This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
Quick Answer: NVIDIA RTX 6000 Ada has 48GB VRAM, enough for models up to roughly 120B parameters at 4-bit quantization. It draws 300W under load.
With 48GB VRAM, NVIDIA RTX 6000 Ada can run models up to approximately 120B parameters using 4-bit quantization. That covers most popular models, including 70B-class ones at 4-bit.
Consider H100 or MI300X — Maximum VRAM for enterprise workloads.
Buy directly on Amazon with fast shipping and reliable customer service.
Essential accessories to pair with NVIDIA RTX 6000 Ada
Accessories Total
Typical prices — check Amazon for current pricing
💡 Not ready to buy? Try cloud GPUs first
Test NVIDIA RTX 6000 Ada performance in the cloud before investing in hardware. Pay by the hour with no commitment.
Showing 12 of 80 rows. Speeds are calculated estimates, not measurements — search for your model to jump straight to it.
| Model | Size | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|---|
| Deepseek AI Deepseek Coder 1.3B Instruct | 1.3B | Q4 | ~270 tok/sEstimated | 1GB |
| Deepseek AI Deepseek R1 Distill Qwen 1.5B | 1.5B | Q4 | ~270 tok/sEstimated | 1GB |
| Deepseek AI Deepseek Ocr 2 | Unknown | Q4 | ~225 tok/sEstimated | 2GB |
| Deepseek AI Deepseek Ocr | Unknown | Q4 | ~225 tok/sEstimated | 2GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 8bit | 8B | Q4 | ~225 tok/sEstimated | 4GB |
| Lmstudio Community Deepseek R1 0528 Qwen3 8B Mlx 4bit | 8B | Q4 | ~225 tok/sEstimated | 4GB |
| Deepseek AI Deepseek R1 Distill Qwen 7B | 7B | Q4 | ~225 tok/sEstimated | 4GB |
| Nineninesix Kani Tts 2 En | Unknown | Q4 | ~215 tok/sEstimated | 1GB |
| Qwen Qwen3 Tts 12hz 1 7B Customvoice | 7B | Q4 | ~215 tok/sEstimated | 1GB |
| Zai Org Glm Ocr | Unknown | Q4 | ~215 tok/sEstimated | 1GB |
| Qwen Qwen3 Asr 1 7B | 7B | Q4 | ~215 tok/sEstimated | 2GB |
| Nari Labs Dia2 2B | 2B | Q4 | ~215 tok/sEstimated | 1GB |
Showing 12 of 240 rows.
| Model | Size | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|---|
| 01 AI Yi 1 5 34B Chat | 34B | Q4 | Fits comfortably | ~62 tok/sEstimated | 18GB (have 48GB) |
| 01 AI Yi 1 5 34B Chat | 34B | Q8 | Fits comfortably | ~44 tok/sEstimated | 35GB (have 48GB) |
| 01 AI Yi 1 5 34B Chat | 34B | FP16 | Not supported | ~24 tok/sEstimated | 69GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q4 | Fits comfortably | ~135 tok/sEstimated | 7GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | Q8 | Fits comfortably | ~94 tok/sEstimated | 13GB (have 48GB) |
| AI Forever Rugpt 3.5 13B | 13B | FP16 | Fits comfortably | ~51 tok/sEstimated | 26GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | Q4 | Fits comfortably | ~36 tok/sEstimated | 37GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | Q8 | Not supported | ~25 tok/sEstimated | 73GB (have 48GB) |
| AI Mo Kimina Prover 72B | 72B | FP16 | Not supported | ~14 tok/sEstimated | 146GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q4 | Fits comfortably | ~215 tok/sEstimated | 1GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | Q8 | Fits comfortably | ~150 tok/sEstimated | 2GB (have 48GB) |
| Alibaba Nlp Gte Qwen2 1.5B Instruct | 1.5B | FP16 | Fits comfortably | ~81 tok/sEstimated | 4GB (have 48GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Explore how RTX 4090 stacks up for local inference workloads.
Explore how RTX 4080 stacks up for local inference workloads.
Explore how RTX 4070 Ti stacks up for local inference workloads.
Explore how RTX 3090 stacks up for local inference workloads.
Explore how RX 7900 XTX stacks up for local inference workloads.