Boosting Language Model Performance: Cutting Processing Time from 3 Hours on CPU to Just 12 Minutes with Modal.com and NVIDIA H100 GPUs

Hey LinkedIn community!

I wanted to share an exciting project I recently worked on that significantly improved the performance of a language model we’re using. By leveraging Modal.com’s robust infrastructure and the powerhouse NVIDIA H100 GPUs, we managed to slash our processing time from 3-4 hours on a CPU down to an incredible 12 minutes on H100 GPUs. Let me walk you through how we did it and what’s next on the optimization journey.

The Starting Point: A Time-Consuming Process

Initially, running our language model on a standard CPU took about 3-4 hours to process our tasks. Even when we switched to NVIDIA’s Tesla T4 GPUs, we saw a reduction to 1 hour. While that was an improvement, we knew there was room for more efficiency to keep up with our growing demands and the fast-paced nature of AI development.

Finding the Solution: Modal.com Meets NVIDIA H100 GPUs

To tackle this challenge, we turned to Modal.com for a few key reasons. Modal.com offers containerized images for ready-to-use virtual machines (VMs) with GPU support, billed on an hourly basis. This flexibility allowed us to quickly deploy and scale our infrastructure without the overhead of managing hardware or long-term commitments.

Pairing Modal.com’s containerized, GPU-ready VMs with NVIDIA’s H100 GPUs—which are renowned for their top-tier performance—we unlocked significant computational power. This combination was instrumental in achieving our performance milestones.

Here’s What We Did:

1. Integrated llama.cpp:
2. Optimized GPU Layers:
3. Harnessed CUDA Toolkit:
4. Upgraded Hardware:

Why Modal.com?

Choosing Modal.com was a strategic decision based on several factors:

• Containerized Images: Modal.com provides containerized images that allow us to deploy our applications quickly and consistently across different environments. This streamlined our setup process and reduced deployment time.
• Ready-to-Use VMs with GPU Support: Modal.com’s VMs come pre-configured with GPU support, eliminating the need for extensive hardware setup and maintenance. This ready-to-use approach enabled us to focus more on optimizing our models rather than managing infrastructure.
• Flexible Hourly Pricing: The ability to pay for GPU resources on an hourly basis offered us the flexibility to scale our operations up or down based on demand. This cost-effective model ensured we only paid for what we used, making our project more economically viable.

The Results: A Game-Changing Improvement

Achieving a 20x speedup over CPU processing and a 5x improvement over Tesla T4 GPUs was beyond our initial expectations. This massive reduction in processing time not only enhances our operational efficiency but also opens up exciting possibilities for real-time data processing and quicker deployment of AI-driven solutions.

Looking Ahead: More Ways to Optimize

While we’re thrilled with these results, there’s always room for improvement. Here are a few areas we’re exploring next:

1. Quantization Techniques:
2. Pipeline Parallelism:
3. Memory Management Enhancements:
4. Advanced CUDA Optimizations:
5. Scalable Infrastructure:
6. Model Pruning:
7. Distributed Computing:

What’s Next: The Future of Efficient AI Processing

This journey has been a fantastic example of how combining cutting-edge hardware with smart software optimizations can lead to remarkable performance gains. Moving forward, I’m excited to keep pushing the boundaries of what’s possible with AI. There’s so much potential to unlock, and I’m eager to continue exploring these optimizations and sharing more breakthroughs with you all.

Wrapping Up

Optimizing language model performance isn’t just about making things faster—it’s about enabling quicker insights, more responsive applications, and fostering greater innovation. My recent experience with Modal.com and NVIDIA’s Tesla T4 and H100 GPUs has truly highlighted the transformative power of strategic technology integration. I’m looking forward to continuing this journey and sharing more exciting developments in the world of AI and machine learning.

Comparing GPU Performance: Tesla T4 vs. RTX 4090 vs. NVIDIA H100