PyTorch in Production
When you are paying thousands of dollars a month for GPU instances, running unoptimized, eager-mode PyTorch code in production is financial negligence. Standard Hugging Face generate() calls are heavily CPU-bound and do not fully utilize the GPU's tensor cores.
Compiling with TensorRT-LLM
We compiled our fine-tuned Llama model using Nvidia's TensorRT-LLM framework.
TensorRT-LLM fuses transformer layers, optimizes attention kernels for specific GPU architectures (like Hopper or Ada Lovelace), and supports advanced In-Flight (Continuous) Batching.
# The compilation process converts PyTorch weights into a highly optimized TensorRT engine
python build.py --model_dir ./llama-3-8b-hf --dtype float16 --use_gpt_attention_plugin float16 --use_gemm_plugin float16 --output_dir ./trt_engines/The compilation process took hours and was incredibly frustrating to debug due to C++ dependency issues. However, the resulting engine delivered a massive 3x increase in tokens/sec compared to standard inference, allowing us to serve three times as many users on the exact same hardware footprint.