LLM Serving Optimisation Tools and Techniques

You do not solve the LLM trade-off triangle with one tool.
You solve it by stacking the right ones.

Here are the five layers that actually move the needle:

1. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹

Use quantisation before deployment to shrink weights and reduce bandwidth pressure.
In the right setup, this can cut infrastructure needs dramatically without hurting quality.

1. 𝗦𝗲𝗿𝘃𝗲 𝗶𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆

vLLM helps you use hardware properly with continuous batching, PagedAttention, and prefix caching.
That means better throughput and less wasted memory.

1. 𝗦𝗽𝗲𝗲𝗱 𝘂𝗽 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴

Speculative decoding pairs a fast draft path with a larger target model.
The result: faster token generation on repetitive, high-context tasks.

1. 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆

GuideLLM lets you test real LLM traffic patterns and measure TTFT, ITL, and throughput.
If you do not measure it, you will overbuy hardware.

1. 𝗦𝗽𝗹𝗶𝘁 𝗽𝗿𝗲𝗳𝗶𝗹𝗹 𝗳𝗿𝗼𝗺 𝗱𝗲𝗰𝗼𝗱𝗲

llm-d separates the compute-heavy prompt phase from the memory-bound generation phase.
That lets each part scale on the hardware it actually needs.

The pattern is simple:

• compress first
• serve efficiently
• accelerate decoding
• test under load
• separate the phases

That is how you get major cost reduction without sacrificing model quality.

If you are building LLM infrastructure, which layer are you optimising first?