{"id":3478,"date":"2025-07-23T19:07:50","date_gmt":"2025-07-23T19:07:50","guid":{"rendered":"https:\/\/www.velocitymicro.com\/blog\/?p=3478"},"modified":"2025-08-06T14:26:52","modified_gmt":"2025-08-06T14:26:52","slug":"amd-radeon-ai-pro-r9700","status":"publish","type":"post","link":"https:\/\/www.velocitymicro.com\/blog\/amd-radeon-ai-pro-r9700\/","title":{"rendered":"AMD Radeon AI PRO R9700"},"content":{"rendered":"

 <\/p>\n

\"R9700\"<\/p>\n

Redefining Local AI Performance<\/h1>\n

As AI development increasingly shifts from cloud to local environments<\/a>, professionals are running into a significant barrier\u2014video memory. Modern large language models (LLMs) like DeepSeek R1, Mistral 3.1, and Flux.1 require more than 20GB of VRAM<\/a> 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.<\/p>\n

Enter the AMD Radeon™ AI PRO R9700<\/strong><\/a>\u2014a 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.<\/p>\n

Built for Local AI at Scale<\/h2>\n

The Radeon™ AI PRO R9700 is equipped with:<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Specification<\/strong><\/th>\nDetails<\/strong><\/th>\n<\/tr>\n<\/thead>\n
Compute Units<\/td>\n64<\/td>\n<\/tr>\n
VRAM<\/td>\n32GB GDDR6<\/td>\n<\/tr>\n
Memory Interface<\/td>\n256-bit<\/td>\n<\/tr>\n
Memory Bandwidth<\/td>\n640 GB\/s<\/td>\n<\/tr>\n
AI Accelerators<\/td>\n128<\/td>\n<\/tr>\n
FP16 Dense Performance<\/td>\n191 TFLOPS<\/td>\n<\/tr>\n
INT4 Sparse Performance<\/td>\n1531 TOPS<\/td>\n<\/tr>\n
Power Draw (TDP)<\/td>\n300W<\/td>\n<\/tr>\n
Interface<\/td>\nPCIe\u00ae 5.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

That massive 32GB VRAM buffer is the game-changer here. It’s not just about storing more data\u2014it\u2019s about enabling high-performance inference and training for increasingly demanding models without offloading to system RAM.<\/p>\n

Performance Comparison: AMD Radeon AI Pro 9700 vs NVIDIA RTX 5080<\/h2>\n

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\u2019s GeForce RTX 5080 (16GB). For large prompts and high-parameter models, the Radeon card posted up to 496% faster throughput<\/strong> in tokens\/sec\u2014a critical metric in LLM performance.<\/p>\n

Token Throughput Benchmark (Higher is Better)<\/h3>\n\n\n\n\n\n\n\n\n\n
Model \/ Prompt<\/strong><\/th>\nRTX 5080 (16GB)<\/strong><\/th>\nRadeon AI PRO R9700 (32GB)<\/strong><\/th>\nPerformance Uplift<\/strong><\/th>\n<\/tr>\n<\/thead>\n
Phi 3.5 MoE Q4<\/td>\n100% (baseline)<\/td>\n361%<\/td>\n+261%<\/td>\n<\/tr>\n
Mistral Small 3.1 24B Instruct 2503 Q8<\/td>\n100% (baseline)<\/td>\n437%<\/td>\n+337%<\/td>\n<\/tr>\n
Qwen 3 32B Q6 (Standard Prompt)<\/td>\n100% (baseline)<\/td>\n447%<\/td>\n+347%<\/td>\n<\/tr>\n
DeepSeek R1 Distill Qwen 32B Q6<\/td>\n100% (baseline)<\/td>\n454%<\/td>\n+354%<\/td>\n<\/tr>\n
Qwen 3 32B Q6 (Large Prompt >3000 tokens)<\/td>\n100% (baseline)<\/td>\n496%<\/td>\n+396%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Source: AMD RPW-495 Benchmarks, May 2025<\/em><\/p>\n

The takeaway? For professionals running large prompts or full-sized models locally, the Radeon™ AI PRO R9700 isn’t just competitive\u2014it\u2019s transformative.<\/p>\n

Ideal Use Cases for Radeon AI PRO R9700<\/h2>\n

The AI PRO R9700 is designed for professionals and researchers working in:<\/p>\n