Inquiry into 4x AMD Radeon AI Pro R9700 Performance for Local LLMs
A user in the r/LocalLLaMA community requested real-world benchmarks and hardware experiences regarding a setup with four AMD Radeon AI Pro R9700 GPUs for an office AI server. The proposed build aims to host models such as DeepSeek V4 Flash and Qwen Flash using memory offloading or aggressive quantization. Deploying large language models locally requires massive video memory (VRAM), driving interest in multi-GPU configurations for developer workstations. Evaluating AMD's RDNA 4 professional GPUs provides the community with insights on viable alternatives to Nvidia hardware for cost-effective AI inference. Each AMD Radeon AI Pro R9700 GPU includes 32GB of VRAM, giving a 4-card system a aggregate total of 128GB of memory. This capacity makes it feasible to run large Mixture-of-Experts (MoE) models like DeepSeek V4 Flash when combined with heavy quantization or partial CPU/GPU offloading.
## BACKGROUND
The AMD Radeon AI Pro R9700 is a professional workstation graphics card built on the 4nm RDNA 4 architecture, offering 32GB of memory for memory-intensive AI workloads. Open-weight models like DeepSeek V4 Flash utilize Mixture-of-Experts architectures with up to 284 billion parameters, requiring high memory bandwidth and significant VRAM capacity to achieve practical inference speeds.