Please add "AMD Radeon AI PRO R9700" to "My Hardware"

A user request asks that the AMD Radeon AI PRO R9700 be added to the "My Hardware" database, citing its RDNA4 architecture, 32 GB GDDR6 memory, and 383 FP8 TFLOPS / 383 INT8 TOPS throughput as making it a fit for 30B–70B models at Q4_K_M quantization. The request argues the addition would improve Fit Finder accuracy for AMD users and broaden hardware coverage for ROCm and DirectML setups, and includes a Python script that classifies a GPU's local-AI profile from memory capacity, FP8/INT8 throughput, and architecture family.

The R9700 is a good candidate for “My Hardware”: RDNA4 architecture, 32 GB GDDR6, high FP8/INT8 throughput and a memory profile that fits 30B–70B models in Q4 K M. Adding it would help AMD users get accurate Fit Finder results and improve hardware coverage for ROCm/DirectML setups. Below is a small diagnostic script I use to classify a GPU for local‑AI workloads. It doesn’t benchmark anything — it simply evaluates the specs and produces a profile that tells which model sizes fit and how strong the GPU is for FP8/INT8 inference. python def gpu ai profile memory gb, fp8 tflops, int8 tops, architecture : """ This function builds an 'AI profile' for a GPU based only on its specs. It answers three practical questions: 1. Which model sizes fit in VRAM? 2. How strong is the GPU for FP8/INT8 inference? 3. What architecture family does it belong to? """ profile = {} VRAM capacity → determines which GGUF sizes fit if memory gb = 32: profile "models fit" = "30B", "34B", "40B", "70B Q4 K M, borderline " else: profile "models fit" = "7B", "13B", "20B" FP8 throughput → good indicator for multimodal and MoE models profile "fp8 class" = "high" if fp8 tflops = 300 else "medium" if fp8 tflops = 100 else "low" INT8 throughput → relevant for GGUF quantized inference profile "int8 class" = "high" if int8 tops = 300 else "medium" if int8 tops = 100 else "low" Architecture tag profile "arch" = architecture return profile Example using the R9700 specs: print gpu ai profile memory gb=32, fp8 tflops=383, int8 tops=383, architecture="RDNA4"