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

Описание и применение

Развертывание модели Llama 3.3 70B на GPU‑дроплете DigitalOcean с использованием TensorRT‑LLM и INT8‑квантования снижает стоимость до <$10 в месяц, удваивает скорость инференса и уменьшает потребление VRAM вдвое.

Задача

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

Результат

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

Рабочий процесс

• 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

Доказательства

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

Источник

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

Источник

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

Источник