NVIDIA RTX Pro 6000 vs NVIDIA RTX 5090
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 64 GB more VRAM and -25% more memory bandwidth, runs 57 models natively (vs 47), and exclusively fits 10 models the other cannot.
Analysis
The NVIDIA RTX 5090 and RTX Pro 6000 share the same GB202 Blackwell die, which makes this comparison unusually revealing: both cards use identical silicon, yet cost roughly $2,000 and $6,300 respectively. What exactly does that $4,300 premium buy?
The RTX 5090 uses GDDR7 on a wider 512-bit bus, giving it 1,792 GB/s — about 33% more bandwidth than the Pro 6000's 1,344 GB/s. That means the consumer card is actually faster for models that fit in 32 GB. But the Pro 6000 triples the VRAM to 96 GB, changing which models fit entirely. The 5090 tops out at 32B-class models natively at Q4; the Pro 6000 runs full 70B models at Q8_0 without CPU offload. Professional features — ECC memory, NVIDIA's professional driver stack, NVLink certification, and longer product lifecycles — add operational value for managed environments.
Bottom line: The RTX 5090 wins on tokens-per-second per dollar for any model that fits in 32 GB. The RTX Pro 6000 wins for anyone whose workload requires 70B-class models natively in VRAM, who needs ECC memory reliability for sensitive inference tasks, or who is buying cards for a managed workstation fleet. The premium is justified by 3× the VRAM and professional guarantees — not by raw compute performance.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | NVIDIA RTX 5090 |
|---|---|---|
| VRAM | 96 GB | 32 GB |
| Memory type | GDDR7 | GDDR7 |
| Bandwidth | 1344 GB/s | 1792 GB/s(+33%) |
| Architecture | Blackwell | Blackwell |
| Backend | CUDA | CUDA |
| Tier | Workstation | Consumer |
| Released | 2025 | 2025 |
| Models (native) | 57 | 47 |
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 5090 | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 38.4 t/s(NVFP4) | 77.8 t/s(Q2_K) | -51% |
| Qwen 3.6 27B(27B) | 24.9 t/s(BF16) | 132.7 t/s(NVFP4) | -81% |
| Llama 3.1 8B Instruct(8B) | 42 t/s(FP32) | 112 t/s(BF16) | -63% |
| Qwen 2.5 7B Instruct(7.6B) | 44.2 t/s(FP32) | 117.9 t/s(BF16) | -63% |
Delta is NVIDIA RTX Pro 6000 relative to NVIDIA RTX 5090.
Only NVIDIA RTX Pro 6000 can run(10)
Only NVIDIA RTX 5090 can run(0)
No exclusive models — NVIDIA RTX Pro 6000 can run everything NVIDIA RTX 5090 can.
Both run natively(47)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct37.3 t/svs75.7 t/s
- Llama 3.3 70B Instruct38.4 t/svs77.8 t/s
- DeepSeek R1 Distill Llama 70B38.4 t/svs77.8 t/s
- Llama 3.1 70B Instruct38.4 t/svs77.8 t/s
- Mixtral 8x7B Instruct v0.1229.2 t/svs305.6 t/s
- Command-R 35B19.2 t/svs119.1 t/s
- Qwen 3.5 35B-A3B (MoE)246.4 t/svs1314.1 t/s
- Qwen 3.6 35B19.2 t/svs102.4 t/s
- Yi 1.5 34B Chat19.5 t/svs104.2 t/s
- Qwen3 32B20.5 t/svs109.3 t/s
- Qwen 2.5 32B Instruct20.7 t/svs110.3 t/s
- Qwen 2.5 Coder 32B Instruct20.7 t/svs110.3 t/s
- DeepSeek R1 Distill Qwen 32B20.7 t/svs110.3 t/s
- Nemotron 3 Nano 30B246.4 t/svs1314.1 t/s
- Gemma 4 31B21.7 t/svs115.6 t/s
- Qwen3 30B-A3B (MoE)246.4 t/svs1314.1 t/s
- +31 more on both
Which should you choose?
- • You need to run larger models (>32 GB VRAM)
- • Faster token generation is the priority
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX Pro 6000 or NVIDIA RTX 5090?
- For local AI inference, the NVIDIA RTX Pro 6000 has the edge. It offers 96 GB VRAM (vs 32 GB) and 1344 GB/s bandwidth (vs 1792 GB/s), letting it run 57 models natively in VRAM vs 47 for its rival.
- How much VRAM does the NVIDIA RTX Pro 6000 have vs the NVIDIA RTX 5090?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1344 GB/s. The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The NVIDIA RTX Pro 6000 has 64 GB more VRAM, allowing it to run 10 models the NVIDIA RTX 5090 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 5090 run Llama 3.3 70B?
- Yes. The NVIDIA RTX 5090 runs Llama 3.3 70B natively at Q2_K quantization at approximately 77.8 tokens per second.
- What is the difference between the NVIDIA RTX Pro 6000 and NVIDIA RTX 5090 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 5090 has 32 GB VRAM at 1792 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX Pro 6000 runs 57 models natively vs 47 for the NVIDIA RTX 5090.