How to Train AI Models on a Budget

Most are sleeping on the power of 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, and every company should have a Distillation Factory to stay competitive This technique is reshaping how companies build efficient, scalable, and cost-effective AI. First, 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? Also known as knowledge distillation, is a machine learning technique where a smaller, more efficient "student" model is trained to replicate the behavior and performance of a larger, more complex "teacher" model. Think of it as a master chef (the teacher) passing down their culinary expertise to an apprentice (the student) without sharing the exact recipe. The student learns by observing the teacher’s outputs and mimicking their decision-making process, resulting in a lightweight model that retains much of the teacher’s capabilities but requires fewer resources. Introduced by Geoffrey Hinton in his 2015 paper, “Distilling the Knowledge in a Neural Network,” the process involves: 1/ Teacher Model: A large, powerful model trained on massive datasets. 2/ Student Model: A smaller, efficient model built for faster, cheaper deployment. 3/ Knowledge Transfer: The student learns from the teacher’s outputs—distilling its intelligence into a lighter version. There are several types of distillation: 1/ Response-Based: The student mimics the teacher’s final outputs 2/ Feature-Based: The student learns from the teacher’s intermediate layer representations. 3/ Relation-Based: The student captures relationships between the teacher’s outputs or features. The result? A student model that’s faster, cheaper to run, and nearly as accurate as the teacher, making it ideal for real-world applications. 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝘁𝗼𝗿𝘆? In today’s AI landscape, very large LLMs are incredibly powerful but come with significant drawbacks: high computational costs, massive energy consumption, and complex deployment requirements. A Distillation Factory is a dedicated process or team focused on creating distilled models, addressing these challenges and unlocking transformative benefits. Here’s why every company should invest in one: 1/ Cost Efficiency: Distilled models cut costs, running on minimal GPUs or smartphones, not data centers. 2/ Scalability: Smaller models deploy easily. 3/ Faster Inference: Quick responses suit real-time apps. 4/ Customization: Tailor models for healthcare or finance with proprietary data, no full retraining. 5/ Sustainability: Lower compute needs reduce carbon footprints, aligning with green goals. 6/ Competitive Edge: Rapid AI deployment via distillation outpaces costly proprietary models. A Distillation Factory isn’t just a technical process; it’s a strategic move.

When you want a large language model to get better at a specific task—like solving math problems or navigating websites—the standard approach is to finetune it: you adjust the model's internal parameters using training data and gradient descent, which is expensive, requires lots of data, and often makes the model worse at everything else. Instead of changing the model's parameters, this paper proposes to run the model on a small set of problems multiple times, compare the successful and failed attempts, and use the model itself to write down natural-language "lessons learned"—things like "when solving geometry problems, always check that your solution falls within the valid region." These lessons get iteratively refined across a few rounds and then get pasted into the prompt at inference time. The method is modeled after GRPO, a reinforcement learning algorithm where you generate a group of outputs, score them, and use the relative quality differences to improve the model—except here the "improvement" happens in the prompt text rather than in the weights. The paper shows that doing this with just 100 training examples and about $18 worth of API calls on a large frozen model (DeepSeek-V3.1-Terminus, 671 billion parameters) outperforms smaller models that were finetuned with thousands of examples at costs exceeding $10,000. The results hold across both math reasoning and web search tasks, and unlike finetuned models that degrade when moved to a different domain, swapping in a different set of learned experiences lets the same frozen model perform well in multiple domains simultaneously. Read with an AI tutor: https://lnkd.in/eA3Ud2a2 Download the PDF: https://lnkd.in/ekVxsz3B

