Deploy production ready Llama-4 on EC2 instances

Hi Everyone,

Meta just released the Llama 4 herd which represents their newest generation of large language models, featuring Scout and Maverick variants. These models comes with Massive context window of upto 10 million tokens for Scout and introduce architecture innovations like Mixture of Experts (MoE) and Interleaved RoPE (iRoPE). This makes Llama-4 extremely performant for enabling multi-document summarisation and reasoning over vast codebases.

So, we have done the heavy lifting for you to run each variant on the cheapest and highest-availability GPUs. All these configurations have been tested with vLLM for high throughput and auto-scale with the Tensorfuse serverless runtime.

Llama 4 models offer impressive context length capabilities across different hardware configurations:

If you are looking to use Llama-4 models in your production application, follow our detailed guide to deploy it on your AWS account using Tensorfuse.

The guide covers all the steps necessary to deploy open-source models in production:

1. Deployed with the vLLM inference engine for high throughput

2. Support for autoscaling based on traffic

3. Prevent unauthorized access with token-based authentication

4. Configure a TLS endpoint with a custom domain

At Tensorfuse we're building the best serverless gpu runtime that lets you run AI inference on your own AWS GPUs. Join our community for more info.