Alibaba's newly released Tongyi Qwen3 models mark a significant leap in the development of open-source large language models (LLMs). Optimized for both dense and Mixture-of-Experts (MoE) architectures, the Qwen3 family includes eight models, spanning from the lightweight 0.6B dense model to the highly capable 235B-A22B MoE model, featuring 235 billion total parameters with 22 billion active during inference. With rapid token generation speeds and state-of-the-art performance, Qwen3 models are primed for deployment in production environments—especially when paired with the compute power of NVIDIA GPUs.
In this blog post, we’ll walk through best practices for integrating and deploying Qwen3 models into real-world applications using NVIDIA’s AI inference stack, including TensorRT-LLM, Ollama, SGLang, and vLLM. Whether your use case demands low latency, high throughput, or optimized GPU memory usage, there’s a deployment path to meet your requirements.
Meet the Tongyi Qwen3 Family
The Qwen3 family includes:
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Dense models: 0.6B, 1.7B, 4B, 8B, 14B, and 32B
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MoE models: 30B-A3B (30B total / 3B active), 235B-A22B (235B total / 22B active)
Dense models are ideal for applications requiring consistent performance with predictable compute needs, while MoE models offer massive scalability with reduced inference cost by activating only a subset of parameters.
Choosing the Right Framework for Deployment
Each deployment framework has strengths suited to different production requirements. Here's a breakdown of how to choose and use them effectively with Qwen3:
1. TensorRT-LLM: High-Performance Inference at Scale
Best for: Maximum throughput, latency-sensitive applications, tight GPU optimization
Highlights:
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NVIDIA’s TensorRT-LLM is a high-performance inference library tailored for large models.
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It supports custom FP8, INT8, and other mixed-precision optimizations.
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Ideal for deploying the larger Qwen3 models (14B, 32B, 235B-A22B) with high efficiency.
How to use:
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Convert Qwen3 weights to ONNX or the supported format.
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Use the provided TensorRT-LLM examples to build an optimized engine.
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Run inference with built-in batching and streaming support.
2. vLLM: Efficient Throughput with PagedAttention
Best for: Multi-user serving, batch inference, memory optimization
Highlights:
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vLLM’s PagedAttention reduces memory fragmentation and improves parallelism.
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Scales well across multiple GPUs.
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Supports model parallelism and dynamic batching.
How to use:
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Load Qwen3 models directly from Hugging Face or local weights.
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Serve via vLLM's OpenAI-compatible API for seamless integration.
3. SGLang: Flexible for Custom Applications
Best for: Developers building applications with complex control flows or task chaining
Highlights:
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SGLang supports function calling, routing, and task decomposition out of the box.
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Excellent for deploying smaller Qwen3 models (4B, 8B) in application-level logic.
How to use:
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Define model routes, prompts, and evaluation flows using SGLang DSL.
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Integrate Qwen3 checkpoints with standard Hugging Face loading.
4. Ollama: Local and Lightweight Inference
Best for: Rapid prototyping, developer desktops, edge devices
Highlights:
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Ollama provides a fast, containerized environment for running LLMs locally.
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Optimized for models like Qwen3-1.7B or Qwen3-4B for on-device inference.
How to use:
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Use the Ollama CLI to pull and run a Qwen3 model.
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Interact with the model using REST APIs or in a local REPL session.
Deployment Best Practices
To make the most of Qwen3’s capabilities, consider the following strategies:
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Match Model Size to Use Case:
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Use Qwen3-0.6B to 4B for chatbots, form filling, or basic summarization.
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Deploy Qwen3-14B or 32B for content generation, code completion, or advanced reasoning.
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Choose MoE models (30B-A3B, 235B-A22B) when ultra-large model quality is needed but compute efficiency matters.
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Quantize for Speed and Efficiency:
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Use INT4 or INT8 quantization (via TensorRT-LLM or vLLM) to reduce latency and memory usage.
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Leverage Model Parallelism:
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Distribute model layers across multiple GPUs for large models using vLLM or TensorRT-LLM.
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Use Prompt Caching and Streaming:
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Stream outputs for chat-like apps to improve UX.
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Cache common prompt outputs when possible.
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Conclusion
Tongyi Qwen3 models represent a robust and flexible suite of LLMs for production AI. Whether you're building customer service agents, code assistants, or knowledge retrieval systems, these models—when paired with NVIDIA’s powerful inference stack—can meet diverse performance and deployment needs.
With frameworks like TensorRT-LLM, vLLM, SGLang, and Ollama, developers can easily integrate Qwen3 into real-world applications with confidence. As the LLM landscape continues to evolve, Qwen3 offers an open, scalable alternative ready for enterprise deployment.