NVIDIA RTX Pro 6000 vs NVIDIA RTX 6000 Ada
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 99 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 wins for local AI inference. It has 48 GB more VRAM and 86.7% more memory bandwidth, runs 69 models natively (vs 58), and exclusively fits 11 models the other cannot.
Analysis
The NVIDIA RTX Pro 6000 and RTX 6000 Ada Generation occupy the same slot in NVIDIA's workstation lineup, but the Blackwell successor makes a strong case for upgrading. Both target the same on-prem AI workstation buyer, making this a generation-over-generation comparison rather than a tier debate. One thing neither card offers is NVLink: NVIDIA's datasheets list it as not supported on both, so a second card of either kind pools over PCIe only.
The gap comes down to VRAM and bandwidth. The Pro 6000 doubles the Ada's 48 GB GDDR6 to 96 GB of GDDR7: the difference between fitting Llama 3.3 70B at Q8_0 (86.35 GB total) and being limited to Q3_K_M (40.72 GB) natively, since Q4_K_M's 50.75 GB spills past the Ada's 48 GB. At 1,792 GB/s vs 960 GB/s, the Pro 6000 generates tokens about 64% faster on any model both cards can hold, a little under the 87% bandwidth gap because every layer also carries a fixed cost that faster memory does not shrink: Qwen 3.6 27B at Q4_K_M runs 68.5 tok/s on the Pro 6000 versus 41.8 tok/s on the Ada. Moving from Ada's Lovelace compute units to Blackwell's updated Tensor Cores also brings FP8 and FP4 support, which TensorRT-LLM increasingly exploits for throughput on newer models.
Bottom line: For anyone running Llama 3.3 70B, Qwen 3-72B, or other 70B-class models that have become the standard benchmark for local inference quality, the RTX Pro 6000 is the clearly superior card: it runs them at Q8_0 natively, comfortably, and faster. If your workload stays below 48 GB and you already own the Ada, the upgrade isn't forced, but for new purchases, the Pro 6000 is the easy choice.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | NVIDIA RTX 6000 Ada |
|---|---|---|
| VRAM | 96 GB | 48 GB |
| Memory type | GDDR7 | GDDR6 |
| Bandwidth | 1792 GB/s(+87%) | 960 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Workstation | Workstation |
| Released | 2025 | 2022 |
| Models (native) | 69 | 58 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX Pro 6000 | NVIDIA RTX 6000 Ada | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 34.8 t/s(NVFP4) | 20.9 t/s(Q3_K_M) | +72%at Q3_K_M |
| Qwen 3.6 27B(27B) | 26 t/s(BF16) | 26 t/s(Q8_0) | +72%at Q8_0 |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 45.2 t/s(BF16) | +73% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 49.4 t/s(BF16) | +74% |
Delta is NVIDIA RTX Pro 6000 relative to NVIDIA RTX 6000 Ada, compared at the highest quantization that fits natively on both cards.
Only NVIDIA RTX Pro 6000 can run(11)
Only NVIDIA RTX 6000 Ada can run(0)
No exclusive models: NVIDIA RTX Pro 6000 can run everything NVIDIA RTX 6000 Ada can.
Both run natively(58)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct34.1 t/svs20.4 t/s
- Llama 3.3 70B Instruct34.8 t/svs20.9 t/s
- DeepSeek R1 Distill Llama 70B34.8 t/svs20.9 t/s
- Llama 3.1 70B Instruct34.8 t/svs20.9 t/s
- Mixtral 8x7B Instruct v0.1135.2 t/svs60.9 t/s
- Command-R 35B18.7 t/svs20.4 t/s
- Qwen 3.5 35B-A3B (MoE)139.1 t/svs137.9 t/s
- Qwen 3.6 35B-A3B (MoE)139.1 t/svs137.9 t/s
- Ornith 1.5 35B-A3B (MoE)139.1 t/svs137.9 t/s
- Yi 1.5 34B Chat20.6 t/svs20.3 t/s
- Qwen3 32B21.6 t/svs21.4 t/s
- Qwen 2.5 32B Instruct21.5 t/svs21.2 t/s
- Qwen 2.5 Coder 32B Instruct21.5 t/svs21.2 t/s
- DeepSeek R1 Distill Qwen 32B21.5 t/svs21.2 t/s
- Nemotron 3 Nano 30B123.4 t/svs123.5 t/s
- Gemma 4 31B23 t/svs22.7 t/s
- +42 more on both
Which should you choose?
- • You need to run larger models (>48 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
- • Price or availability decides it: NVIDIA RTX 6000 Ada has no VRAM, bandwidth or backend advantage over NVIDIA RTX Pro 6000
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX Pro 6000 or NVIDIA RTX 6000 Ada?
- For local AI inference, the NVIDIA RTX Pro 6000 has the edge. It offers 96 GB VRAM (vs 48 GB) and 1792 GB/s bandwidth (vs 960 GB/s), letting it run 69 models natively in VRAM vs 58 for its rival.
- How much VRAM does the NVIDIA RTX Pro 6000 have vs the NVIDIA RTX 6000 Ada?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1792 GB/s. The NVIDIA RTX 6000 Ada has 48 GB of GDDR6 at 960 GB/s. The NVIDIA RTX Pro 6000 has 48 GB more VRAM, allowing it to run 11 models the NVIDIA RTX 6000 Ada cannot fit natively.
- Can the NVIDIA RTX Pro 6000 run Llama 3.3 70B?
- Yes. The NVIDIA RTX Pro 6000 runs Llama 3.3 70B natively at NVFP4 quantization at approximately 34.8 tokens per second.
- Can the NVIDIA RTX 6000 Ada run Llama 3.3 70B?
- Yes. The NVIDIA RTX 6000 Ada runs Llama 3.3 70B natively at Q3_K_M quantization at approximately 20.9 tokens per second.
- What is the difference between the NVIDIA RTX Pro 6000 and NVIDIA RTX 6000 Ada for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX Pro 6000 has 96 GB VRAM at 1792 GB/s (CUDA backend). The NVIDIA RTX 6000 Ada has 48 GB VRAM at 960 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX Pro 6000 runs 69 models natively vs 58 for the NVIDIA RTX 6000 Ada.