llamaperf

AMD Radeon AI PRO R9700 32GB vs NVIDIA RTX 3090 for local LLMs

61 reports on the AMD Radeon AI PRO R9700 32GB and 217 on the NVIDIA RTX 3090

Which is faster for local LLMs?

On Qwen3.8 27B at 4-bit, the AMD Radeon AI PRO R9700 32GB has 29.8 generation tokens/s (3 runs); prompt processing 1287.0 tokens/s (one run); the NVIDIA RTX 3090 has 38.4 generation tokens/s (5 runs); prompt processing 561.0 tokens/s (2 runs). Those are community reports whose engines and settings differ, so the gap is a rough sign, not a controlled measure of the two cards. Memory bandwidth, which caps how fast a card can write, is 46% higher on the NVIDIA RTX 3090 (640 GB/s on the AMD Radeon AI PRO R9700 32GB, 936 on the NVIDIA RTX 3090). Across 3 shared models with generation measurements on both cards, the AMD Radeon AI PRO R9700 32GB's median is higher on 0 and the NVIDIA RTX 3090's on 3.

AMD Radeon AI PRO R9700 32GB
Memory
32GB
Memory bandwidth
640 GB/s
FP16 compute
191 TFLOPS
Reports
61
NVIDIA RTX 3090
Memory
24GB
Memory bandwidth
936 GB/s
FP16 compute
142 TFLOPS
Reports
217

Measured on both

Generation and prompt processing speeds from plain runs (one device, one request, no speculative decoding, the whole model in the device's memory) of the same model size at the same bit class. Each side shows the engines and reported context behind its median. Context can mean a configured window or prompt depth, so these are community comparisons with differing settings. How to read these

ModelAMD Radeon AI PRO R9700 32GBNVIDIA RTX 3090
Qwen3.8 27B4-bit29.83 runsPP 1287.0 (one run)llama.cpp, 1 not stated2K context, 2 not stated38.45 runsPP 561.0 (2 runs)llama.cpp, vLLM128K to 256K context, 1 not stated
Qwen3.6 35B · 3B active4-bit61.4one runPP 7800.0 (one run)vLLM256K context101.7one runllama.cpp64K context
Qwen3.8 27B4-bit37.5one runengine not statedcontext not stated67.0one runengine not statedcontext not stated
Qwen3.5 35B · 3B active4-bit149.2one runPP 3075.0 (one run)llama.cppcontext not stated-0 runsPP 980.0 (one run)llama.cpp64K context

Estimated by the calculator

The quant the calculator recommends for each card and its estimated speed at a 32,768-token context with 32 GB of system RAM, from memory bandwidth and the model's shape. Where a model was also measured above, the measurement wins.

ModelAMD Radeon AI PRO R9700 32GBNVIDIA RTX 3090
Qwen3.8 27B16.0 t/sQ6_K33.9 t/sQ4_K_M
Qwen3.6 35B · 3B active58.2 t/sQ5_K_M120.1 t/sQ4_K_S
Qwen3.5 35B · 3B active58.2 t/sQ5_K_M120.1 t/sQ4_K_S
Qwen3.8 125B · 6B active18.7 t/sQ2_K, experts in RAM19.4 t/sQ2_K, experts in RAM
DeepSeek V4 Flash 284B · 13B activedoesn't fit at these settingsdoesn't fit at these settings
Qwen3.6 27B16.0 t/sQ6_K33.9 t/sQ4_K_M
Gemma 4 26B · 4B active40.9 t/sQ6_K76.4 t/sQ5_K_S
GLM-5.3 320B · 18B activedoesn't fit at these settingsdoesn't fit at these settings
DeepSeek V4.1 Flash 552B · 16B activedoesn't fit at these settingsdoesn't fit at these settings

Change the context, RAM or model in the calculator: AMD Radeon AI PRO R9700 32GB · NVIDIA RTX 3090

Every report on each card, with its source: AMD Radeon AI PRO R9700 32GB · NVIDIA RTX 3090

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