01
PagedAttention & continuous batching
Efficient KV-cache memory management and continuous batching, so each GPU serves far more concurrent requests than naive serving.
Technologies — Cloud, DevOps & AI
vLLM & open-source LLM self-hosting
We deploy and tune self-hosted open-source LLMs with vLLM on dedicated GPUs, and model the per-token cost against hosted APIs before you commit.
Core capabilities
Why we build with vLLM & Local LLMs
02
Multi-GPU tensor parallelism
Splitting 70B-class models across multiple NVIDIA GPUs, tuned for time-to-first-token and throughput.
03
OpenAI-compatible API
Serving models through the standard OpenAI API, so existing tools and SDKs work with a base-URL change.
Use cases
Where vLLM & Local LLMs fits
Private LLM infrastructure
Models running inside your private VPC, so prompts and outputs stay on infrastructure you control, for healthcare, finance and other regulated work.
High-volume synthetic data generation
Large synthetic datasets generated without per-token API bills.
How we staff it
vLLM & Local LLMs engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
It depends on volume, model size and how evenly traffic is spread. A dedicated GPU costs the same whether it is busy or idle, so self-hosting tends to win only at sustained high volume; we model your token volumes against GPU and API prices before recommending either.
vLLM's PagedAttention manages KV-cache memory like virtual memory in an OS, which keeps fragmentation close to zero and lets far larger batches fit on each GPU.
Ecosystem
Related technologies
Planning a vLLM & Local LLMs project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.