The Future of Gen AI Models: Speed vs. Depth
Ed Lotoczky, PMP, PSM
Senior Manager, Technical Project Management at The Walt Disney Company
?? Today, let's talk about LLM inference. OpenAI's launch of their o1 preview model last week has sparked widespread interest in concept of inference, and I’m excited to dive into its key implications.
What is LLM inference?
It's the process where a large language model takes your input prompt and applies the pre-training in the model to generate output. This can be considered the “thinking” or “reasoning” phase. The process is often measured in time (seconds) as one benchmark metric. Throughput and accuracy are other benchmark metrics.
The Current State and OpenAI’s o1 Preview Model
Typically, current frontier LLMs like Claude 3.5 Sonnet (my current favorite), GPT 4o, Gemini 1.5 Pro, Llama 3.1 , Mistral Large 2, etc. aim for quick inference times to provide output really quickly. Historically, the speed in which these models generate output has been part of the mystique around these tools and quickly impresses people.
But OpenAI just flipped the script with their o1 preview model. Here's why it’s in the headlines:
?? Current frontier LLMs are known for:
?? OpenAI o1 preview sets itself apart with:
Pros and Cons
Pros of o1's approach:
? Enhanced problem-solving abilities
? Improved performance on math, coding, and scientific tasks
? More thoughtful and accurate responses in some cases
Cons to consider:
? Slower response times
? Significantly higher token usage and computational costs
? May not be suitable for all use cases - such as applications where fast response times are needed
Implications and Questions
This shift raises interesting questions for Gen AI applications. When is it worth trading speed for deeper reasoning? How will this impact user experience and infrastructure needs? Is this the future of AI, or just a niche approach?
My Take
Overall, I’m a fan of the concept of models increasing inference time if that means higher quality output and less hallucinations. Most of my use cases don’t require near real-time output. Additionally, as models continue to improve quality of output, becoming more persuasive and convincing, the importance of being the human in the loop (a concept I’m a HUGE proponent of) becomes more critical.
Based on my early personal testing at home, this model is more evolutionary than revolutionary. Quality of output on initial non-Chain of Thought prompts are generally better, but I’m not sure by enough to get to me switch from Claude and Perplexity. Using o1 preview feels more like a system prompt wrapper has been applied to the existing GPT 4o model to automatically force the use of Chain of Thought prompting in the input prompt, which forces a longer step-by-step reasoning process by default. As I write this, I’m reminded of the Reflection 70B model released a couple of weeks ago which basically added a wrapper around the Llama 3.1 70B model (or potentially Claude - the controversy continues) to force the model to double check answers before providing output.
A Look to the Future
I also find it interesting that OpenAI recommends AGAINST using Chain of Thought prompts with the o1 preview model. This recommendation highlights the future importance that models automatically determine the best type of input prompt for each model, and potentially manipulate the user input before beginning the inference process for optimal results.
Let’s Discuss!
What are your thoughts on this trade-off between quick responses and more in-depth processing? Let's discuss in the comments! ??
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