The Economics of AI Decoding Chips: Rebalancing Compute, Capacity, and Bandwidth for Efficient LLM Inference

Abstract

Every mainstream GPU is built compute-heavy and capacity-light: it pairs enormous arithmetic throughput with too little memory to hold a modern model. In contrast, large language model decoding requires little compute and a large amount of memory: a GPU's floating-point units run at single-digit-percent utilization during decoding, and the memory the workload does need is sold only bundled with yet more compute. The compute is recovered only at hyperscale, where Mixture-of-Experts (MoE) models are spread across 96--320-GPU expert-parallel clusters serving thousands of concurrent users, a scale available to a handful of operators. We formalize the inefficiency with two fixed per-chip constants. F/B, the roofline ridge point, determines whether the compute can be utilized; F/S, the compute bundled with each GB of memory, determines how much compute must be bought. We then argue for a rebalanced decode accelerator: less compute, far more commodity memory, and a deliberately lower and cheaper bandwidth. The Skymizer HTX-301, a purpose-built 28nm PCIe accelerator using commodity DDR5, occupies that design point. Its entry cost is low. A single eight-chip card holds DeepSeek-R1 671B for about \$19,000, and a 4U server of four four-chip cards serves two users at a deterministic 20.3 tokens per second each for about \$28,000. Either costs less than a single H100, while the minimum GPU deployment for the model is an eight-GPU node near \$350,000. Concurrency then scales out by adding hardware: eight 4U servers carry sixteen users for about \$224,000, two-thirds of the node's price, with the cost per token unchanged at about \$12 per million against the node's \$21. The HTX-301's decisive advantage is a supply chain free of every rationed input: it uses no high-bandwidth memory, no CoWoS, and no leading-edge logic.