Small Models, Big Impact

 

Copyright: Sanjay Basu

   Why Size Isn’t Everything in AI

Small models matter — a lot. It’s easy to get dazzled by trillion-parameter giants that promise general intelligence, but I strongly believe the smaller 7-billion-parameter models like MPT-7B, the Llama family of 7B, Falcon 7B, and Mistral 7B are the real unsung heroes, especially when you’re dealing with multi-agent workflows.

Why am I advocating for smaller models? Well, let’s talk practicality. On a single NVIDIA A100 40GB GPU — the workhorse we have at our fingertips — you can comfortably run inference for open-source models like Llama 2 7B, Mistral 7B, Phi-2, Falcon 7B, MPT 7B, and even some smaller instruction-tuned variants like FLAN-T5 (up to 3B parameters). Closed-source gems such as Claude 3 Haiku variants and early GPT models at 7B also fit neatly. With a bit of clever optimization (quantization at 4-bit or 8-bit), you can even squeeze in certain 13B models.

Now, let’s consider fine-tuning. Full fine-tuning these models suddenly balloons memory demands by 2–4x compared to inference, quickly exceeding a single GPU’s capacity — even the robust A100 40GB. However, parameter-efficient techniques like PEFT/LoRA let you fine-tune a 7B model on that single GPU. That’s a big deal for agility and cost-efficiency.

Here’s where things get interesting with multi-agent systems. Smaller models aren’t just cheaper — they’re specialists. Imagine agents in a workflow, each perfectly tuned for a specific task — data extraction, summarization, sentiment analysis, or basic reasoning. These compact models excel precisely because they’re not overgeneralized. They’re precise, performant, and incredibly efficient. This approach turns your workflows into orchestras, each agent playing its perfect note, all orchestrated smoothly on a relatively modest hardware footprint.

But let’s not get overly dogmatic. The future isn’t binary. We’ll still see massive trillion-plus parameter models emerging. These behemoths are built for broad intelligence and deep contextual understanding. Think of them as your chief strategists — expensive and powerful but justified by their generalized utility.

Yet, for day-to-day tasks, especially at scale or in specialized domains, smaller models are the smarter play. Consider retrieval-augmented generation (RAG) — for inference, your A100 40GB can handle the model part. But handling large data sets, say 5TB of text, shifts the challenge from GPU constraints to managing embeddings and vector databases. This scenario demands multiple GPUs connected over an RDMA network, significant CPU RAM, and fast SSD storage or a high-performance file system. Even then, smaller, targeted models reduce complexity, optimizing both cost and performance.

The takeaway? It’s not about choosing between small or giant models — it’s about using the right tool for the right job. Smaller models matter immensely because they empower precise, efficient, and cost-effective AI agents. And let’s be honest, who doesn’t appreciate efficiency without sacrificing performance?

Appendix 1: Benchmarking on NVIDIA A100 40GB (in OCI)

Copyright: Sanjay Basu

(Note: These results were from 6–8 months ago. I will run a complete benchmark against most of the popular 7B Open-Source LLMs, after the NVIDIA GTC 2025 event (March 17th to 21st). Specific power consumption metrics were unavailable, and benchmarks may vary based on workload conditions.) Please contact me for any specific benchmarks.

Appendix 2: Industry-specific Use Cases

• Healthcare: Smaller models efficiently handle tasks like summarizing patient notes, extracting critical medical information, and preliminary diagnostics, enabling rapid decision-making without compromising privacy and computational resources.
• Finance: Compact models excel at real-time fraud detection, transaction categorization, and risk assessment, ensuring high-throughput, low-latency processing crucial for financial services.
• Automotive: Embedded small models enable efficient in-vehicle systems for driver assistance, real-time diagnostics, and predictive maintenance without overwhelming onboard hardware.
• Retail and E-commerce: These models swiftly personalize user experiences, handle chatbot interactions, and optimize inventory management, all vital for responsive customer service at scale.
• Life Sciences: Smaller models enhance laboratory automation and data preprocessing, accelerating research tasks such as genomic sequence annotation and drug discovery workflows.

By tailoring smaller models to specific industry demands, you maximize value, efficiency, and responsiveness, demonstrating clearly why they’re essential assets in an AI-powered world.