Deploying LLM

On-prem LLM deployment looks hard.

It isn’t.

The real difference between a costly bottleneck and an efficient production system comes down to two kinds of optimisation.

There are two levers:

• 𝗠𝗼𝗱𝗲𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀: change the model before deployment

Examples:

• 𝗤𝘂𝗮𝗻𝘁𝗶𝘀𝗮𝘁𝗶𝗼𝗻: smaller weights, lower memory use
• 𝗦𝗽𝗮𝗿𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: fewer active parameters, less compute

• 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀: change how the model is served at runtime

Examples:

• 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗯𝗮𝘁𝗰𝗵𝗶𝗻𝗴: more requests per GPU
• 𝗣𝗮𝗴𝗲𝗱𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻: better KV cache memory use
• 𝗣𝗿𝗲𝗳𝗶𝘅 𝗰𝗮𝗰𝗵𝗶𝗻𝗴: reuse repeated prompt work

Tool: vLLM

The best on-prem setups use both:

• shrink the model
• optimise the runtime

That’s how you reduce cost, cut latency, and keep quality high.

#productionai