Two AMD Ryzen AI Halo PCs Run 397B Quen 3.5 in Local Test
TL;DR
AMD’s Ryzen AI Halo introduces a compact yet capable solution for handling large-scale AI models, using its integrated APU architecture to streamline processing. With 128 GB of shared memory per unit, a single device can execute models with up to 200 billion parameters, while clustering two devices doubles this capacity to accommodate 400 billion-parameter models. […] The post Two AMD Ryzen AI Halo PCs Run 397B Quen 3.5 in Local Test appeared first on Geeky Gadgets.
Nauti's Take
The first test should use a real workflow and measure latency, tokens per second, power draw, and the quantization actually required. Two devices with 256 GB of shared memory sound promising for local prototypes, yet without solid benchmarks it remains unclear whether the cluster improves speed or mainly adds capacity.
Small teams should verify availability and total cost before comparing the setup with cloud spending.
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