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 70 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 wins for local AI inference. It has 48 GB more VRAM and 40% more memory bandwidth, runs 57 models natively (vs 52), and exclusively fits 5 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 at a similar $6,000–$7,000 price point, making this a generation-over-generation comparison rather than a tier debate.
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 a 70B model at Q8_0 with headroom versus fitting it at Q4_K_M with minimal buffer. At 1,344 GB/s vs 960 GB/s, the Pro 6000 generates tokens roughly 40% faster on any model both cards can hold. 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 at this price tier, 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 | 1344 GB/s(+40%) | 960 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Workstation | Workstation |
| Released | 2025 | 2022 |
| Models (native) | 57 | 52 |
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) | 38.4 t/s(NVFP4) | 27.4 t/s(NVFP4) | +40% |
| Qwen 3.6 27B(27B) | 24.9 t/s(BF16) | 71.1 t/s(NVFP4) | -65% |
| Llama 3.1 8B Instruct(8B) | 42 t/s(FP32) | 30 t/s(FP32) | +40% |
| Qwen 2.5 7B Instruct(7.6B) | 44.2 t/s(FP32) | 31.6 t/s(FP32) | +40% |
Delta is NVIDIA RTX Pro 6000 relative to NVIDIA RTX 6000 Ada.
Only NVIDIA RTX Pro 6000 can run(5)
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(52)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Nemotron 3 Super 120B246.4 t/svs267.5 t/s
- GPT-OSS 120B591.4 t/svs641.9 t/s
- Llama 4 Scout 109B173.9 t/svs188.8 t/s
- GLM-4.5 Air 106B246.4 t/svs267.5 t/s
- GLM-4.6V 106B246.4 t/svs267.5 t/s
- Qwen 2.5 72B Instruct37.3 t/svs26.7 t/s
- Llama 3.3 70B Instruct38.4 t/svs27.4 t/s
- DeepSeek R1 Distill Llama 70B38.4 t/svs27.4 t/s
- Llama 3.1 70B Instruct38.4 t/svs27.4 t/s
- Mixtral 8x7B Instruct v0.1229.2 t/svs163.7 t/s
- Command-R 35B19.2 t/svs54.9 t/s
- Qwen 3.5 35B-A3B (MoE)246.4 t/svs704 t/s
- Qwen 3.6 35B19.2 t/svs54.9 t/s
- Yi 1.5 34B Chat19.5 t/svs55.8 t/s
- Qwen3 32B20.5 t/svs58.5 t/s
- Qwen 2.5 32B Instruct20.7 t/svs59.1 t/s
- +36 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
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 1344 GB/s bandwidth (vs 960 GB/s), letting it run 57 models natively in VRAM vs 52 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 1344 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 5 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 38.4 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 NVFP4 quantization at approximately 27.4 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 1344 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 57 models natively vs 52 for the NVIDIA RTX 6000 Ada.