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I Started Moving Inference In-House

nicholasreese.com6 points0 comments
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The author decided to move AI inference for USA Arms onto hardware he owns after encountering model refusals and unpredictable behavior from hosted models. Having transitioned from decades of outsourced human data work to AI-driven processes, he rebuilt his inference harness across multiple iterations and favored Qwen 3.8 27B for its reliability on local, smaller models. Price cuts on hosted models like Luna initially pushed heavy use of cloud inference, but repeated refusals on routine tasks (FFL identification, ammo-dealer pages, range policies) accelerated the shift to internal compute to maintain control, privacy, and consistent output for a controversial vertical.

He built three Linux-based rack servers, each with four ASRock Radeon AI PRO R9700 (32 GB) GPUs, an AMD EPYC 7452 CPU, 128 GB ECC RAM, 1 TB NVMe, and robust cooling/power, spending about $10,734 per machine ($32,202 total). On his bench, twelve R9700 GPUs processed ~175 validated jobs/min versus 12 jobs/min on six Mac Studios (~15x throughput) and also run CI/CD, saving ~$300-$450/month in rented compute. Macs remain in the stack for large-model experiments and auditing. He published a 93-page build guide and video documenting parts, BIOS, wiring, tuning, and costs so others can replicate the setup.

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