Infrastructure for AI Builders
Deploy private LLMs, ComfyUI and GPUs on demand — all from one platform.
Launch any GPU in 60 seconds
Choose dedicated hardware by the hour, connect over SSH, and run your own stack with full root access.
Use cases
Your GPU. Any workload.
Use the machine directly with the tools and execution model you already have.
01Model training
Run your own training code, checkpoints, and distributed stack.
02Fine-tuning
Use LoRA, QLoRA, or full-parameter workflows without a managed runtime.
03Batch compute
Process datasets, embeddings, media, or scheduled CUDA jobs.
04Custom workloads
Bring containers, notebooks, renderers, or any CUDA-compatible stack.
Shell access
H100 SXM ready$ ssh root@gpu.hexgrid.cloudConnected to Hexgrid GPU Cloudroot@hexgrid:~# nvidia-smi --query-gpu=name --format=csv,noheaderNVIDIA H100 SXMroot@hexgrid:~# Direct SSH access with your keys, containers, and startup scripts.
Bring any framework, container, or training script. Hexgrid provides the machine; everything above it stays yours.
Deploy private LLM in 3-clicks
Configure, choose your hardware, and ship a private inference endpoint — no infrastructure to manage.
Choose a model
Pick from open-weight models, then dial in the precision and context length that match your latency and cost targets.
Select a GPU
Match your workload to the right accelerator — compare VRAM, datacenter region, and price per hour before you commit.
Deploy & get an API
Launch in one click and get a secure, OpenAI-compatible HTTPS endpoint you can call from anywhere.
OpenAI compatible API
Point your existing OpenAI-compatible client at HexGrid — keep the model private, run it on dedicated GPUs, no rewrites.
Swap one line — set base_url bearer_token to your endpoint. Everything else stays the same.
curl https://api.hexgrid.cloud/v1/chat/completions \
-H "Authorization: Bearer $HEXGRID_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.3-70b-instruct",
"messages": [
{ "role": "user", "content": "Explain private GPU inference." }
]
}'No more CUDA mismatch errors
We align NVIDIA drivers, CUDA, PyTorch, kernels, and model dependencies before deployment so your runtime starts correctly.
No more guessing what fits in VRAM
We tune precision, quantization, context length, batching, and memory allocation for the GPU you select.
No more inference-engine tuning
We configure vLLM, SGLang, and model-specific runtimes with optimized settings for your workload.
No more incompatible GPU deployments
We match models to supported GPU architectures and deployment modes to avoid unsupported operations and costly fallbacks.
Trending models on day zero
Private LLM deployments in minutes, train your model, and scale without managing servers. Dedicated hardware and complete isolation.
Private ComfyUI for unlimited videos
Deploy pre-provisioned MiniMax H3, Wan 2.2 or LTX Video into ComfyUI on your own private GPU.
What people make here
Native ComfyUI, Ready to use.
It's the ComfyUI you already know, on a box only you can reach. Swap checkpoints, drop in LoRAs, install whatever node pack you found on GitHub last night, then queue a hundred seeds and walk away.
Ready-to-run video workflows
Launch preconfigured workflows for MiniMax H3, Wan, LTX and more without wiring nodes, models, or dependencies yourself.
Models provisioned automatically
Required checkpoints, VAEs, text encoders, LoRAs, and runtime dependencies are installed and matched to the workflow for you.
Open ComfyUI and start generating
No setup maze, missing-model errors, or compatibility debugging. Your GPU, workflow, and models are ready from the first launch.
Your workspace
MiniMax H3 on H100 SXMWeights, custom nodes and your output folder are already in place when the box comes up.
Certified Infrastructure
All our Datacenter partners are GDPR, ISO 27001, and SOC 2 Type II compliant.
SOC 2, ISO 27001, GDPR-ready infrastructure partners
Deploy closer to users and data residency needs
B200, H200, H100, L40S-class servers across providers
From GPU selection to running pods