Admission control, not silent queueing.
When capacity is exhausted, refuse clearly and quickly. An unbounded queue turns a capacity problem into a timeout cascade.
Weights are
the easy part.
Downloading a model takes minutes. Serving it reliably to concurrent users, at a context length real documents require, is the engineering.
READ THE DEFINITION ↓SERVING · BATCHING · LIMITS
01 / DEFINITION
The short answer first.What is an on-premise LLM deployment?
An on-premise LLM deployment is a language model served from an organisation's own hardware through an internal API. It consists of the weights, an inference server that batches and schedules requests, and the surrounding integration, monitoring and capacity management.
02 / THE SERVING LAYER
What an inference server is actually doing.Running a model in a script and serving it to users are different problems. The serving layer handles concurrency, memory management for the key-value cache, scheduling, streaming, and admission control when capacity is exhausted. The single feature that matters most is continuous batching: new requests join the batch as earlier ones finish, rather than waiting for a whole batch to complete.
The difference is large. Naive per-request execution leaves the GPU idle between requests and under-uses it during them; continuous batching keeps it saturated and multiplies effective throughput for concurrent workloads. Any serving choice should be assessed on whether it does this well before anything else.
| Approach | Suits | Trade-off |
|---|---|---|
| Dedicated inference server with continuous batching | Interactive multi-user services | More moving parts; a real deployment to operate |
| Lightweight local runtime | Single-user tools, desktops, small pilots | Limited concurrency; not a shared service |
| Custom framework code | Research and unusual model architectures | You implement batching and scheduling yourself |
| Batch offline processing | Bulk document review with no latency requirement | No interactive use; throughput optimised instead |
03 / THROUGHPUT AGAINST LATENCY
One dial, two constituencies.You cannot optimise
for both.
Larger batches raise total throughput and lengthen the wait for each individual response. The right setting depends on who is waiting.
A supervision model reviewing every document an organisation produces overnight is a throughput workload: batch aggressively, accept per-request latency, maximise documents per hour. An assistant a person is watching is a latency workload: smaller batches, prioritise time to first token, accept lower total throughput. Running both on one deployment with one configuration disappoints both.
Two practical measures follow. Separate the queues, so a bulk job cannot starve interactive requests. And set an explicit context limit rather than allowing the model's maximum: a single 128k-token request can consume the cache budget of many normal ones, which is how an otherwise healthy deployment starts refusing admissions. Chunking long documents in the retrieval layer is usually both cheaper and better for quality — see RAG evaluation.
04 / OPERATING IT
The details that decide reliability.Health checks that run a real inference.
A process that is alive but cannot allocate cache memory will pass a port check and fail every request.
Pin and record every version.
Weights, quantisation, serving version, decoding parameters. Without them no evaluation result can be attributed to a cause.
One further detail is worth building early: no silent fallback. Resilience features that route to an external provider when the local endpoint fails will, in an environment chosen for its data boundary, quietly void the reason the deployment exists. The correct behaviour on failure is to stop and report. See private AI inference.
05 / QUESTIONS
Asked while standing up a local inference service.What is continuous batching and why does it matter?
+
It lets new requests join an in-flight batch as earlier ones complete, instead of waiting for the whole batch to finish. For concurrent workloads it is the difference between a GPU that is mostly idle and one that is saturated, and it is the first thing to check in any serving stack.
Should we expose an OpenAI-compatible API internally?
+
It is a pragmatic choice: client libraries, tooling and application code work unchanged, and it keeps the option of swapping the backend. The compatibility surface is a convention rather than a standard, so pin the behaviour you depend on and test it.
How do we handle very long documents?
+
Chunk in the retrieval layer and set an explicit context limit on the serving layer. Very long contexts consume cache memory disproportionately and frequently reduce answer quality, so the maximum supported context is rarely the right operating configuration.
Can one deployment serve interactive and batch workloads?
+
Only with separate queues and different batching settings, and ideally separate capacity. A single configuration optimised for neither leaves interactive users waiting behind bulk work.
What monitoring does a local LLM service need?
+
Requests and tokens per second, time to first token, cache utilisation and admission rejections, plus GPU memory and temperature. Cache pressure and rejections are the capacity signals that actually predict user-visible failure.
Serve it
properly.
Privilege AI builds local inference and application integration with explicit hardware, network and operational constraints.