TRT LLM for Inference

Basic idea

NVIDIA TensorRT-LLM (TRT-LLM) is an open-source library for optimizing and accelerating large language model (LLM) inference on NVIDIA GPUs.

It provides highly efficient kernels, memory management, and parallelism strategies—like tensor, pipeline, and sequence parallelism—so developers can serve LLMs with lower latency and higher throughput.

TRT-LLM integrates with frameworks like Hugging Face and PyTorch, making it easier to deploy state-of-the-art models at scale.

What you'll accomplish

You'll set up TensorRT-LLM to optimize and deploy large language models on your DGX Spark, achieving significantly higher throughput and lower latency than standard PyTorch inference through kernel-level optimizations, efficient memory layouts, and advanced quantization.

What to know before starting

Python proficiency and experience with PyTorch or similar ML frameworks
Command-line comfort for running CLI tools and Docker containers
Basic understanding of GPU concepts including VRAM, batching, and quantization (FP16/INT8)
Familiarity with NVIDIA software stack (CUDA Toolkit, drivers)
Experience with inference servers and containerized environments

Prerequisites

DGX Spark device
NVIDIA drivers compatible with CUDA 12.x: nvidia-smi
Docker installed and GPU support configured: docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc13 nvidia-smi
Hugging Face account with token for model access: echo $HF_TOKEN
Sufficient GPU VRAM (40GB+ recommended for 70B models)
Internet connectivity for downloading models and container images
Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving

Ancillary files

All required assets can be found here on GitHub

trtllm-mn-entrypoint.sh — container entrypoint script for multi-node setup

Model Support Matrix

The following models are supported with TensorRT-LLM on Spark. All listed models are available and ready to use:

Model	Quantization	Support Status	HF Handle
Nemotron-3-Nano-Omni-30B-A3B-Reasoning	BF16	✅	`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`
Nemotron-3-Nano-Omni-30B-A3B-Reasoning	FP8	✅	`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8`
Nemotron-3-Nano-Omni-30B-A3B-Reasoning	NVFP4	✅	`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4`
Nemotron-3-Super-120B	NVFP4	✅	`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4`
GPT-OSS-20B	MXFP4	✅	`openai/gpt-oss-20b`
GPT-OSS-120B	MXFP4	✅	`openai/gpt-oss-120b`
Llama-3.1-8B-Instruct	FP8	✅	`nvidia/Llama-3.1-8B-Instruct-FP8`
Llama-3.1-8B-Instruct	NVFP4	✅	`nvidia/Llama-3.1-8B-Instruct-FP4`
Llama-3.3-70B-Instruct	NVFP4	✅	`nvidia/Llama-3.3-70B-Instruct-FP4`
Qwen3-8B	FP8	✅	`nvidia/Qwen3-8B-FP8`
Qwen3-8B	NVFP4	✅	`nvidia/Qwen3-8B-FP4`
Qwen3-14B	FP8	✅	`nvidia/Qwen3-14B-FP8`
Qwen3-14B	NVFP4	✅	`nvidia/Qwen3-14B-FP4`
Qwen3-32B	NVFP4	✅	`nvidia/Qwen3-32B-FP4`
Phi-4-multimodal-instruct	FP8	✅	`nvidia/Phi-4-multimodal-instruct-FP8`
Phi-4-multimodal-instruct	NVFP4	✅	`nvidia/Phi-4-multimodal-instruct-FP4`
Phi-4-reasoning-plus	FP8	✅	`nvidia/Phi-4-reasoning-plus-FP8`
Phi-4-reasoning-plus	NVFP4	✅	`nvidia/Phi-4-reasoning-plus-FP4`
Qwen3-30B-A3B	NVFP4	✅	`nvidia/Qwen3-30B-A3B-FP4`
Llama-4-Scout-17B-16E-Instruct	NVFP4	✅	`nvidia/Llama-4-Scout-17B-16E-Instruct-FP4`
Qwen3-235B-A22B (two Sparks only)	NVFP4	✅	`nvidia/Qwen3-235B-A22B-FP4`

NOTE

You can use the NVFP4 Quantization documentation to generate your own NVFP4-quantized checkpoints for your favorite models. This enables you to take advantage of the performance and memory benefits of NVFP4 quantization even for models not already published by NVIDIA.

Reminder: not all model architectures are supported for NVFP4 quantization.

Time & risk

Duration: 45-60 minutes for setup and API server deployment
Risk level: Medium - container pulls and model downloads may fail due to network issues
Rollback: Stop inference servers and remove downloaded models to free resources.
Last Updated: 04/28/2026
- Docker image 1.3.0rc13; Nemotron Omni reasoning BF16, FP8, NVFP4 in matrix