AI Model Deployment
Deploy LLMs and custom models on dedicated GPU infrastructure. vLLM, TGI, Triton, and SGLang runtimes. Private hardware, no sharing, fixed pricing.
At a Glance
Supported Models
Pre-quantized, optimized, ready to deploy
| Model | Params | Quantization | VRAM | Ideal For |
|---|---|---|---|---|
| Llama 3.1 70B | 70B | AWQ/GPTQ 4-bit | 40 GB | Chat, reasoning, code |
| Llama 3.1 8B | 8B | AWQ/GPTQ 4-bit | 8 GB | Fast chat, classification |
| Mixtral 8x7B | 46.7B | AWQ/GPTQ 4-bit | 26 GB | MoE, multilingual, code |
| Qwen 2.5 72B | 72B | AWQ/GPTQ 4-bit | 40 GB | Multilingual, math, code |
| DeepSeek Coder 33B | 33B | AWQ/GPTQ 4-bit | 20 GB | Code generation, completion |
| Custom / Fine-tuned | Any | Any | Custom | Your LoRA/QLoRA adapters |
Runtimes & Features
Best-in-class inference engines, optimized for your hardware
vLLM
PagedAttention, continuous batching, prefix caching. Highest throughput for LLM serving. OpenAI-compatible API.
TGI
Hugging Face Text Generation Inference. Tensor parallelism, speculative decoding, watermarking. Enterprise features.
Triton
NVIDIA Triton Inference Server. Multi-model, multi-framework, ensemble pipelines. Model repository management.
SGLang
Structured generation, RadixAttention, tensor parallelism. Fastest for constrained decoding and agentic workflows.
How It Works
From model selection to OpenAI-compatible endpoint in minutes.
Your Models, Your Hardware
No shared tenancy. Your model weights never leave your dedicated GPUs. No data used for training. Full control over inference parameters.
And when you need help, our ML engineers are a Slack message away.
Deploy Your Models on Dedicated GPUs
vLLM, TGI, Triton, SGLang. Private hardware. Fixed pricing. Expert support.