In a recent roundtable with fellow CXOs, a recurring theme emerged: the staggering costs associated with artificial intelligence (AI) implementation. While AI promises transformative benefits, many organizations find themselves grappling with unexpectedly high Total Cost of Ownership (TCO). Businesses are seeking innovative ways to optimize AI spending without compromising performance. Two pain points stood out in our discussion: module customization and production-readiness costs. AI isn't just about implementation; it's about sustainable integration. The real challenge lies in making AI cost-effective throughout its lifecycle. The real value of AI is not in the model, but in the data and infrastructure that supports it. As AI becomes increasingly essential for competitive advantage, how can businesses optimize costs to make it more accessible? Strategies for AI Cost Optimization 1.Efficient Customization - Leverage low-code/no-code platforms can reduce development time - Utilize pre-trained models and transfer learning to cut down on customization needs 2. Streamlined Production Deployment - Implement MLOps practices for faster time-to-market for AI projects - Adopt containerization and orchestration tools to improve resource utilization 3. Cloud Cost Management -Use spot instances and auto-scaling to reduce cloud costs for non-critical workloads. - Leverage reserved instances For predictable, long-term usage. These savings can reach good dollars compared to on-demand pricing. 4.Hardware Optimization - Implement edge computing to reduce data transfer costs - Invest in specialized AI chips that can offer better performance per watt compared to general-purpose processors. 5.Software Efficiency - Right LLMS for all queries rather than single big LLM is being tried by many - Apply model compression techniques such as Pruning and quantization that can reduce model size without significant accuracy loss. - Adopt efficient training algorithms Techniques like mixed precision training to speed up the process -By streamlining repetitive tasks, organizations can reallocate resources to more strategic initiatives 6.Data Optimization - Focus on data quality since it can reduce training iterations - Utilize synthetic data to supplement expensive real-world data, potentially cutting data acquisition costs. In conclusion, embracing AI-driven strategies for cost optimization is not just a trend; it is a necessity for organizations looking to thrive in today's competitive landscape. By leveraging AI, businesses can not only optimize their costs but also enhance their operational efficiency, paving the way for sustainable growth. What other AI cost optimization strategies have you found effective? Share your insights below! #MachineLearning #DataScience #CostEfficiency #Business #Technology #Innovation #ganitinc #AIOptimization #CostEfficiency #EnterpriseAI #TechInnovation #AITCO

🚀 How to manage the budget dilemma when dealing with "Model Size" vs "Data Size" One of the biggest decisions in building language models today is figuring out where to spend your limited compute budget. Should you train a bigger model or train on more data? It’s something I’ve had to decide on more than once, especially while planning model training pipelines with fixed GPU hours and tight delivery timelines. This is a real-world challenge faced by AI product and engineering leaders every day. Some recent experiments in this space have shown something interesting: 💠 If you have a small budget, it’s often better to go with a smaller model and train it on a lot of data. Bigger models don’t help much if they don’t have enough data to learn from. 💠 As your budget increases, the ideal approach shifts. You can start scaling up the model size, but the data size still plays a major role. The improvement you get from adding more parameters tends to flatten out quickly. What continues to help is feeding your model more tokens. The key takeaway for you is: ◾ For a fixed budget, a medium-sized model with the right amount of training data can outperform a large model with limited data. ◾ As budgets grow, don't just throw more parameters at the problem. Focus on the balance between model size and data, and lean toward more training data if you're unsure. Finding that balance is key. It often determines whether you’re building something usable or simply burning through compute. 🔍 While there’s no plug and play enterprise product for this yet, there are practical tools you can explore: 1️⃣ A helpful GitHub repo on scaling laws that shows how to model this trade-off (link in comments) 2️⃣ A Hitchhiker’s Guide to Scaling Law Estimation, which walks through small-scale simulations and extrapolation techniques (link in comments) I highly recommend you use these type of Open-source tools, combined with internal logs and basic plotting, to give your AI teams a strong head start on getting the most out of your training budgets. Remember, it is not just about building the POCs, it is about getting these AI products/ solutions into production I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

