Pods
You get the machine.
- Root SSH and a browser IDE — it's your box
- Ready-made templates, or your own image
- Billed by the second while it runs
Pick this for training, fine-tuning, notebooks, rendering — anything you babysit.
The task-first GPU cloud
Tell Meshive what you're running and it lands on the right GPU in under two minutes.
Runs what you already use
Solutions
What are you running?
Meshive is organised around the job, not the rack. Pick the thing you actually need done and we put it on hardware that fits 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.
Pods vs Serverless
Two ways to run. Pick one.
The difference is what you get handed: a machine you log into, or a URL you call.
Serverless
You get an endpoint.
- An OpenAI-compatible URL — change one line of code
- Models ready to call, or bring your own
- Billed per call, scales to zero between them
OpenAI(base_url="https://qwen3-a1b2.inference.meshive.ai/v1")
Pick this for apps and APIs, bursty traffic, anything you'd rather not keep warm.
Same GPUs underneath. You can use both in one workspace.
How it works
From an empty console to a running pod
1
Configure
Start from a template: JupyterLab, ComfyUI, vLLM. Or bring your own image.
2
Select hardware
Say how much GPU, vCPU and RAM the job needs. We place it on a host that has them free.
3
Connect and run
HTTPS, SSH and private networking are configured before the pod hands over.
Pricing
Honest, market‑leading pricing
Premium GPUs, high-core CPUs, and fast NVMe storage — billed per second with no hidden fees. Just clear rates, among the most competitive you’ll find anywhere.
RTX 5090
GPU$0.57/ GPU·hour
RTX PRO 5000
GPU$0.85/ GPU·hour
RTX PRO 6000
GPU$1.61/ GPU·hour
CPU Pod
32 vCPU · 64GB RAM$0.26/ hr
1TB SSD Storage
NVMe-backed$60/ mo
* Starting rates shown. Live pricing may vary by availability and region.
Security
What sits between your data and everyone else
Three layers: the link between hosts, the route in from your machine, and what we are certified for.
Between hosts
VPN-encrypted transmission between all cluster nodes. Your data never travels unprotected.
From your machine
Native HTTPS end-to-end encryption for all client communications. TLS 1.3 standard.
Compliance
SOC 2 Type II certification is in progress. We will say so here when it lands.
Benefits
You bring the workload. That's all Meshive needs.
Meshive is a task-first GPU cloud for AI training, inference, rendering, and serverless model serving.
- Under 2 min
- From picking a template to a running pod. JupyterLab, ComfyUI and vLLM are ready to go, or bring your own image.
- Up to 70% less
- Than the major clouds, billed by the hour. The meter stops when you stop the pod — there is no reserved capacity to justify.
- 1 card per pod
- A non-virtualized GPU in a container of your own. Traffic between hosts runs over an encrypted link.
What's new in Meshive
All posts
NewsThis Week in AI, GPU, and LLM: Who Gets to Hit the Brakes
Sep 18, 2026
NewsThis Week in AI, GPU, and LLM: The Price of Intelligence Went Up
Sep 7, 2026
NewsThis Week in AI, GPU, and LLM: Compute Got More Expensive While Intelligence Got Cheaper
Aug 25, 2026
NewsThis Week in AI, GPU, and LLM: Why Compute Just Became Geopolitics
Jul 22, 2026
NewsThis Week in AI, GPU, and LLM: The Agent Wars Go Public, and So Does the Memory Crunch
Jul 12, 2026
NewsThis Week in AI, GPU, and LLM: Capability Gets Cheaper, Compute Gets Tighter
May 2, 2026
NewsThis Week in AI, GPU, and LLM: The Compute Race Has Become the Product
Apr 24, 2026
NewsThis Week in AI, GPU, and LLM: Why Deployment Just Became the Real Differentiator
Apr 17, 2026FAQ
Questions we get asked
Hardware, billing, storage, and how Serverless differs from a pod.
What is Meshive?
Meshive is a task-first GPU cloud for AI training, inference, rendering, and serverless model serving. It provides instant access to high-performance NVIDIA GPUs, including RTX 5090, RTX PRO 5000, and RTX PRO 6000, at up to 70% lower cost than traditional hyperscalers, billed in fixed one-minute intervals, with zero cluster provisioning.
What GPU models are available on Meshive?
Meshive supports a broad range of NVIDIA GPUs, including the RTX 5090 (32GB VRAM), RTX PRO 5000 (48GB VRAM), and RTX PRO 6000 (96GB VRAM) for LLM fine-tuning, vLLM inference, and ComfyUI rendering.
How does Meshive reduce GPU cloud costs by up to 70%?
Meshive operates an automated distributed compute marketplace connecting verified high-performance hardware hosts worldwide. Automated driver installation, health checks, and Kubernetes clustering eliminate traditional data center overhead, passing direct cost savings to users.
How does Meshive Serverless Inference work?
Meshive Serverless provides OpenAI-compatible HTTPS endpoints for LLM text, image, and video generation. You can deploy models with one click, autoscale within a replica range you set, and stream or receive async webhook callbacks directly to your S3, Cloudflare R2, or MinIO storage.
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.
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