Building High-Performance LLM Applications on AMD GPUs with Lamini

Lamini

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have become the cornerstone of numerous applications. As these models grow in size and complexity, software engineers face the challenge of efficiently building and deploying LLM applications that can handle the demanding computational requirements. This is where the combination of AMD GPUs and Lamini comes into play, offering a powerful solution for creating high-performance LLM applications.

Why AMD GPUs for LLM Applications?

AMD's latest GPUs, particularly the MI300X series, have emerged as formidable contenders in the AI and machine learning space. AMD GPUs offer:

  • High memory bandwidth
  • Large memory capacity
  • Excellent floating-point performance
  • Energy efficiency
These characteristics make AMD GPUs particularly well-suited for the memory-intensive and computationally demanding tasks involved in running and training large language models.

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Introducing Lamini: The Key to Unlocking AMD GPU Potential

While AMD GPUs provide the hardware foundation, Lamini serves as the software catalyst that allows developers to fully harness this power for LLM applications. Lamini stands out as the premier solution for running high-performance multi-node LLM inference and training servers on AMD GPUs.

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Real-World Impact: Example with a Financial Services Q&A System

To illustrate the transformative power of Lamini on AMD GPUs, let's consider a concrete example in the financial services sector: a Q&A system for analyzing earnings reports.

Scenario: Earnings Report Analysis for a Major Investment Bank

Imagine a large investment bank that needs to quickly analyze earnings reports for hundreds of companies each quarter. They want to build an LLM-powered Q&A system that can handle complex queries about financial data, provide accurate insights, and serve multiple analysts simultaneously.

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Before Lamini:

Infrastructure: 5 servers with AMD MI300X GPUs

Model: A 70B parameter model

Performance:

  • Inference: 0.2 queries/second with 5s average response time
  • Accuracy: 85% for complex financial queries
  • Concurrent Users: 1 max!

Challenges:

  • Slow response times during peak earnings seasons
  • Limited accuracy on nuanced financial questions
  • Inability to handle the volume of requests during critical periods
After Implementing Lamini:

Supercharged Inference with Function Calling

Utilizing Lamini's 52x higher RPM and optimized function-calling inference server:

  • Queries handled: 104 queries/second (520x improvement from 52x throughput and 10x model size)
  • Average response time: reduced to 0.5s (due to higher accuracy of smaller mode

Impact:

  • Analysts can process 520x more earnings reports in the same timeframe
  • Real-time insights during earnings calls become possible

Enhanced Accuracy with Lamini Memory Tuning

Leveraging Lamini's advanced yet easy-to-use fine-tuning method:

  • Model accuracy improves from 50% to 97% on complex financial queries
  • Smaller 7B model now outperforms larger 70B models on domain-specific tasks

Impact:

  • More reliable insights lead to better investment decisions
  • Reduced need for human verification, freeing up analyst time

Scalability for Peak Demands (dedicated compute)

Concurrent users increase to 520 (520x improvement from 52x throughput and 10x model size)

System easily handles traffic spikes during earnings seasons

Impact:

  • All analysts can access the system simultaneously during critical periods
  • No need for expensive cloud bursting during peak times

Precision Through Function Calling

100% accuracy in schema/format for integrations with:

  • Financial databases for real-time data verification
  • Compliance systems for automatic regulatory checks
  • Visualization tools for instant chart generation

Impact:

  • 40% reduction in time spent on data validation and compliance checks (huge amount of developer months)
  • Instant generation of compliant, data-rich reports

For financial services companies, these improvements translate directly to faster insights, better investment decisions, and a significant competitive advantage in a market where speed and accuracy are paramount.

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Unparalleled Performance: Benchmark Result

Lamini's superiority is backed by impressive benchmark results that showcase its exceptional performance in both fine-tuning and inference tasks.

Fine-tuning & Training Efficiency

Model Flops Utilization (MFU): Lamini achieves an outstanding 48.9% MFU during fine-tuning.

  • This high MFU demonstrates Lamini's ability to extract maximum performance from AMD GPUs.

Scaling Efficiency: Lamini maintains an impressive 85% scaling efficiency.

  • This means that as you add more computational resources, you get nearly linear performance improvements.
Inference Throughput (dedicated compute)

Requests Per Minute (RPM): Lamini achieves 52x higher RPM compared to vLLM.

  • This staggering improvement means you can serve 52 times more requests in the same timeframe.
  • Interested in high throughout? Contact us!

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Conclusion

Building high-performance LLM applications on AMD GPUs is no longer a distant goal—it's a present reality with Lamini. By leveraging AMD's powerful GPU hardware and Lamini's optimized software stack, developers can create LLM applications that are not just marginally better, but transformatively superior.

The benchmark results speak for themselves: with a 48.9% MFU and 85% scaling efficiency for fine-tuning, coupled with a 52x improvement in inference RPM, Lamini is redefining what's possible in LLM application development on AMD GPUs.

For software engineers and enterprises looking to stay at the cutting edge of AI technology, this combination translates to: Faster development cycles with rapid model iterations and deployments.