TensorRT-LLM + Ertas

Overview

TensorRT-LLM is NVIDIA's high-performance inference library purpose-built for deploying large language models on NVIDIA GPUs. It applies advanced optimizations including kernel fusion, quantization-aware compilation, in-flight batching, and paged KV-cache management to squeeze maximum performance from GPU hardware. Models compiled with TensorRT-LLM routinely achieve 2-5x higher throughput and significantly lower latency compared to standard PyTorch inference, making it the go-to runtime for production LLM deployments that need to serve many concurrent users.

TensorRT-LLM supports the full spectrum of NVIDIA hardware from consumer RTX cards to data center H100 and B200 GPUs, with optimizations tailored to each architecture. It handles multi-GPU and multi-node tensor parallelism for models that exceed single-GPU memory, and integrates with NVIDIA's Triton Inference Server for production serving with load balancing, model versioning, and health monitoring. For organizations running fine-tuned models in production — whether for customer-facing applications, internal tools, or API services — TensorRT-LLM represents the highest-performance deployment path on NVIDIA hardware.

How Ertas Integrates

Ertas Studio handles the model customization phase — curating training data, running fine-tuning jobs, and exporting trained models — while TensorRT-LLM handles the production deployment phase, optimizing those models for maximum GPU performance. After fine-tuning a model in Ertas, you export it in a format compatible with TensorRT-LLM's build pipeline, which compiles the model into an optimized engine tailored to your specific GPU hardware and serving requirements.

This separation of concerns lets your team focus on model quality in Ertas without worrying about deployment optimization, and focus on serving performance in TensorRT-LLM without worrying about training infrastructure. The workflow supports rapid iteration: fine-tune a new version in Ertas, rebuild the TensorRT engine, and swap it into production with minimal downtime. For teams serving fine-tuned models to many users — customer support bots, coding assistants, document processing pipelines — the combination delivers both the domain specificity of fine-tuning and the raw performance needed for production scale.

Getting Started

1. 1

   Fine-tune your model in Ertas Studio

   Prepare your domain-specific dataset, select a base model, and run fine-tuning in Ertas Studio. Use experiment tracking to identify the best checkpoint based on your evaluation metrics.

2. 2

   Export the model in a compatible format

   Export the fine-tuned model from Ertas in Hugging Face safetensors or PyTorch format. Ensure the model architecture is supported by TensorRT-LLM's converter scripts for your chosen base model family.

3. 3

   Build the TensorRT-LLM engine

   Use TensorRT-LLM's build API to compile the model into an optimized engine for your target GPU. Configure quantization level (FP16, INT8, FP8), tensor parallelism for multi-GPU setups, and maximum batch size based on your serving requirements.

4. 4

   Deploy with Triton Inference Server

   Load the compiled engine into NVIDIA Triton Inference Server for production serving. Configure model versioning, dynamic batching, health checks, and an OpenAI-compatible API endpoint for client applications.

5. 5

   Monitor and iterate on model versions

   Track inference latency, throughput, and output quality in production. When you fine-tune improved versions in Ertas, rebuild the TensorRT engine and deploy with zero-downtime model swaps through Triton's version management.

Benefits

• 2-5x inference throughput improvement over standard PyTorch serving on the same hardware
• Sub-100ms latency for interactive applications like chat, code completion, and search
• Multi-GPU tensor parallelism for serving large fine-tuned models across GPU clusters
• Production-ready deployment with Triton's load balancing, health monitoring, and versioning
• Hardware-specific optimizations for every NVIDIA architecture from RTX to H100
• Rapid model iteration — rebuild and swap TensorRT engines when new fine-tuned versions are ready