AI Inference

Making model responses fast enough and cheap enough to use at scale.

Serving Engines

The serving engine you choose can change throughput several times over on the same GPU.

Engines:

vLLM, SGLang and TensorRT-LLM
Continuous batching and paged attention
OpenAI-compatible endpoints, so switching engines doesn't require application changes

Quantization

Smaller number formats mean fewer GPUs and faster responses. We measure the accuracy you give up on your own evaluation set, so it's a trade-off you choose knowingly.

Formats:

FP8 and INT8
4-bit AWQ and GPTQ
Side-by-side accuracy, latency and cost comparison

bash

1# Same model, same eval set: full precision vs FP8
2vllm serve meta-llama/Llama-3.1-70B-Instruct \
3  --tensor-parallel-size 4
4
5vllm serve meta-llama/Llama-3.1-70B-Instruct \
6  --quantization fp8 \
7  --kv-cache-dtype fp8 \
8  --tensor-parallel-size 2   # half the GPUs

Routing & Caching

Most requests don't need your largest model. Sending each request to the right model, and reusing work you've already paid for, cuts cost without users noticing.

Techniques:

Routing simple requests to small models and hard ones to large models
Prompt caching for long, repeated context
Semantic caching for repeated questions
Fallback across providers when one is down

Load Testing & Capacity Planning

Know how the system behaves at peak before your users find out.

What we measure:

Time to first token, tokens per second and p95 latency under realistic concurrency
GPU sizing and autoscaling thresholds
Cost per million tokens, self-hosted compared with API

AI Inference

Serving Engines

Quantization

Routing & Caching

Load Testing & Capacity Planning

Related capabilities

Private LLM Deployment

Distributed Data Centers

Model Fine-Tuning