Deploy LLM Inference Using Vast.ai Serverless

Teams running inference-heavy workloads almost always run into the same problem: the high cost of compute. They're often forced to pay for capacity they're not using, or struggle to keep up when demand spikes.

What teams really need is scalable LLM deployment without expensive hyperscaler pricing or overprovisioned infrastructure that sits idle between peak loads.

That's where Vast.ai Serverless comes in. It's a new way to run AI inference on GPUs with automated scaling that solves three problems traditional cloud providers often exacerbate.

Why Traditional GPU Clouds Aren't Ideal for LLM Inference

LLM inference workloads are fundamentally different from training jobs. They're bursty and latency-sensitive, so they amplify traditional GPU infrastructure challenges, such as:

• Unpredictable costs when compute usage varies but capacity must be provisioned ahead of time.
• Laggy cold starts when scaling up to meet spikes in demand.
• Infrastructure overhead from manual capacity planning, instance management, and vendor lock-in.

These limitations make it difficult to run LLM inference efficiently at scale. However, Vast.ai Serverless solves this problem by routing workloads to the most cost-effective hardware and scaling compute automatically. In doing so, it eliminates the need to manage infrastructure altogether.

What Makes Vast.ai Serverless Different

The following are a few ways Vast.ai Serverless stands out:

Predictive Optimization That Anticipates Demand

Instead of reacting to spikes in demand after they occur, Vast.ai Serverless anticipates them.

The platform proactively provisions reserve GPU workers based on historical usage patterns, real-time load, and ongoing market benchmarking. This approach minimizes laggy cold starts for latency-sensitive LLM inference.

One Endpoint, Powered by a Global GPU Fleet

Vast.ai continuously benchmarks over 17,000 GPUs across 500+ locations worldwide. Our network includes everything from RTX-class consumer GPUs to enterprise-grade machines like A100s, H100s, and B200s.

But rather than locking you into a single hardware profile, Vast.ai Serverless lets you deploy one endpoint backed by our entire globally distributed GPU fleet. Multiple Workergroups per Endpoint enable requests to be routed dynamically based on workload requirements.

This real-time hardware selection ensures your unique, inference-heavy workloads run on the most cost-effective GPUs available at any given moment while also improving fault tolerance due to reduced single-point risk.

Lowest-Cost Transparent Serverless Pricing

Vast.ai bills by the second across three pricing options: On-Demand, Interruptible, and Reserved. You can start with just $5, and there are no hidden fees, tiers, or usage caps.

Plus, Vast.ai Serverless delivers AI inference cost savings of up to 75% or more compared to traditional providers with centralized infrastructure. It's the lowest-cost autoscaling GPU cloud on the market today.

How does it work? Let's take a look at the deployment process.

How to Deploy LLM Inference Serverless on Vast.ai

Running LLM inference on Vast.ai Serverless takes five steps:

1. Bring Your Own Large Language Model

Launch open-source LLMs like Llama 3 or DeepSeek, or deploy your own custom fine-tuned models. Vast.ai provides prebuilt images, including vLLM, TGI, and Oobabooga, so you can serve models with minimal setup.

2. Send an Inference Request

Call your endpoint via API to send inference requests. Vast.ai handles all orchestration behind the scenes.

3. Vast.ai Serverless Routes and Provisions Compute on Ready GPUs

Our platform automatically selects the most efficient hardware based on your performance targets as well as current availability. Reserve workers are already provisioned and ready to handle your request immediately.

4. GPU Executes and Returns Results

The selected GPU worker executes the inference request and returns results through the same API endpoint. This entire process happens seamlessly.

5. Serverless Predictively Scales Up and Down Based on Usage

As demand fluctuates, Vast.ai Serverless automatically scales capacity up or down to match. Billing stops immediately when resources are released, so you only pay for actual compute time.

Get Started with Vast.ai Serverless

Deploying LLM inference at scale doesn't have to mean choosing between performance and affordability. Vast.ai Serverless gives you both. You can run production-ready inference that automatically scales when you need it and stops charging when you don't.

Ready to build? Check out our Serverless Overview and our Model Library, and get started today with just $5.