Strategies for Small Models to Compete With Large AI Models

I recently delved into some intriguing research about the often-overlooked potential of Small Language Models (SLMs). While LLMs usually grab the headlines with their impressive capabilities, studies on SLMs fascinate me because they challenge the “bigger is better” mindset. They highlight scenarios where smaller, specialized models not only hold their own but actually outperform their larger counterparts. Here are some key insights from the research: 𝟏. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞, 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: SLMs excel in situations where data privacy and low latency are critical. Imagine mobile apps that need to process personal data locally or customer support bots requiring instant, accurate responses. SLMs can deliver high-quality results without sending sensitive information to the cloud, thus enhancing data security and reducing response times. 𝟐. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝, 𝐃𝐨𝐦𝐚𝐢𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐓𝐚𝐬𝐤𝐬: In industries like healthcare, finance, and law, accuracy and relevance are paramount. SLMs can be fine-tuned on targeted datasets, often outperforming general LLMs for specific tasks while using a fraction of the computational resources. For example, an SLM trained on medical terminology can provide precise and actionable insights without the overhead of a massive model. 𝟑. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐟𝐨𝐫 𝐋𝐢𝐠𝐡𝐭𝐰𝐞𝐢𝐠𝐡𝐭 𝐀𝐈: SLMs leverage sophisticated methods to maintain high performance despite their smaller size: • Pruning: Eliminates redundant parameters to streamline the model. • Knowledge Distillation: Transfers essential knowledge from larger models to smaller ones, capturing the “best of both worlds.” • Quantization: Reduces memory usage by lowering the precision of non-critical parameters without sacrificing accuracy. These techniques enable SLMs to run efficiently on edge devices where memory and processing power are limited. Despite these advantages, the industry often defaults to LLMs due to a few prevalent mindsets: • “Bigger is Better” Mentality: There’s a common belief that larger models are inherently superior, even when an SLM could perform just as well or better for specific tasks. • Familiarity Bias: Teams accustomed to working with LLMs may overlook the advanced techniques that make SLMs so effective. • One-Size-Fits-All Approach: The allure of a universal solution often overshadows the benefits of a tailored model. Perhaps it’s time to rethink our approach and adopt a “right model for the right task” mindset. By making AI faster, more accessible, and more resource-efficient, SLMs open doors across industries that previously found LLMs too costly or impractical. What are your thoughts on the role of SLMs in the future of AI? Have you encountered situations where a smaller model outperformed a larger one? I’d love to hear your experiences and insights.

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

My biggest fear as an AI startup founder? Getting crushed by giants before proving our value. 6 counterintuitive strategies that helped CrewAI win against better-funded competitors: When I started CrewAI, we faced tech giants with unlimited resources and VC-backed startups with massive teams. I was just a Brazilian developer with an open-source project. Today, we power 50M+ agents monthly and partner with IBM, Cloudera, PwC, and NVIDIA. 1. Turn "small" into speed While others debated in meetings, we shipped product. Our size became our superpower - we could experiment faster than anyone else. 2. Build in public, strategically We shared every win and lesson learned. This wasn't about transparency. It was about creating a movement people wanted to join. Our community became our strongest evangelists. 3. Education drives adoption Two courses with Andrew Ng on Deeplearning.[ai] changed everything. Instead of pushing features, we taught AI agent orchestration. Our customers became champions because they truly understood the value. 4. Focus on tomorrow's problems We looked 3-5 years ahead: Companies will deploy thousands of AI agents. They'll need ways to manage this complexity. While others chase today's features, we're building the control plane for the agentic future. 5. Be a partner, not a vendor Enterprise leaders don't want another tool. They want partners who share their vision for AI transformation. This mindset attracted IBM and PwC as partners. 6. Let competition fuel growth Each new competitor made us stronger: • Their presence validated our market • Their size made us more agile • Their complexity highlighted our simplicity The key insight? Today's AI winners aren't just building tools. They're preparing for what's next. Soon, every enterprise will run hundreds of AI agents handling sales, support, content, and analytics. How will you manage them all? That's why we built CrewAI - tomorrow's AI infrastructure to help enterprises orchestrate agents, ensure compliance, and scale securely. Want to future-proof your AI strategy? DM me or follow @joaomdmoura for insights on the agentic future. ⚡

🤩 What if you could use just 17k fine-tuning samples and change only 5% of the model to make a small LLM reason like the o1-preview model? DeepSeek-R1’s famous trick to make cheaper/smaller LLMs behave more like reasoning models seems to be working well—another paper reproduces similar results more efficiently! The DeepSeek-R1 paper introduced an experiment where they fine-tuned smaller Qwen and Llama models to improve their reasoning abilities by using outputs from the larger DeepSeek-R1 671B model. Some have called this soft distillation, while others say it's fine-tuning, but you get the point! Another recent paper has done something similar: ⛳ The paper focuses on improving LLMs' reasoning ability by getting them to generate Long Chain-of-Thought (Long CoT) responses for complex problems. ⛳It uses DeepSeek-R1's results to fine-tune smaller models like the Qwen2.5-32B-Instruct . ⛳They use only supervised fine-tuning (SFT) and low-rank adaptation (LoRA) with just 17k samples, meaning they didn't even modify the entire model (only 5% of it as per the authors). ⛳ The paper highlights that the structure of Long CoTs is far more critical than the content of individual reasoning steps. Errors in the content (e.g., mistakes in reasoning steps) have little impact, while disrupting the structure (e.g., deleting or shuffling reasoning steps) significantly hurts performance. ⛳They demonstrate that this approach works across different models and tasks! If this approach works well, many smaller models could be adapted to perform reasoning tasks! 💡 It's super interesting, every breakthrough with large models seems to push smaller models to become much more powerful simply by using these big models as teachers. Link: https://lnkd.in/e5rzRWqd

