Beating ChatGPT-4o with a Local Model: Qwen 2.5 72B on a Single H200

Over the past week, I conducted a series of performance benchmarks to compare locally hosted large language models with cloud-based APIs in real-world automation tasks. What started as a simple experiment turned into an eye-opening result.

🧪 The Task

The benchmark involved a structured browser automation workflow: configuring a Volvo C40 Recharge on the Volvo Cars Australia website and completing an enquiry form, all driven by natural language instructions processed by the model.

⚔️ The Competitors

• Qwen 2.5 72B (Quantized) on a single NVIDIA H200
• ChatGPT-4o, accessed via OpenAI API
• Qwen 2.5 72B (Quantized) on a multi-GPU A6000 setup

⏱️ Results

💡 Why Did the H200 Win?

The NVIDIA H200 significantly outperformed the other setups due to several key factors:

• High-Bandwidth Memory (HBM3e): With 141 GB of on-chip memory, the entire 72B model fits without the need for model parallelism or cross-device communication, eliminating synchronization overhead.
• Exceptional Memory Bandwidth: Approximately 5 TB/s bandwidth ensures that transformer layers remain fully saturated with weight data, reducing latency.
• Hopper-Class Transformer Engine: Accelerates quantized FP8/INT8 computations to near-FP16 speeds, enhancing inference performance.
• Single-GPU Simplicity: Avoids complexities associated with multi-GPU setups, such as NCCL overhead and PCIe latency, resulting in more efficient execution.

Collectively, these advantages led to 2.4× faster token generation compared to the same model running across multiple A6000s, and even surpassed the performance of ChatGPT-4o in this structured task.

🧠 Technical Insights

• Quantization Techniques: Leveraging INT8 quantization reduced the model size and improved inference speed without significant loss in accuracy.
• Model Deployment: The model was deployed using the Ollama framework, facilitating efficient local inference.
• Automation Tool: The use of browser-use enabled seamless browser automation through natural language instructions, showcasing the potential of AI-driven web interactions.

This experiment reinforces a key point: with the right hardware, local LLM deployment is not only viable—but can outperform cloud models in specific tasks. If you're exploring on-prem AI, edge inference, or real-time systems, don't underestimate the power of the H200 + quantised LLMs.

Feel free to connect if you're working on similar infrastructure or model optimization challenges—I’d love to exchange ideas.

#AI #LLM #H200 #InferenceOptimization #EdgeAI #Qwen #ChatGPT4o #Ollama #Transformers #Benchmarking #NVIDIA #BrowserAutomation