As AI development increasingly shifts from cloud to local environments, professionals are running into a significant barrier—video memory. Modern large language models (LLMs) like DeepSeek R1, Mistral 3.1, and Flux.1 require more than 20GB of VRAM to run smoothly. Consumer-grade GPUs with 16GB or less often fall short, leading to sluggish performance, model incompatibility, or the need to offload tasks to slower system memory.
Enter the AMD Radeon™ AI PRO R9700—a professional-grade GPU built specifically to meet the demands of local AI workloads. Featuring the new AMD RDNA™ 4 architecture and a generous 32GB of GDDR6 memory, the R9700 delivers the throughput and compute power required for next-gen AI development, simulation, and generative workflows.
Built for Local AI at Scale
The Radeon™ AI PRO R9700 is equipped with:
Specification | Details |
---|---|
Compute Units | 64 |
VRAM | 32GB GDDR6 |
Memory Interface | 256-bit |
Memory Bandwidth | 640 GB/s |
AI Accelerators | 128 |
FP16 Dense Performance | 191 TFLOPS |
INT4 Sparse Performance | 1531 TOPS |
Power Draw (TDP) | 300W |
Interface | PCIe® 5.0 |
That massive 32GB VRAM buffer is the game-changer here. It’s not just about storing more data—it’s about enabling high-performance inference and training for increasingly demanding models without offloading to system RAM.
Performance Comparison: AMD Radeon AI Pro 9700 vs NVIDIA RTX 5080
In benchmark testing using models like Phi 3.5 MoE, DeepSeek R1, and Qwen 3 32B Q6, the Radeon™ AI PRO R9700 dramatically outpaced NVIDIA’s GeForce RTX 5080 (16GB). For large prompts and high-parameter models, the Radeon card posted up to 496% faster throughput in tokens/sec—a critical metric in LLM performance.
Token Throughput Benchmark (Higher is Better)
Model / Prompt | RTX 5080 (16GB) | Radeon AI PRO R9700 (32GB) | Performance Uplift |
---|---|---|---|
Phi 3.5 MoE Q4 | 100% (baseline) | 361% | +261% |
Mistral Small 3.1 24B Instruct 2503 Q8 | 100% (baseline) | 437% | +337% |
Qwen 3 32B Q6 (Standard Prompt) | 100% (baseline) | 447% | +347% |
DeepSeek R1 Distill Qwen 32B Q6 | 100% (baseline) | 454% | +354% |
Qwen 3 32B Q6 (Large Prompt >3000 tokens) | 100% (baseline) | 496% | +396% |
Source: AMD RPW-495 Benchmarks, May 2025
The takeaway? For professionals running large prompts or full-sized models locally, the Radeon™ AI PRO R9700 isn’t just competitive—it’s transformative.
Ideal Use Cases for Radeon AI PRO R9700
The AI PRO R9700 is designed for professionals and researchers working in:
- Large Language Model Development – Fine-tune and test LLMs like Qwen, Mistral, and DeepSeek locally without cutting model size or performance.
- Generative Design & Simulation – Run CAD simulations or generative AI workflows without offloading compute to the cloud.
- AI-Driven Content Creation – Utilize advanced text-to-image tools like Stable Diffusion 3.5 Medium, which requires more than 20GB of VRAM.
With native support for the AMD ROCm™ framework, the card is optimized for deep learning frameworks like PyTorch, enabling broader compatibility across AI pipelines.
Multi-GPU Scalability & Form Factor Advantage
One key strength of the AI PRO R9700 is its suitability for multi-GPU workstation deployments. The compact form factor combined with PCIe® 5.0 compatibility means users can scale up performance by adding additional cards—critical for inference farms or training setups where concurrency matters.
Conclusion: A Smart Bet for AI-First Professionals
The AMD Radeon™ AI PRO R9700 is more than a professional GPU—it’s a platform for pushing the boundaries of local AI. With 32GB of VRAM, 128 AI accelerators, and incredible token-per-second performance, it’s purpose-built for the future of machine learning and large model development on the desktop.
For professionals seeking a high-throughput, scalable, and cost-effective alternative to cloud compute or memory-limited GPUs, the R9700 is a compelling new benchmark. Get yours on our ProMagix HD150 now.
Josh has been with Velocity Micro since 2007 in various Marketing, PR, and Sales related roles. As the Director of Sales & Marketing, he is responsible for all Direct and Retail sales as well as Marketing activities. He enjoys Seinfeld reruns, the Atlanta Braves, and Beatles songs written by John, Paul, or George. Sorry, Ringo.