Last week, a VC firm invited me to eval an AI startup. The founder pitched building a custom 70B parameter model for insurance underwriting. Tailored inference. Vertical SaaS. The works. The ask? $1M seed round. I stayed quiet during the pitch. Every founder deserves respect — they're risking everything to build something. That courage is real. But afterwards, I told the VC: "This math doesn't work. Not even close." Here's why 👇 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰 𝗠𝗮𝘁𝗵 𝗡𝗼𝗯𝗼𝗱𝘆 𝗗𝗶𝗱 Training a 70B model needs ~8.4 × 10²³ FLOPs. One FLOP = one math operation (a multiply or add). A 70B model does 6 of these per parameter, per token. Trained on 2 trillion tokens. 6 × 70B × 2T = 840,000,000,000,000,000,000,000 operations. That's 8.4 × 10²³. Let that sink in. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗖𝗼𝘀𝘁𝘀 → 512 H100 GPUs × 40 days × $2.50/hr = $1.2M just for ONE training run → First run WILL fail. Budget 2-3 attempts = $2.5-3.5M → That's just training. No team. No data. No infra. His entire $1M? Gone in 3 weeks. Not even one complete training run. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗖𝗼𝘀𝘁𝘀 (𝗣𝗼𝘀𝘁-𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴) Serving 70B model to customers: → 4x H100 GPUs minimum = $7,200/month (cloud) → 1000 concurrent users? 16x H100s = $28,800/month → That's $3.5L/month just to keep the lights on 𝗪𝗵𝗮𝘁 𝗛𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 Fine-tune Llama 3 70B with QLoRA on insurance data. Cost: $200-500. Time: 2 days. Same business outcome. Spend the $1M on distribution, not GPUs. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻 Bangalore has incredible founders. Brilliant people building real things. But VCs — please do the FLOP math before writing cheques. Founders — please learn compute economics before pitching "we'll build our own model." Not every AI startup needs to train from scratch. Most shouldn't. The moat isn't the model. It's the data, distribution, and domain expertise. $1M buys you a world-class fine-tuned product. $1M doesn't buy you 1% of a foundation model. Know the difference. 💡 Quick Reference: 70B Model Costs Training: $1.2-3.5M (cloud GPUs) Inference: $7,200-28,800/month Fine-tuning instead: $200-500 Engineering team (12 months): $1-3M Total realistic budget: $5-8M minimum 1 FLOP = 1 floating point operation 70B model training = 8.4 × 10²³ FLOPs Your H100 does 10¹⁵ FLOPs/sec Do the division before the pitch. #AI #Startups #Bangalore #VentureCapital #DeepTech #GPUEconomics #FounderAdvice

𝐘𝐨𝐮 𝐚𝐫𝐞 𝐩𝐫𝐨𝐛𝐚𝐛𝐥𝐲 𝐨𝐯𝐞𝐫𝐩𝐚𝐲𝐢𝐧𝐠 𝐟𝐨𝐫 𝐀𝐈 𝐜𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧  95% of teams jump straight to fine-tuning when a $0 prompt would have worked. 𝐋𝐞𝐭 𝐦𝐞 𝐬𝐡𝐨𝐰 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐄𝐗𝐀𝐂𝐓 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐈 𝐮𝐬𝐞 𝐭𝐨 𝐬𝐚𝐯𝐞 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬: The $0 → $100K Decision Tree Start here ↓ --- 𝐋𝐞𝐯𝐞𝐥 𝟏: 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 (𝐂𝐨𝐬𝐭: $𝟎) "Just ask better questions." Sounds dumb. Works incredibly well. Before spending a dollar, try: - Few-shot examples in your prompt - Chain-of-thought reasoning - Role-based prompting ("You are an expert...") ✅ Use when: The model already knows what you need ❌ Do not use when: The model lacks domain knowledge Real talk: If your prompt is under 3 iterations, you have not tried hard enough. --- 𝐋𝐞𝐯𝐞𝐥 𝟐: 𝐑𝐀𝐆 (𝐂𝐨𝐬𝐭: $-$$) "Give the model a library card." This is where things get interesting. You are NOT changing the model. You are changing what it can ACCESS. Perfect for: - Company documentation Q&A - Legal/compliance lookups - Real-time data that changes daily The magic: Model stays dumb about your data, but answers stay smart. 💡 Hot take: RAG is underrated. Most "fine-tuning" projects should be RAG projects. Why? Because your knowledge base will update. Your fine-tuned model will not. --- 𝐋𝐞𝐯𝐞𝐥 𝟑: 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 (𝐂𝐨𝐬𝐭: $$-$$$) "Teach the model to think like you." Here is when you ACTUALLY need it: - Specific writing style/tone - Domain-specific reasoning patterns - Consistent structured outputs - Reducing prompt lengths ⚠️ Warning: Fine-tuning does not add knowledge it changes behavior. If the model does not know something, fine-tuning will not help. Common mistake: Fine-tuning on facts → Model hallucinates confidently Better approach: RAG for facts + Fine-tuning for style --- 𝐋𝐞𝐯𝐞𝐥 𝟒: 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 (𝐂𝐨𝐬𝐭: $$$$$) "Welcome to the big leagues." Unless you're: - OpenAI, Google, or Anthropic - Sitting on 100M+ unique data points - Building the next foundation model You do not need this. Seriously. I have seen teams waste $100K+ on custom models when a $200 fine-tune would have crushed it. --- 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐦𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐭𝐫𝐞𝐞: 1. Try advanced prompting (2 hours) 2. Still not working? Add RAG (2 days) 3. Still not working? Fine-tune (2 weeks) 4. Building the next GPT? Train from scratch (2 years) --- 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐘𝐎𝐔𝐑 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐛𝐨𝐮𝐭 𝐜𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/esF52fm5 #AIEngineering #GenAI #AIAgents #AgenticAI

