𝐋𝐋𝐌𝐬 𝐀𝐫𝐞 𝐈𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞. 𝐁𝐮𝐭 𝐀𝐫𝐞 𝐒𝐋𝐌𝐬 𝐌𝐨𝐫𝐞 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬?
In the past few weeks, I've been exploring a range of AI applications powered by language models and autonomous agents. One question keeps coming to mind:
"Are Large Language Models (LLMs) truly the future of enterprise transformation, or can Small Language Models (SLMs) meet most needs more effectively for specific, tailored use cases?"
Here is my analysis so far (still a work in progress):
LLMs like GPT-4 and Claude 3.5 Sonnet contain 175 billion to over 1 trillion parameters requiring massive distributed computing infrastructure. Small language models, typically 1-20 billion parameters like Llama 3.2, Gemini Nano, sacrifice some general knowledge breadth for dramatic gains in efficiency, speed and cost-effectiveness.
Here's the interesting part:
For many enterprise scenarios, smaller models aren't a compromise—they're a strategic advantage. A fine-tuned 7B parameter model focused on a specific domain can outperform GPT-4 on specialized tasks, all while costing 10x to 50x less to operate.
(Source: https://lnkd.in/erjTAnWu)
Premium LLM APIs can cost $75 per million input tokens for GPT 4.5, while SLMs like Llama can operate at around $0.03 for the same.
Adding to this, LLMs often require GPU clusters worth hundreds of thousands of dollars, whereas SLMs can run efficiently on local machines under $10,000.
Most importantly, latency often matters more than the capability.
SLMs deliver 6-10 tokens per second on modest hardware making them ideal for real-time applications. LLMs, despite their power, typically require 5-30 seconds for complex responses and introduce variable latency due to distributed processing. For applications where users expect immediate feedback, this performance gap becomes a competitive disadvantage regardless of the model's theoretical capabilities.
So unless, there is a strong need for strategic analysis or multi-step reasoning, is there a real need for LLMs in enterprise applications?
Follow the blog for detailed analysis in the next post.
"Are Large Language Models (LLMs) truly the future of enterprise transformation, or can Small Language Models (SLMs) meet most needs more effectively for specific, tailored use cases?"
Here is my analysis so far (still a work in progress):
LLMs like GPT-4 and Claude 3.5 Sonnet contain 175 billion to over 1 trillion parameters requiring massive distributed computing infrastructure. Small language models, typically 1-20 billion parameters like Llama 3.2, Gemini Nano, sacrifice some general knowledge breadth for dramatic gains in efficiency, speed and cost-effectiveness.
Here's the interesting part:
For many enterprise scenarios, smaller models aren't a compromise—they're a strategic advantage. A fine-tuned 7B parameter model focused on a specific domain can outperform GPT-4 on specialized tasks, all while costing 10x to 50x less to operate.
(Source: https://lnkd.in/erjTAnWu)
Premium LLM APIs can cost $75 per million input tokens for GPT 4.5, while SLMs like Llama can operate at around $0.03 for the same.
Adding to this, LLMs often require GPU clusters worth hundreds of thousands of dollars, whereas SLMs can run efficiently on local machines under $10,000.
Most importantly, latency often matters more than the capability.
SLMs deliver 6-10 tokens per second on modest hardware making them ideal for real-time applications. LLMs, despite their power, typically require 5-30 seconds for complex responses and introduce variable latency due to distributed processing. For applications where users expect immediate feedback, this performance gap becomes a competitive disadvantage regardless of the model's theoretical capabilities.
So unless, there is a strong need for strategic analysis or multi-step reasoning, is there a real need for LLMs in enterprise applications?
Follow the blog for detailed analysis in the next post.
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