Premium Inference: Serving Fast Tokens for Agentic AI
AI demand is heating up. Models are getting larger, more capable, and more expensive to serve, and every new release raises expectations for what an AI…
- Models are getting larger, more capable, and more expensive to serve, and every new release raises expectations for what an AI experience should feel like.
- TL;DR Premium inference is fast, responsive serving for large, intelligent models.
- OpenAI reported a long-horizon Codex agent running roughly 25 hours and consuming approximately 13 million tokens.
AI demand is heating up. Models are getting larger, more capable, and more expensive to serve, and every new release raises expectations for what an AI experience should feel like. TL;DR Premium inference is fast, responsive serving for large, intelligent models. Agentic AI has turned it into a distinct product tier rather than a nice-to-have. Agent loops multiply latency. OpenAI reported a long-horizon Codex agent running roughly 25 hours and consuming approximately 13 million tokens. Slow decode delays the entire product, not just one response. The market is already pricing speed. MiniMax charges $2.40 per 1 M high-speed output tokens versus $1.20 for standard output tokens. OpenAI, Anthropic, and Fireworks all ship fast tiers at a premium. Delivering premium inference requires disaggregation: GPUs for compute-heavy prefill, SambaNova RDUs for latency-sensitive decode, with a serving layer routing between them. SambaRack SN50 runs MiniMax M2.7 at roughly 820 tokens per second (TPS) for premium interactivity, or roughly 420 TPS when the goal is throughput and concurrency. For inference providers and neoclouds, premium inference is a tier they can price, not a cost they have to absorb.
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