Inference Engineering Services
Higher throughput. Lower cost. Same model.
We engineer production-grade inference for the GPUs you already operate. Custom CUDA kernels, FP8 / INT4 quantization, continuous batching, speculative decoding — delivered as fixed-fee engagements with code in your repository.
- 2.4×
- FireAttention vs vLLM 0.6
- −42%
- Cost / 1M tokens vs baseline
- < 5d
- Discovery → signed SoW
- Fixed
- Fee — no hourly billing
Five productised engagements
Fixed scope, fixed fee, concrete deliverables. Need something custom? We do that too.
Performance Audit
Two-week deep profile of your inference stack. Top 3 bottlenecks, quantified gain estimates, prioritised roadmap.
Learn moreCustom Kernel Development
Hand-tuned CUDA / Triton / CUTLASS kernels for your hottest forward-pass operations. 1.5-3× per kernel.
Learn moreMigration Engagement
Move from OpenAI / Anthropic / vLLM to optimised inference. Side-by-side shadow traffic, zero-downtime cutover.
Learn moreOn-Prem / VPC Deployment
FireAttention runtime inside your AWS / GCP / Azure / on-prem cluster. Air-gapped supported.
Learn moreCustom Model Optimization
Take a research checkpoint to production. FP8/INT4 with quality validation, custom draft for speculative decoding, deployment artifacts.
Learn moreBrowse the full Solutions catalog →
Detailed deliverables, methodology, and outcomes for every engagement type.
View SolutionsReal engineers, embedded with your team.
Every Enterprise customer gets a Forward Deployed Engineer assigned at signup. Not a salesperson, not a CSM — an inference engineer who can write code, debug your prompts, and ship optimisations alongside your team.
Migration assistance
We benchmark your current setup, design a migration plan, and help you cut over without downtime.
On-call for production
Direct line to the engineers who built FireAttention. P1 response in under 1 hour, 24/7.
Embedded in your Slack
Shared Slack channel with your TAM and on-call rotation. Talk to humans, not a ticket queue.
Faster than every reference baseline
Public benchmarks against stock vLLM and TGI on identical hardware. Reproducible — full methodology in the docs.
Throughput vs reference (8× H100 SXM, batch 32, 1K input / 256 output)
| Model | Context | Luminet (tok/s) | vLLM baseline | Speedup | P50 TTFT | Cost |
|---|---|---|---|---|---|---|
| DeepSeek V4 | 256K | 480 | 195 | 2.46× | 95 ms | -45% |
| Kimi K2.6 | 200K | 320 | 132 | 2.42× | 130 ms | -42% |
| GLM-5 | 1M | 410 | 168 | 2.44× | 110 ms | -44% |
| Llama 5 Instruct | 2M | 285 | 118 | 2.42× | 145 ms | -41% |
| Nemotron Ultra 340B | 256K | 240 | 96 | 2.50× | 175 ms | -38% |
| Qwen3-Next 80B A3B | 256K | 640 | 245 | 2.61× | 78 ms | -48% |
Don't want to run anything yourself?
We also operate a hosted inference platform — the same FireAttention runtime, but managed by us. Per-token billing across 30+ open and closed models, OpenAI-compatible API.
Ship inference at the speed
your users deserve.
Join the teams routing billions of tokens through Luminet's inference cloud every month.