GPU cloud solutions, by workload
Meshive is organised around the job, not the rack. Pick the thing you are actually running and we put it on hardware sized for it — billed by the hour, with no cluster to provision.
Six workloads, six ways in
Fine-tuning, LLM inference, ComfyUI, Blender rendering, simulation and GPU notebooks — six workloads, each on the NVIDIA card that suits it.
RTX PRO 6000 · 96GBFine-tuning
LoRA and full fine-tunes on 8B–70B models. Mount a volume, run your trainer, keep the checkpoints.
L40S · 48GBInference serving
vLLM or SGLang behind an OpenAI-compatible endpoint — or skip the pod entirely and go Serverless.
RTX 5090 · 32GBImage & video generation
ComfyUI, FLUX and Wan 2.x workflows, from a single run to an overnight batch.
RTX PRO 5000 · 48GB3D rendering
Blender Cycles and OptiX frame batches on workstation cards, billed by the hour you use.
RTX PRO 6000 · 96GBSimulation & physical AI
Headless CUDA rollouts and robotics sims that need a whole GPU, not a shared slice.
RTX 4090 · 24GBNotebooks & experiments
JupyterLab in one click for sweeps, evals and the experiments that do not deserve a cluster.
Browse by industry instead
The same platform, described by who is running on it.
GPU cloud, by industry
Pick a card and start the pod
Sign up, choose the GPU, and the pod is yours in under two minutes. It bills by the hour and stops when you stop it.
Already a user? Invite friends and earn 15% of their first top-up.