How to Balance Performance and Cost with LLMs

Eden AI
4 min readAug 30, 2024
Photo by Elena Mozhvilo on Unsplash

Imagine your business is at a crossroads. You’ve tapped into the power of Large Language Models (LLMs), and the potential they hold is clear — efficiency, innovation, and the ability to do things that once seemed impossible. But there’s a catch. As you start to see the results, you notice something else creeping in: the costs. They’re rising faster than you expected, and suddenly, what felt like a smooth path to progress starts to feel like a balancing act between performance and budget.

At Eden AI, we’ve seen this scenario play out many times. Businesses eager to leverage the transformative capabilities of LLMs find themselves navigating a complex landscape where the promise of cutting-edge technology must be balanced against the reality of its cost. It’s a delicate dance — one that requires strategic planning, careful evaluation, and, most importantly, the right tools and expertise.

Navigating the Maze of LLM Performance

When it comes to evaluating LLMs, the road isn’t always clear. Imagine trying to judge a race car’s performance with three different sets of criteria, none of which tell the full story.

  1. First, there’s the “eyeballing” method. It’s like watching the race from the stands, relying on your instincts and what you can see to decide who’s winning. Quick and straightforward, but prone to error and hard to scale.
  2. Next, there’s HELM (Holistic Evaluation of Language Models). It is comprehensive, but not always reflective of the real-world scenarios your business might face. Plus, it’s time-consuming and resource-intensive, much like taking your car in for a full diagnostic workup every time you hit the road.
  3. Lastly, there’s the LLM-as-a-Judge approach, where you let one AI evaluate another. Think of it as having a seasoned race car driver critique your laps. It’s insightful, but it can be tricky to replicate and requires fine-tuning — like adjusting the car’s settings to perfection.

These methods offer valuable insights, but none provide a complete, ongoing picture of performance across all tasks. It’s like trying to maintain peak performance in a race without a pit crew to monitor and adjust the car’s performance in real-time.

The Balancing Act: Performance vs. Cost

So, how do you keep your LLMs performing at their best without burning through your budget? At Eden AI, we’ve developed strategies that help businesses like yours strike the right balance.

  • Optimise Hardware: Think of this as upgrading your car’s engine. Faster GPUs are like turbochargers, helping your LLMs process information quicker and more efficiently. It might seem like a big upfront cost, but the time saved and the boost in performance can pay off in the long run.
  • Choose the Right Model Size: Bigger models can be like high-powered sports cars — they’re impressive, but do you really need all that horsepower? Sometimes, a more modest model can get the job done just as well, without the hefty fuel bills. Consider whether you truly need the latest, most powerful model, or if a leaner version could be just as effective.
  • Consider Quantization: Imagine lightening your car to improve speed and fuel efficiency. Quantization reduces the precision of your LLMs, making them smaller and cheaper to run, without a significant drop in performance. It’s a smart way to cut costs without compromising too much on quality.
  • Fine-Tune for Specific Tasks: Just like tuning your car for a specific type of race, fine-tuning your LLMs for specific tasks can yield better performance where it matters most. It’s an investment that can lead to more efficient use of resources and cost savings over time.
  • Craft Better Prompts: Clear, concise prompts are like giving precise instructions to your race car’s crew. The better your prompts, the less room there is for error, leading to smoother, more accurate outcomes. But be cautious — more complex prompts can be like demanding too much from your engine, potentially leading to increased costs.
  • Adopt an Analytical Approach: Finally, taking an analytical approach is like having a top-tier team of engineers constantly monitoring your car’s performance. Tools like LLMstudio allow you to test different scenarios, track costs, and optimise your setup. This data-driven approach ensures you’re always making informed decisions, balancing performance with cost.

Balancing performance and cost isn’t just about saving money — it’s about ensuring your AI initiatives are sustainable and scalable. By implementing these strategies and leveraging the expertise at Eden AI, you can harness the full potential of LLMs without stretching your budget too thin. It’s about finding that sweet spot where innovation meets cost-effectiveness, ensuring that your AI investments drive real, lasting value for your business.

Ready to make your AI strategy both powerful and sustainable? Reach out to our team at specialists@edenai.co.za or visit us at https://edenai.co.za. Let’s work together to ensure your AI initiatives deliver the best possible returns without breaking the bank.

This post was enhanced using information from:

WhyLabs Team (2024) 7 Ways To Evaluate and Monitor LLMs
https://whylabs.ai/blog/posts/7-ways-to-evaluate-and-monitor-llms

Lanza, E. (2023) Empower Applications with Optimized LLMs: Performance, Cost, and Beyond Intel Tech
https://medium.com/intel-tech/empower-applications-with-optimized-llms-performance-cost-and-beyond-59c6e79cceb4n

Benram, G. (2018) Understanding the cost of Large Language Models (LLMs) TensorOps
https://www.tensorops.ai/post/understanding-the-cost-of-large-language-models-llms

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Eden AI
Eden AI

Written by Eden AI

Accelerating AI adoption for organizations. Data Science | Analytics | Computer Vision | MLOps | AI Advisory Practical optimism about AI application

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