How to Deploy an LLM On-Premise: A Practical Guide

Self-hosting a language model is not one task — it is a chain of decisions, each with a real trade-off. Get the model right and the hardware wrong and you pay for idle GPUs. Get the runtime right and the quantization wrong and you leave latency on the table. This guide walks the full sequence so you can make each call deliberately instead of by default.

The single most common mistake is choosing the largest model you can find. A frontier model sized for every possible task is overkill for a workload that does one thing well. Start from the task: what does the model actually need to do, and what accuracy bar does the workload set?

A smaller model that is fine-tuned and compressed for your specific workload will often match a much larger general model on the task that matters — at a fraction of the memory, latency, and cost. Size the model to the job.

The model's parameter count and precision determine your memory floor. A rough rule: a model with N billion parameters in FP16 needs roughly 2N GB of VRAM just for the weights, plus headroom for the KV cache and activations. Quantization changes that math dramatically — an INT4 model needs roughly a quarter of the memory of its FP16 equivalent.

Your latency target then decides how many GPUs and how much concurrency you need. Batch size, sequence length, and throughput all feed the same equation. Size for the workload you actually run, not the theoretical maximum.

The runtime is the layer that turns weights into a usable service. The main options:

• vLLM — high-throughput serving with continuous batching and PagedAttention. The default for GPU production serving.
• MLX — Apple Silicon optimized. The right choice for on-device and edge deployments on Mac hardware.
• llama.cpp — lightweight, CPU-friendly, and portable. Good for constrained environments and quick tests.

Your choice depends on your hardware and your throughput needs. For GPU-heavy production, vLLM is the standard starting point.

Quantization reduces the precision of the weights to fit more model into less memory and run faster. The two approaches that matter in practice:

• Post-training quantization (PTQ) — like AWQ or GPTQ — compresses an existing model without retraining. Fast to apply, and with good calibration it preserves most of the quality.
• Pruning removes parameters the model does not use, shrinking the model itself before quantization.

The right recipe depends on your model and hardware. The key is to measure quality after compression, not assume it — a well-calibrated INT4 model can be near-lossless on your task.

An on-prem model is software you now operate. That means you need the same discipline as any production service: request logging, latency and throughput metrics, error tracking, and — most importantly — an evaluation loop that tells you whether the model is still meeting the accuracy bar as you upgrade it.

The teams that succeed with on-prem treat the model as a versioned product: they A/B test a new release against the incumbent on a real workload and decide on measured results, not claims.

The steps above are exactly what a model vendor does for you. A production-shaped artifact — already pruned, quantized, and validated for a serving runtime — removes most of the engineering risk. If you would rather evaluate a model on your hardware than build the pipeline yourself, that is the path most enterprises take.