Summarization

In a summarization use case, small language models (SLMs) and large language models (LLMs) differ significantly in their trade-offs between speed, efficiency, and quality. Below is an example comparison of their performance in summarizing documents like news articles or meeting transcripts.

Use Case: Summarizing News Articles

Scenario

A company processes large volumes of news articles daily and requires automated summarization to extract key points for quick decision-making. Both an SLM and an LLM are deployed to compare efficiency and performance in summarizing the same set of news articles.

Key Metrics for Comparison

Latency: Time taken to generate the summary.
Resource Utilization: Memory usage and compute power.
Summarization Accuracy: Measured by ROUGE score (commonly used for evaluating summarization quality).

Metric

Model Size
Latency (average)
Memory Usage (RAM)
Compute Power
Energy Consumption
ROUGE Score (F1)

Small Language Model (SLM)

60M parameters
0.1 seconds/article
700 MB
CPU
3 kWh/month
0.72

Large Language Model (LLM)

1.7B parameters
2 seconds/article
10 GB
GPU/High-end CPU
30 kWh/month
0.85

Technical Insights

Latency: The SLM provides summaries almost instantaneously (0.1 seconds per article), while the LLM takes significantly longer (2 seconds per article). For a batch process that handles thousands of articles, this translates into massive time savings with the SLM.
Memory and Compute Efficiency: The SLM consumes minimal memory (700 MB) and can run efficiently on a standard CPU. In contrast, the LLM requires 10 GB of RAM and typically runs on GPUs or high-performance CPUs, which increases hardware requirements and operating costs. The energy consumption of the LLM is also 10x higher than the SLM.
Summarization Quality: While the LLM achieves a higher ROUGE score (0.85 vs. 0.72), the SLM provides reasonably accurate summaries for straightforward content like news articles. For more nuanced or complex content (e.g., technical papers), the LLM’s advanced capabilities would shine, but in simpler summarization tasks, the SLM is a strong contender.

Business Insights

Cost Efficiency: The SLM offers a clear advantage in terms of cost. It runs on basic hardware and consumes much less energy. If your business processes a high volume of articles daily, using an SLM reduces infrastructure expenses and energy costs, offering a cost-per-summary reduction.
Faster Throughput: With a response time of 0.1 seconds per summary, the SLM allows for near-real-time summarization, improving the decision-making process. The LLM, while more accurate, takes longer and may introduce delays, especially when processing large volumes of content.
Fit for Purpose: While the LLM provides slightly more detailed and contextually rich summaries, the added benefit may not justify the cost for routine tasks like summarizing daily news articles or meeting notes. In scenarios where speed and volume are prioritized, the SLM can handle the workload effectively, giving you the best return on investment.

Benchmarking Example

Let’s assume the business needs to summarize 10,000 news articles daily.

SLM Processing Time: 0.1 seconds/article → 16 minutes total for all articles.
LLM Processing Time: 2 seconds/article → 5.5 hours total for all articles.

This shows that an SLM is 21 times faster than an LLM, making it highly scalable in time-sensitive applications.

Conclusion

For routine summarization tasks where high throughput and cost efficiency are critical, small language models (SLMs) offer a more balanced approach compared to large language models (LLMs). While LLMs may provide superior accuracy and more nuanced insights, SLMs are sufficiently accurate, far more efficient, and cost-effective, making them a better choice for summarizing large volumes of straightforward content in real time.