Serverless LLM Platform - A Technology Architecture Blueprint for Function-Style LLM Deployment

The winning system is not "a conventional function platform with a GPU attached." It is a model-residency and token-scheduling platform exposed as functions. Its fast path is created by compiling most functions into safe prompt bytecode, doing static work at deployment, keeping the right weights and prefixes resident, routing straight to the cell most likely to produce the first token soonest, batching only within each request's deadline budget, isolating general user code in CPU microVMs while leaving shared inference outside, reserving resources before accepting work, and streaming immediately while accounting at the point of execution. KVM belongs to the security architecture; shared inference cells provide the execution architecture; and scale-to-zero is a selectable operating mode, not a universal rule. Performance improves through sensible residency, short request paths, fair scheduling, controlled batching, and transparent service classes — never through a single absolute latency promise. Call to action: Begin with the simulator and the single-host hot slice. Establish the latency budget, prove the inference and scheduling thesis on one model and one accelerator, and let measured workload data — not architectural ambition — pace the path to a multiplexed, multi-region, density-optimized platform.

·       TTFT (time-to-first-token) — elapsed time from request arrival to the first streamed token. ·       Prefill — processing the input prompt to populate KV state before decoding begins. ·       Decode — autoregressive generation of output tokens, one iteration at a time. ·       KV cache / KV pages — per-sequence key/value attention state; here managed as fixed-size paged allocations. ·       Prefix cache — reusable KV for a shared leading token sequence, keyed by model, tokenizer, adapter, exact tokens, attention options, and privacy namespace. ·       Continuous batching — forming and re-forming batches at each model iteration rather than per request. ·       Inference cell — a persistent, shared execution unit owning weights, KV memory, batching, and sampling for a model version. ·       Adapter — a small per-function low-rank weight delta applied over a resident base model. ·       MicroVM — a minimal KVM virtual machine used to isolate untrusted CPU function code (no accelerator, no default network). ·       Residency — where a model's weights currently live across the HBM → host RAM → NVMe → rack → region hierarchy. ·       Alias — a mutable atomic pointer (e.g., production) to an immutable function/model version. ·       Goodput — useful completed work per unit time (distinct from raw throughput).