Cloud Use Case

LLM Inference & API Deployment

Deploy large language models for production inference with vLLM, TGI, Ollama, or custom serving stacks on NVIDIA GPUs. Low latency, fixed pricing, 24 global data centers.

$4/mo

Starting price

Global Data Centers

99.9%

Uptime SLA

24/7

Human Support

Why Deploy LLM Inference on OMC Cloud

Production LLM inference needs low-latency GPU compute, high availability, and predictable costs. API providers charge per token — costs scale unpredictably with usage. Self-hosting on OMC Cloud gives you fixed monthly pricing regardless of token volume.

Run vLLM, Text Generation Inference (TGI), Ollama, LiteLLM, or any serving framework on NVIDIA L40S or H100 GPUs. Deploy in 24 global data centers for lowest latency to your users. Full root access for custom optimizations — quantization, batching, KV-cache tuning.

Key Benefits

Fixed Cost per GPU

No per-token billing. Serve unlimited tokens at a fixed monthly price.

vLLM & TGI Ready

Production-grade serving frameworks with continuous batching and PagedAttention.

24 Global DCs

Deploy inference closest to your users for sub-100ms response times.

Ollama Compatible

Run Ollama for simple local-style LLM deployment on cloud GPUs.

Auto Scaling

Add GPU instances during peak hours, scale down at night.

99.9% Uptime SLA

Production-grade reliability for customer-facing AI features.

NVMe Model Storage

Fast model loading from NVMe. No cold start delays.

Full API Access

Provision and manage inference servers via REST API.

How It Works

Choose

Select data center, GPU/CPU, RAM, storage, and OS.

Deploy

Server ready in under 60 seconds via console or API.

Go Live

Install your stack, configure, launch with 24/7 support.

Cloud vs On-Premise vs Shared

Feature	OMC Cloud	On-Premise	Shared
Upfront Cost	None — from $4/mo	$5,000-50,000+	$5-20/mo
Performance	Dedicated NVMe	Dedicated but fixed	Shared
Scaling	Instant	Weeks	Limited
Control	Full root access	Full	Very limited
Uptime	99.9% SLA	Depends on you	95-99%
Backups	Automated, 14 points	DIY	Basic
Global Reach	24 data centers	Single location	Shared

Recommended Configurations

GPU instances optimized for inference throughput.

Small Models

$49/mo

per month

• NVIDIA L40S (48GB)
• 4 vCPU, 16 GB RAM
• 100 GB NVMe
• 7B-13B models
• Ollama, vLLM

Deploy Now

Production

$89/mo

per month

• NVIDIA L40S (48GB)
• 8 vCPU, 32 GB RAM
• 200 GB NVMe
• Up to 34B models
• vLLM, TGI with batching

Deploy Now

High Throughput

$199/mo

per month

• NVIDIA H100 (80GB)
• 16 vCPU, 64 GB RAM
• 500 GB NVMe
• 70B models
• High concurrency serving

Deploy Now

Technical Specifications

GPU: NVIDIA H100, L40S, A16

Serving: vLLM, TGI, Ollama, LiteLLM, Triton

Quantization: GPTQ, AWQ, GGUF, bitsandbytes

Models: Llama 3, Gemma 4, Mistral, Mixtral, Phi-3, Command-R

Latency: Sub-100ms TTFT in nearest DC

API: OpenAI-compatible endpoints via vLLM

Uptime: 99.9% SLA

Network: Up to 40 Gbps

Frequently Asked Questions

How much does LLM inference hosting cost?+

From $49/mo for 7B models on L40S to $199/mo for 70B models on H100. Fixed pricing — no per-token charges.

What serving frameworks do you support?+

Any: vLLM, TGI (Text Generation Inference), Ollama, LiteLLM, Triton Inference Server. Full root access.

Can I serve quantized models?+

Yes. GPTQ, AWQ, GGUF, and bitsandbytes quantization all supported. Lower VRAM usage means smaller GPU instances.

Is the API OpenAI-compatible?+

Yes if you use vLLM — it provides an OpenAI-compatible API endpoint out of the box. Drop-in replacement for GPT API calls.

How do I handle traffic spikes?+

Deploy multiple inference servers behind a load balancer. Our API supports programmatic provisioning for auto-scaling.

What latency can I expect?+

Time-to-first-token (TTFT) under 100ms when deployed in the data center nearest to your users.

Related Use Cases

LLM Training

Fine-tune models before deployment

AI Agents

Power agents with your LLM API

RAG Pipeline

Augment LLM responses with your data

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