Deploy Llama 3.3 70B with TensorRT‑LLM + INT8 Quantization on a $9/Month DigitalOcean GPU Droplet

Description and use

Deploying Llama 3.3 70B on a DigitalOcean GPU droplet with TensorRT‑LLM and INT8 quantization cuts monthly cost to <$10, doubles inference speed, and halves VRAM usage.

Problem

High cost of commercial LLM APIs and limited inference speed on generic GPU setups.

Outcome

The setup delivers 2× faster inference, halves VRAM usage, and reduces monthly cost from $1,200 to <$10 while maintaining near‑identical output quality.

Workflow

• Provision a DigitalOcean GPU Droplet (L40S, $9.99/month)
• SSH into the droplet and verify GPU with nvidia‑smi
• Install system packages, Python 3.10, CUDA 12.2, and set environment paths
• Create a Python virtual environment and install TensorRT‑LLM from source
• Clone the TensorRT‑LLM repo and install dependencies
• Download the Llama 3.3 70B‑Instruct model from Hugging Face or Meta
• Quantize the model to INT8 using a provided script
• Run inference with TensorRT‑LLM and observe 2× speedup and 50% VRAM reduction

Evidence

2x Faster Inference at 1/160th Claude Opus Cost

Source

Tokens/sec (batch=1): 45 (vLLM) vs 92 (TensorRT‑LLM)

Source

Cost/month: $1,200 (vLLM) vs $9 (TensorRT‑LLM)

Source