Why CPU-runnable SLMs win in regulated pharma
GPU procurement is a six-month problem. Quantised SLMs collapse it to a Docker pull.
When pharma teams talk about deploying AI inside their walls, the conversation almost always converges on one question: do we need a GPU farm? The honest answer for most medical workloads is no.
Modern small language models, fine-tuned on domain data and quantised to 4 or 5 bits, deliver enterprise-grade latency on commodity CPU servers. We routinely see Evarx-Med-3B respond in under 200ms per token on a 16-core box that already exists in customer datacenters.
This unlocks a different procurement story. No capex cycle. No GPU shortage waiting list. No ML platform team hire. Just a Docker image, an existing server, and an air-gapped network if you need one.
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