LLM Serving
& Inference
I stand up large language models on your own hardware and make them fast: GPU-tuned serving with llama.cpp and vLLM, low latency, tight VRAM and no dependency on a third-party API.
Fast, private inference on your own hardware
Running a model well is a different problem from calling one. I serve open-weight models on-prem or in a private VPC, then tune the setup until it's genuinely fast and cheap — quantization, KV-cache and flash-attention, right-sizing the model to the task, and batching where it helps.
Everything is measured end-to-end: tokens per second, time-to-first-token, VRAM headroom and cost per request. You get an inference layer that hits your quality bar at the smallest, fastest footprint that will do the job.
What's included
Stack
llama.cpp · vLLM · CUDA · GGUF / quantization · flash-attention · Python · Docker · Linux · NVIDIA GPUs
Frequently Asked Questions
Which models can you run?
Any open-weight model — Llama, Gemma, Qwen, Mistral and similar — in quantized (GGUF) or full-precision form, sized to your hardware and latency budget.
Do I need a big GPU?
Not necessarily. Quantization and right-sizing let capable models run on a single consumer or workstation GPU. I'll match the model to the hardware you have or advise on the smallest setup that meets your target.
Can it run completely offline?
Yes — that's the default. Everything runs locally or in your private environment with no external API calls.
How is this different from using an API?
You own the model and the data path: no per-token vendor cost, no rate limits, no data leaving your infrastructure, and full control over latency and availability.
