NVIDIA RTX Pro 6000 vs AMD Radeon AI Pro 9700 32GB
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 110% more memory bandwidth, runs 57 models natively (vs 47), and exclusively fits 10 models the other cannot. Note: NVIDIA RTX Pro 6000 uses CUDA while AMD Radeon AI Pro 9700 32GB uses ROCM — software ecosystem matters for your framework.
Analysis
The AMD Radeon AI Pro 9700 and NVIDIA RTX Pro 6000 both target professional AI workloads, but they sit at very different price points: roughly $2,500 vs $6,300. That 2.5× price gap sets up a direct question — how much VRAM and bandwidth do you actually need?
The RTX Pro 6000 has 3× more VRAM (96 GB vs 32 GB) and roughly double the bandwidth (1,344 vs 640 GB/s). In practice, the Radeon AI Pro 9700 handles 7B through 30B models comfortably at Q4–Q8, but 70B models require CPU offload, significantly reducing inference speed. The Pro 6000 runs 70B models natively at Q8_0, covering today's most capable open-weight models without compromise. The RTX Pro 6000 also comes with NVIDIA's mature CUDA ecosystem; the Radeon AI Pro 9700 runs on ROCm, which is Linux-only for GPU-accelerated inference and has narrower Ollama and vLLM support compared to CUDA.
Bottom line: For workloads that stay within 30B parameters, the Radeon AI Pro 9700 is a capable card at a competitive price, especially on Linux where ROCm support has improved substantially. For workflows that require 70B-class models locally — which increasingly includes production-grade instruction following and long-context tasks — the RTX Pro 6000 is the only practical on-prem single-GPU option at this tier. The $3,800 premium buys 70B capability, ECC memory, and significantly better software ecosystem coverage.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | AMD Radeon AI Pro 9700 32GB |
|---|---|---|
| VRAM | 96 GB | 32 GB |
| Memory type | GDDR7 | GDDR6 |
| Bandwidth | 1344 GB/s(+110%) | 640 GB/s |
| Architecture | Blackwell | RDNA 4 |
| Backend | CUDA | ROCM |
| Tier | Workstation | Datacenter |
| 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 | AMD Radeon AI Pro 9700 32GB | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 38.4 t/s(NVFP4) | 27.8 t/s(Q2_K) | +38% |
| Qwen 3.6 27B(27B) | 24.9 t/s(BF16) | 28.9 t/s(Q6_K) | -14% |
| Llama 3.1 8B Instruct(8B) | 42 t/s(FP32) | 40 t/s(BF16) | +5% |
| Qwen 2.5 7B Instruct(7.6B) | 44.2 t/s(FP32) | 42.1 t/s(BF16) | +5% |
Delta is NVIDIA RTX Pro 6000 relative to AMD Radeon AI Pro 9700 32GB.
Only NVIDIA RTX Pro 6000 can run(10)
Only AMD Radeon AI Pro 9700 32GB can run(0)
No exclusive models — NVIDIA RTX Pro 6000 can run everything AMD Radeon AI Pro 9700 32GB 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/svs27 t/s
- Llama 3.3 70B Instruct38.4 t/svs27.8 t/s
- DeepSeek R1 Distill Llama 70B38.4 t/svs27.8 t/s
- Llama 3.1 70B Instruct38.4 t/svs27.8 t/s
- Mixtral 8x7B Instruct v0.1229.2 t/svs126.9 t/s
- Command-R 35B19.2 t/svs42.5 t/s
- Qwen 3.5 35B-A3B (MoE)246.4 t/svs364.4 t/s
- Qwen 3.6 35B19.2 t/svs28.4 t/s
- Yi 1.5 34B Chat19.5 t/svs28.9 t/s
- Qwen3 32B20.5 t/svs30.3 t/s
- Qwen 2.5 32B Instruct20.7 t/svs30.6 t/s
- Qwen 2.5 Coder 32B Instruct20.7 t/svs30.6 t/s
- DeepSeek R1 Distill Qwen 32B20.7 t/svs30.6 t/s
- Nemotron 3 Nano 30B246.4 t/svs286.2 t/s
- Gemma 4 31B21.7 t/svs32.1 t/s
- Qwen3 30B-A3B (MoE)246.4 t/svs286.2 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
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX Pro 6000 or AMD Radeon AI Pro 9700 32GB?
- 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 640 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 AMD Radeon AI Pro 9700 32GB?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1344 GB/s. The AMD Radeon AI Pro 9700 32GB has 32 GB of GDDR6 at 640 GB/s. The NVIDIA RTX Pro 6000 has 64 GB more VRAM, allowing it to run 10 models the AMD Radeon AI Pro 9700 32GB 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 AMD Radeon AI Pro 9700 32GB run Llama 3.3 70B?
- Yes. The AMD Radeon AI Pro 9700 32GB runs Llama 3.3 70B natively at Q2_K quantization at approximately 27.8 tokens per second.
- What is the difference between the NVIDIA RTX Pro 6000 and AMD Radeon AI Pro 9700 32GB 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 AMD Radeon AI Pro 9700 32GB has 32 GB VRAM at 640 GB/s (ROCM backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX Pro 6000 runs 57 models natively vs 47 for the AMD Radeon AI Pro 9700 32GB.