Serve a model, not a fleet

Run vLLM or SGLang on a GPU you control, or hand the whole thing to Serverless and just call an OpenAI-compatible endpoint. Either way it is minutes, not a capacity request.

Start a pod See pricing

Light pulses racing outward from a bright core along parallel channels

A laptop showing a dark code editor in a dim room

vLLM and SGLang ship configured

Both templates come up as an OpenAI-compatible server. Change base_url in your existing client and leave the rest of the code alone.

Rows of dark server racks in a data hall, status LEDs in teal and blue

Match the card to the model, not the other way round

An L4 serves embeddings for pennies and an H100 keeps a 70B fed. Moving between them is a redeploy, not a renegotiation.

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Or run no pod at all

Serverless gives you the same OpenAI-compatible endpoint with models already deployed, billed per call and scaled to zero between them.

Ready to launch

Templates for this work

Official images with the drivers and the stack already in place.

vLLM
SGLang
NVIDIA Triton Inference Server
NVIDIA NIM
Text Embeddings Inference
Ollama
OpenWebUI

Hardware

Cards that suit this work

Every Meshive pod gets a whole, non-virtualized GPU. These are the ones we would reach for first.

NVIDIA L40S48GB VRAM · Workstation48GB at a steadier price than H100
H100 SXM580GB VRAM · Data CenterHighest throughput for the 70B class
RTX PRO 6000 Blackwell96GB VRAM · Workstation96GB when the context window is long
NVIDIA L424GB VRAM · WorkstationSmall models and embeddings, cheaply

All GPUs and current pricing

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Getting started

Three steps

1Start the vLLM or SGLang template and pass the model id you want served.
2The server comes up OpenAI-compatible - point your client at it and change nothing else.
3Scale by adding pods, or move the model to Serverless and stop managing them.