Serving Systems
Prompt caching, gateways and routing, token accounting, and multi-tenant isolation.
8concepts
66flashcards
69minutes of reading
- 01 Autoscaling and Cold Starts in LLM Serving Why GPU utilisation is a useless autoscaling signal for LLM servers, what a cold start actually costs, and how to scale a fleet whose new replicas take minutes to become useful.
- 02 LLM Gateways and Routing Why every serious LLM deployment ends up behind a gateway, and how to choose between LiteLLM, Portkey, OpenRouter, and rolling your own.
- 03 Prompt Caching Infrastructure How Anthropic, OpenAI, and vLLM let you reuse the KV cache of repeated prefixes, what the cache key actually is, and the patterns that turn cache hit rate into a real bill reduction.
- 04 Serving SLOs: TTFT, TPOT and Goodput Why tokens per second is the wrong number to optimise, how TTFT and TPOT split the latency budget, and what goodput measures that throughput hides.
- 05 Token Accounting, Billing, and Quotas Why a single token counter is not enough, how to attribute spend across users and features without losing your mind, and the patterns that prevent one bad actor from spending the whole month's budget on a Tuesday afternoon.
- 06 Disaggregated Prefill and Decode Serving Why prefill and decode want opposite hardware and parallelism, how splitting them across separate GPU pools raises goodput, and what the KV cache transfer costs.
- 07 Multi-Tenant Serving and Isolation Serving many tenants from one model is cheap and easy; giving each tenant their own fine-tune is expensive and hard. S-LoRA and per-request LoRA serving collapse the trade-off, but only for tenants who can share a base model.
- 08 Prefix-Aware Routing and KV Cache Reuse Why load-balancing LLM requests round-robin throws away computed KV cache, and how routing on prompt prefix turns a fleet's caches into a shared asset.