long documents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 26.9 GB | 256k | 18tok/sest. | 321 tok/s | chat, coding, agents, long documents |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 28.8 GB | 256k | 16tok/sest. | 214 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q8_0 | fits | 33.3 GB | 128k | 14tok/sest. | 233 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 34.8 GB | 205k256k with q8 cache | 13tok/sest. | 246 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 40.1 GB | 128k | 12tok/sest. | 185 tok/s | chat, coding |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 44.8 GB | 128k | 10tok/sest. | 193 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 44.8 GB | 40k | 10tok/sest. | 193 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 44.8 GB | 40k | 10tok/sest. | 193 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 44.8 GB | 128k | 10tok/sest. | 192 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 49.7 GB | 48k78k with q8 cache | 37tok/sest. | 820 tok/s | chat, coding, agents, long documents |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.5 GB | – | 23tok/sest. | 497 tok/s | chat, coding, long documents |
| gpt-oss 120B OpenAI | Q4_K_M | offload | 64.4 GB | – | 12tok/sest. | 473 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 66.7 GB | 24k44k with q8 cache | 3tok/sest. | 82 tok/s | chat |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 49.7 GB | 49k79k with q8 cache | 41tok/sest. | 896 tok/s | chat, coding, agents, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 51.9 GB | 40k | 5tok/sest. | 81 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 51.9 GB | 110k128k with q8 cache | 5tok/sest. | 109 tok/s | chat |
| granite 4.2 30b IBM | Q4_K_M | offload | 50.2 GB | 116k128k with q8 cache | 6tok/sest. | 132 tok/s | chat |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.4 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.0 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.1 GB | 22k41k with q8 cache | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.2 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.1 GB | 22k41k with q8 cache | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 157.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 166.6 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 115.2 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 249.0 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.7 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 248.2 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.1 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.0 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 394.8 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 758.7 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 101.4 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 147.7 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 256.1 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.2 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- RTX A6000
- Memory
- 48 GB GDDR6
- Memory bus
- 384-bit
- Memory bandwidth
- 768 GB/s
- FP16 compute
- 38.71 TFLOPS
- Software
- CUDA
- Board power
- 300 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the NVIDIA RTX A6000 have for AI models?
- The NVIDIA RTX A6000 has 48 GB of GDDR6 memory with 768 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA RTX A6000 can run locally?
- Qwen 3.8 27B (Q8_0) fits fully in its memory at 128k context and generates at about 13 tok/s, an estimate from its memory bandwidth. 60 of the 125 models we track fit at that context.
- Can the NVIDIA RTX A6000 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 102 GB at 128k context, more than the NVIDIA RTX A6000 and system RAM can hold.
- How much does the NVIDIA RTX A6000 cost in the US?
- We have no current price for the NVIDIA RTX A6000 in the US.
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.