NVIDIA RTX Pro 6000 vs AMD Radeon AI PRO R9700 32GB
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 180.0% more memory bandwidth, runs 69 models natively (vs 54), and exclusively fits 15 models the other cannot. Note: NVIDIA RTX Pro 6000 uses CUDA while AMD Radeon AI PRO R9700 32GB uses ROCM; software ecosystem matters for your framework.
Analysis
The AMD Radeon AI PRO R9700 and NVIDIA RTX Pro 6000 both target professional AI workloads, but they sit at very different price points: $1,299 vs roughly $8,565 at launch. That more-than-6× 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 nearly three times the bandwidth (1,792 vs 640 GB/s). In practice, the Radeon AI PRO R9700 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 R9700 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 R9700 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 premium of more than $7,000 buys 70B capability, ECC memory, and significantly better software ecosystem coverage.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | AMD Radeon AI PRO R9700 32GB |
|---|---|---|
| VRAM | 96 GB | 32 GB |
| Memory type | GDDR7 | GDDR6 |
| Bandwidth | 1792 GB/s(+180%) | 640 GB/s |
| Architecture | Blackwell | RDNA 4 |
| Backend | CUDA | ROCM |
| Tier | Workstation | Workstation |
| 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 | AMD Radeon AI PRO R9700 32GB | 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) | 22.6 t/s(Q6_K) | +142%at Q6_K |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 31 t/s(BF16) | +153% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 33.9 t/s(BF16) | +154% |
Delta is NVIDIA RTX Pro 6000 relative to AMD Radeon AI PRO R9700 32GB, 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 AMD Radeon AI PRO R9700 32GB can run(0)
No exclusive models: NVIDIA RTX Pro 6000 can run everything AMD Radeon AI PRO R9700 32GB 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/svs53.5 t/s
- Command-R 35B18.7 t/svs22.2 t/s
- Qwen 3.5 35B-A3B (MoE)139.1 t/svs88.6 t/s
- Qwen 3.6 35B-A3B (MoE)139.1 t/svs88.6 t/s
- Ornith 1.5 35B-A3B (MoE)139.1 t/svs88.6 t/s
- Yi 1.5 34B Chat20.6 t/svs19.7 t/s
- Qwen3 32B21.6 t/svs21 t/s
- Qwen 2.5 32B Instruct21.5 t/svs20.5 t/s
- Qwen 2.5 Coder 32B Instruct21.5 t/svs20.5 t/s
- DeepSeek R1 Distill Qwen 32B21.5 t/svs20.5 t/s
- Nemotron 3 Nano 30B123.4 t/svs72.3 t/s
- Gemma 4 31B23 t/svs19.6 t/s
- Qwen3 30B-A3B (MoE)121.2 t/svs69.2 t/s
- Nemotron 3.5 Lightning 30B-A3B118 t/svs70.2 t/s
- Muse Glimmer 30B26 t/svs22.7 t/s
- Gemma 2 27B Instruct25.5 t/svs20.9 t/s
- +38 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)
- • Price or availability decides it: AMD Radeon AI PRO R9700 32GB 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 AMD Radeon AI PRO R9700 32GB?
- 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 640 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 AMD Radeon AI PRO R9700 32GB?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1792 GB/s. The AMD Radeon AI PRO R9700 32GB has 32 GB of GDDR6 at 640 GB/s. The NVIDIA RTX Pro 6000 has 64 GB more VRAM, allowing it to run 15 models the AMD Radeon AI PRO R9700 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 34.8 tokens per second.
- Can the AMD Radeon AI PRO R9700 32GB run Llama 3.3 70B?
- The AMD Radeon AI PRO R9700 32GB can run Llama 3.3 70B with CPU offload at Q4_K_M, but at reduced speed.
- What is the difference between the NVIDIA RTX Pro 6000 and AMD Radeon AI PRO R9700 32GB 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 AMD Radeon AI PRO R9700 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 69 models natively vs 54 for the AMD Radeon AI PRO R9700 32GB.