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

说明与用途

在 DigitalOcean GPU Droplet 上使用 TensorRT‑LLM 和 INT8 量化部署 Llama 3.3 70B，将月度成本降至 <$10，推理速度翻倍，VRAM 使用量减半。

问题

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

结果

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

工作流

• 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

证据

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

来源

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

来源

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

来源