DGX Spark Local Large Model Deployment Guide: Comparison of Three Solutions—TensorRT-LLM, vLLM, and NIM

The release of NVIDIA DGX Spark marks the arrival of the personal AI supercomputer era, making it possible to run large language model inference locally. This article will deeply compare three mainstream deployment solutions—TensorRT-LLM, vLLM, and NVIDIA NIM—helping readers choose the most suitable local deployment solution based on their own needs (such as performance, ease of use, enterprise support, etc.).

The core of DGX Spark (and OEM models like Lenovo ThinkStation PGX) is the NVIDIA GB10 Grace Blackwell chip, which integrates:

• Grace CPU (high-efficiency core with ARM architecture)
• Blackwell GPU (a new generation of AI acceleration unit supporting FP4 low-precision computing)
• Unified memory architecture (CPU and GPU share memory, reducing data transfer overhead) This architecture is particularly suitable for large language model inference—model parameters can reside in unified memory, and activation value calculations are efficiently completed on the GPU.

TensorRT-LLM is a high-performance inference optimization library launched by NVIDIA, designed specifically for production environments:

Core Technical Features

• Operator fusion: Merges multiple computing operations into a single CUDA kernel, reducing memory access overhead
• Quantization support: Low-precision formats like FP4 and INT8, balancing model quality and memory usage
• Paged attention: Optimizes KV cache management, supporting longer context windows
• Multi-model concurrency: Runs multiple models on the same port, dynamically allocating resources

Applicable Models

Qwen3-FP4, Nemotron-NVFP4

Deployment Examples

Single model (Qwen3-FP4): cd backends/trtllm && docker compose --profile qwen up Multi-model concurrency (Qwen3-FP4 + Nemotron-NVFP4): cd backends/trtllm && docker compose --profile multi up

vLLM is an open-source high-throughput inference engine known for its concise design and active community:

Core Technical Features

• PagedAttention: KV cache paging management, dynamic memory allocation to improve throughput
• Continuous batching: Merges decoding steps of different requests to increase GPU utilization
• Tool call support: Natively supports function calls, facilitating the building of Agent applications
• Good model compatibility: Supports most models in the HuggingFace ecosystem

Applicable Models

Qwen3-Coder, Nemotron, Nemotron-VL

Tool Call Advantages

Natively supports tool calls, allowing easy construction of AI Agents that interact with external APIs and databases (e.g., weather query, database query tools).

NVIDIA NIM provides a plug-and-play model deployment experience:

Core Technical Features

• Pre-optimized images: Models are optimized by NVIDIA, ready to use out of the box
• Standardized API: Unified OpenAI-compatible interface, facilitating application migration
• Security updates: Automatically get security patches and performance optimizations
• Enterprise support: Official technical support

Applicable Models

Qwen3-32B, Llama-3.1-8B, Nemotron-Nano

Deployment Process

cd backends/nim && docker compose up (automatically pulls optimized images from NGC, no need to manually download and convert weights).

Each of the three solutions has its own focus:

• TensorRT-LLM: Suitable for production environments pursuing extreme performance (leading performance, requires a certain level of configuration complexity)
• vLLM: Suitable for development scenarios requiring flexibility and tool call capabilities (wide model support, complete native tool calls)
• NVIDIA NIM: Suitable for users who need quick deployment and enterprise support (simplest deployment, official support) Performance optimization: TensorRT-LLM > vLLM > NIM; Deployment complexity: NIM < TensorRT-LLM ≈ vLLM; Model flexibility: vLLM > others; Tool calls: vLLM is optimal; Enterprise support: TensorRT-LLM and NIM provide official support.

Network Access Control

By default, it binds to the local address (127.0.0.1:8000). To enable LAN access, you need to modify the port binding; when opening to the LAN, ensure the router blocks external access and only allows connections from trusted devices.

Supply Chain Security

When using vLLM or TensorRT-LLM to run Nemotron models, you need to enable the --trust-remote-code option, which carries the risk of supply chain attacks; it is recommended to check the cache directory code during the first download to ensure the source is trustworthy.