AMD Radeon AI PRO R9700 (32GB)

The AMD Radeon AI PRO R9700 is AMD’s first RDNA 4 workstation card. It gives you 32GB of memory in a 2-slot blower design, aimed at teams who want serious local AI capability without stepping up to a 48GB flagship card or a datacenter GPU. It suits AI developers, data scientists and technical creators who need local inference and fine-tuning headroom on a workstation budget.

Who it's for

• Running quantized LLMs in the 30B+ parameter range locally
• Fine-tuning smaller models entirely on the card
• Building and testing RAG pipelines before deploying to cloud infrastructure
• Multi-GPU AI workstations that pool memory across several cards in one chassis

Key features

• RDNA 4 architecture (Navi 48): AMD’s newest GPU architecture on TSMC’s 4nm process, a generational leap in AI-specific hardware over the previous RDNA 3 workstation cards.
• 32GB GDDR6 on a 256-bit bus: 20Gbps for 640GB/s of bandwidth, enough for quantized LLMs in the 30B+ parameter range or fine-tuning smaller models on-card.
• 64 Compute Units, 4,096 Stream Processors: plus 64 second-generation Ray Accelerators for professional rendering and visualization work.
• 128 second-generation AI Accelerators: up to 95.7 TFLOPS of dense FP16 compute and up to 1,531 TOPS of INT4 performance with sparsity, tuned for the low-precision matrix math (INT4, INT8, FP8) of quantized inference.
• Multi-GPU scaling: pool memory and compute across multiple R9700 cards for larger models than one card can hold, at a lower cost per gigabyte than a flagship card, provided your framework and workload support multi-GPU sharding.
• 2-slot blower cooling: a compact, workstation-friendly form factor that supports dense multi-GPU builds.
• Quad DisplayPort 2.1a outputs: full multi-monitor support for engineering and creative work alongside AI workloads.
• ROCm on Linux and Windows: an open software stack for AI development, officially supported on both major desktop operating systems.

Specifications

Good to know

• ROCm is officially supported on this card on Linux and Windows, but framework compatibility and out-of-the-box performance still generally trail CUDA on the newest, most experimental AI research code.
• Mainstream inference stacks such as llama.cpp, vLLM and PyTorch with ROCm backends are the better fit than bleeding-edge research repos.
• The card has more memory and AI-specific hardware than any consumer gaming GPU at a similar price, and it also suits GPU-accelerated rendering and data-heavy visualization.

Choose the R9700 as a first card for local inference infrastructure, with room to add cards once your workload and frameworks are proven. If you need 48GB on a single card, look at the tier above.