Inference Engineering and Optimisation
A model that works in a notebook still has to answer thousands of requests a day at a price that makes sense. Inference engineering is the craft of making that happen: same quality, less waiting, a smaller bill.
In short. Inference Engineering and Optimisation is one of Matrida Infotech's AI services. Serving stacks tuned for speed and cost: quantisation, batching, caching, speculative decoding and GPU planning, measured on your own traffic. Typical stack: vLLM · TensorRT-LLM · llama.cpp · Triton · Kubernetes · load-testing harnesses.
Sound familiar?
- Responses are too slow for the product
- The model bill grows faster than usage
- GPUs sit idle half the day and are overloaded the other half
- You do not know what one request really costs
Capabilities
- Latency and throughput tuning
- Quantisation
- Speculative decoding
- Batching and caching
- GPU capacity planning
- Model routing and cost control
What we build
- Load tests on your real traffic shape, so every number is measured, not borrowed from a benchmark
- Quantisation, batching, caching and speculative decoding, each kept only if it holds quality
- Routing that sends easy requests to small models and hard ones to strong models
- Capacity plans for GPUs: how many, when, and what to do on a spike
- Cost per request and per feature, reported before and after
What you get
- Tuned serving stack
- Before and after report: latency, throughput, cost
- Capacity and scaling plan
- Runbook for the on-call team
Tools and frameworks
- vLLM
- TensorRT-LLM
- llama.cpp
- Ollama
- Triton
- Kubernetes
Where it fits: Retail & E-commerce, Logistics & Supply Chain.
Questions
Will optimisation hurt answer quality?
Some techniques can, so we measure. Each change is scored on your evaluation set and kept only if quality holds within the margin you agreed.
Is it cheaper to host our own model or use an API?
It depends on volume, latency needs and data rules. At low volume an API is usually cheaper; at steady high volume or with strict data rules, self-hosting can win. Our LLM cost calculator gives a first estimate, and a load test settles it.
What is speculative decoding?
A small, fast model guesses the next few words and the large model checks them all at once. When the guesses are right, answers arrive sooner at the same quality. It pays off for some workloads and not others, so we test it before recommending it.
Related services
AI Agent Development
We build AI agents that complete multi-step work inside your systems, with guardrails, human approval and full audit logs.
Learn more →RAG & Enterprise Knowledge Assistants
Private assistants that answer from your documents with citations, permission-aware retrieval and measured accuracy.
Learn more →Generative AI Application Development
Customer-facing and internal LLM products, from document drafting to AI copilots inside your SaaS.
Learn more →Planning Inference Engineering? Tell us the workflow.
Say what is going wrong today. We reply with how inference engineering and optimisation would handle it, a rough scope and the first thing to build.
- You would get: tuned serving stack
- You would get: before and after report: latency, throughput, cost
- You would get: capacity and scaling plan