A returning agent may reuse a large conversation while adding only a small new instruction. Serving every turn as an unrelated request can repeat expensive computation, yet concentrating sessions on one worker can leave other capacity idle. The infrastructure problem is balancing reuse, load and failure recovery.
Prime Intellect’s October 2 Prime Inference launch describes serverless endpoints and reserved capacity across multiple data centers. Its GLM-5.3 example separates prefill from decode, uses cache-aware routing and adds host-memory caching. The company reports nearly a 40% reduction in p90 inter-token latency in its tests. It also reports uptime since an earlier endpoint launch; that historical observation should not become a promise that future service cannot fail. Read the primary source.
Cache hits have an opportunity cost
Routing toward a cached prefix saves computation only if the destination’s queue does not erase the benefit. A useful benchmark mixes returning sessions with genuinely cold arrivals and records both user latency and hardware efficiency. Measuring one favorable repeated prompt would conceal the contention the system is designed to manage.
The immediate question for developers is whether this architecture improves their full session workload at an acceptable cost. A comparison should include failover, context movement, tool-call correctness and the tails of the latency distribution. Reserved capacity and serverless service also create different utilization and billing assumptions.
Cache-aware serving could make sustained agents less expensive if conversation reuse remains predictable and recovery works under pressure. Independent workload replay and clearly scoped reliability reporting would strengthen that case. The relevant outcome is dependable useful work per unit of infrastructure, rather than a token-rate record alone.
References
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