AI monoliths vs Unix Philosophy:  The case for Small Specialized Models. The current thinking in AI is that AGI is coming, and that one gigantic model will be able to reason and solve business problems ranging from customer support to product development. Currently, agents are basically big system prompts on the same gigantic model. Through prompt engineering, AI builders are trying to plan and execute complex multi-step processes. This is not working very well.  This monolith view of AI is in sharp contrast to how we teach engineers to build systems. When multiple people have to build complex systems, they should build specialized modular components. This makes systems reliable and helps large teams of people coordinate with specs that are easy to explain, engineer and evaluate. Monolithic gigantic AI systems are also extremely wasteful in terms of energy and cost: using GPT4o as a summarizer, fact checker, or user intent detector, reminds me of the first days of the big data wave, when people where spinning Hadoop clusters to process 1GB of data.  Instead, I would like to make the case for Small Specialized Models following the Unix philosophy guidelines:  1. Write programs that do one thing and do it well. 2. Write programs to work together. 3. Write programs to handle text streams, because that is a universal interface. Now replace programs with AI models. I believe that the best way to engineer AI systems will be to use post-training to specialize Llama small models into narrow focused jobs. 'Programming' these small specialized models will be done by creating post-training datasets. These datasets will be created by transforming internal data by prompting big foundation models and then distilling them through post-training. This is similar to the "Textbooks is all you need", but for narrow jobs like summarization, legal QA, and so on, as opposed to building general-purpose small models. Several papers have shown that it is possible to create post-training datasets by prompting big models and creating small specialized models that are faster and also outperform their big teachers in narrow tasks. Creating small specialized models is currently hard. Evaluation, post-training data curation and fine-tuning are tricky, and better tools are needed. Still, its good to go back to UNIX philosophy to inform our future architectures. 

The frenzy around the new open-source reasoning #LLM, DeepSeek-R1, continued today, and it’s no wonder. With model costs expected to come in 90-95% lower than OpenAI o1, the news has reverberated across the industry from infrastructure players to hyperscalers and sent stocks dropping. Amid the swirl of opinions and conjecture, I put together a brief synopsis of the news – just the brass tacks – to try and simplify the implications and potential disruptions and why they matter to leaders. 1. 𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝘂𝗹𝗲𝘀: DeepSeek-R1-Zero ditched supervised fine-tuning and relied solely on reinforcement learning—resulting in groundbreaking reasoning capabilities but less polished text. 2. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮: Even a tiny set of curated examples significantly boosted the model's readability and consistency. 3. 𝗦𝗺𝗮𝗹𝗹 𝗕𝘂𝘁 𝗠𝗶𝗴𝗵𝘁𝘆 𝗠𝗼𝗱𝗲𝗹𝘀: Distilled smaller models (1.5B–70B parameters) outperformed much larger ones like GPT-4o, proving size isn’t everything. Why does this matter to business leaders?  • 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗲𝗿 𝗳𝗼𝗿 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗖𝗼𝘀𝘁𝘀: Skipping supervised fine-tuning and leveraging reinforcement learning could reduce costs while improving reasoning power in AI models. • 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: Investing in carefully curated data (even in small quantities) can lead to a competitive edge for AI systems. • 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 𝗦𝗮𝘃𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Smaller, distilled models that perform better than larger ones can drive efficiency, cutting costs on infrastructure while maintaining high performance. Let me know if you agree… And if you're curious, the DeepSeek-R1 paper is a must-read. https://lnkd.in/eYPidAzg #AI #artificialintelligence #OpenAI #Hitachi

Building efficient small language models is no longer just about shrinking large architectures. It’s also about making smart engineering decisions that balance performance, speed, and memory efficiency. This is where techniques like quantization become essential. In a recent blog, the team at Esperanto Technologies, Inc shares practical insights into how they leverage quantization techniques to optimize small language models. They explain the core concept of quantization — reducing the precision of model weights, which can significantly lower storage and compute costs. To minimize the loss in accuracy due to quantization, they explore several strategies, including mixed-mode techniques, where different parts of a model use varying numerical precisions based on each layer’s needs. This flexible approach allows small models to maintain high quality while running more efficiently on specialized hardware. The blog also dives into more detailed techniques, making it a great read for anyone interested in building smaller, more efficient models. #DataScience #MachineLearning #Analytics #LLM #ModelOptimization #Quantization #EdgeAI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gKSWAxmc

Based on both the AI Index Report 2025 and the Securing AI Agents with Information-Flow Control (FIDES) paper, here are actionable points tailored for organizations, and AI teams, Action Points for AI/ML Teams 1. Build Secure Agents with IFC Leverage frameworks like FIDES to track and restrict data propagation via label-based planning. Use quarantined LLMs + constrained decoding to minimize risk while extracting task-critical information from untrusted sources. 2. Optimize Cost and Efficiency Use smaller performant models like Microsoft’s Phi-3-mini to reduce inference costs (up to 280x lower than GPT-3.5). Track model inference cost per task, not just throughput—consider switching to open-weight models where viable. 3. Monitor Environmental Footprint Measure compute and power usage per training run. GPT-4 training emitted ~5,184 tons CO₂; Llama 3.1 reached 8,930 tons. Consider energy-efficient hardware (e.g., NVIDIA B100 GPUs) and low-carbon data centers. #agenticai #responsibleai