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 99 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 wins for local AI inference. It has 64 GB more VRAM and 0.0% more memory bandwidth, runs 69 models natively (vs 54), and exclusively fits 15 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 nearly identical silicon, yet cost $1,999 and roughly $8,565 at launch respectively (NVIDIA publishes no conventional MSRP for the workstation card, and its marketplace price has since risen). What exactly does that premium of more than $6,500 buy?
Both cards pair GDDR7 with the same 512-bit bus and the same 1,792 GB/s of bandwidth, so on any model that fits in 32 GB they decode at the same speed in this site's calculator: Qwen 3.6 27B at Q4_K_M runs 68.5 tok/s on both. The difference is capacity. 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, while the Pro 6000 runs full 70B models at Q8_0 without CPU offload. Professional features (ECC memory, NVIDIA's professional driver stack, and longer product lifecycles) add operational value for managed environments, though neither card offers NVLink.
Bottom line: The RTX 5090 wins on tokens-per-second per dollar for any model that fits in 32 GB, since it decodes just as fast for far less money. 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 | 1792 GB/s | 1792 GB/s |
| Architecture | Blackwell | Blackwell |
| Backend | CUDA | CUDA |
| Tier | Workstation | Consumer |
| Released | 2025 | 2025 |
| Models (native) | 69 | 54 |
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) | 34.8 t/s(NVFP4) | N/A | N/A |
| Qwen 3.6 27B(27B) | 26 t/s(BF16) | 78.5 t/s(NVFP4) | +0%at NVFP4 |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 78.4 t/s(BF16) | +0% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 86.1 t/s(BF16) | +0% |
Delta is NVIDIA RTX Pro 6000 relative to NVIDIA RTX 5090, compared at the highest quantization that fits natively on both cards.
Only NVIDIA RTX Pro 6000 can run(15)
- Step 3.7 Flash198B
- Step 3.5 Flash196.81B
- Qwen3.8-Flash-Next180B
- Mixtral 8x22B Instruct v0.1141B
- Mistral Medium 3.5 128B128B
- Qwen 3.5 122B-A10B (MoE)122B
- Nemotron 3 Super 120B120B
- GPT-OSS 120B117B
- Llama 4 Scout 109B109B
- GLM-4.5 Air 106B106B
- GLM-4.6V 106B106B
- Qwen 2.5 72B Instruct72B
- +3 more
Only NVIDIA RTX 5090 can run(0)
No exclusive models: NVIDIA RTX Pro 6000 can run everything NVIDIA RTX 5090 can.
Both run natively(54)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.1135.2 t/svs135.2 t/s
- Command-R 35B18.7 t/svs56.6 t/s
- Qwen 3.5 35B-A3B (MoE)139.1 t/svs230.5 t/s
- Qwen 3.6 35B-A3B (MoE)139.1 t/svs230.5 t/s
- Ornith 1.5 35B-A3B (MoE)139.1 t/svs230.5 t/s
- Yi 1.5 34B Chat20.6 t/svs63.4 t/s
- Qwen3 32B21.6 t/svs66.3 t/s
- Qwen 2.5 32B Instruct21.5 t/svs64.5 t/s
- Qwen 2.5 Coder 32B Instruct21.5 t/svs64.5 t/s
- DeepSeek R1 Distill Qwen 32B21.5 t/svs64.5 t/s
- Nemotron 3 Nano 30B123.4 t/svs190.6 t/s
- Gemma 4 31B23 t/svs70 t/s
- Qwen3 30B-A3B (MoE)121.2 t/svs185.3 t/s
- Nemotron 3.5 Lightning 30B-A3B118 t/svs176.5 t/s
- Muse Glimmer 30B26 t/svs83 t/s
- Gemma 2 27B Instruct25.5 t/svs75 t/s
- +38 more on both
Which should you choose?
- • You need to run larger models (>32 GB VRAM)
- • Price or availability decides it: NVIDIA RTX 5090 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 5090?
- For local AI inference, the NVIDIA RTX Pro 6000 has the edge. It offers 96 GB VRAM (vs 32 GB) and 1792 GB/s bandwidth (vs 1792 GB/s), letting it run 69 models natively in VRAM vs 54 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 1792 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 15 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 34.8 tokens per second.
- Can the NVIDIA RTX 5090 run Llama 3.3 70B?
- The NVIDIA RTX 5090 can run Llama 3.3 70B with CPU offload at NVFP4, but at reduced speed.
- 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 1792 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 69 models natively vs 54 for the NVIDIA RTX 5090.