The bottleneck isn't GPUs or architecture. It's your dataset. Three ways to customize an LLM: 1. Fine-tuning: Teaches behavior. 1K-10K examples. Shows how to respond. Cheapest option. 2. Continued pretraining: Adds knowledge. Large unlabeled corpus. Extends what model knows. Medium cost. 3. Training from scratch: Full control. Trillions of tokens. Only for national AI projects. Rarely necessary. Most companies only need fine-tuning. How to collect quality data: For fine-tuning, start small. Support tickets with PII removed. Internal Q&A logs. Public instruction datasets. For continued pretraining, go big. Domain archives. Technical standards. Mix 70% domain, 30% general text. The 5-step data pipeline: 1. Normalize. Convert everything to UTF-8 plain text. Remove markup and headers. 2. Filter. Drop short fragments. Remove repeated templates. Redact PII. 3. Deduplicate. Hash for identical content. Find near-duplicates. Do before splitting datasets. 4. Tag with metadata. Language, domain, source. Makes dataset searchable. 5. Validate quality. Check perplexity. Track metrics. Run small pilot first. When your dataset is ready: All sources documented. PII removed. Stats match targets. Splits balanced. Pilot converges cleanly. If any fail, fix data first. What good data does: Models converge faster. Hallucinate less. Cost less to serve. The reality: Building LLMs is a data problem. Not a training problem. Most teams spend 80% of time on data. That's the actual work. Your data is your differentiator. Not your model architecture. Found this helpful? Follow Arturo Ferreira.

You Don't Need GPUs to Train AI Agents 95% of teams think Agent Lightning requires custom models and GPU clusters. They're wrong. Here's what it actually does... THE MISCONCEPTION: "Agent Lightning = Train your own LLM = Need expensive GPUs" THE REALITY: Agent Lightning optimizes HOW you use GPT-4, Claude, or Gemini. → No model training → No GPUs required → Just better prompts and strategies → Learned from your production data WHAT IT OPTIMIZES: Your system prompts → From "You are helpful" to "Use tools before answering" Your few-shot examples → Keeps winners, removes losers from production data Your tool-calling patterns → Learns WHEN to search, WHEN to calculate, in what ORDER Your agent parameters → Finds optimal temperature, token limits, sampling HOW IT WORKS: Week 1-2: Your agent handles 1,000 queries via GPT-4 → 700 succeed, 300 fail → Lightning captures what made 700 work Week 3: Optimization run (30 minutes, CPU only) → Analyzes patterns from successful queries → Generates better prompts → Tests variants → Deploys winner Week 4: Same agent, same GPT-4 API, better results → Success rate jumps from 70% to 85% Continuous cycle repeats automatically. REAL NUMBERS: SQL Agent example: → Before: 68% accuracy → After 4 weeks: 84% accuracy → Same API, zero GPU training THE COST: Infrastructure: $4K/month (CPU servers) API savings: $6K/month (fewer wasted tokens) Net benefit: +$2K/month plus quality gains WORKS WITH: ✓ OpenAI (GPT-4, GPT-4o) ✓ Anthropic (Claude) ✓ Google (Gemini) ✓ Any public API ✓ LangChain, LangGraph, AutoGen, CrewAI ✓ Integration: 10-15 lines of code THE INSIGHT: Most agents using GPT-4 waste 40% of API calls on: → Poorly worded prompts → Unnecessary retries → Wrong tool selections → Suboptimal examples Agent Lightning learns the optimal configuration from YOUR data. WHO THIS IS FOR: → Already using GPT-4/Claude via API → Agents work but could be better → Manual prompt engineering is endless → Want systematic improvement THE TIMELINE: Week 1: Setup Week 2: Integration Week 3: First optimization Week 4: Production deployment 30 days total. THE CHOICE: Keep tweaking prompts manually: → 80 hours/month engineer time → Improvements plateau at 10% → Never learn from production Use Agent Lightning: → Learns automatically from production → Compounds to 25%+ improvement → Frees engineers for real work THE BOTTOM LINE: Agent Lightning isn't training your own LLM. It's making your GPT-4/Claude usage smarter through: → Better prompts → Better examples → Better strategies → Learned from real data No GPUs. No custom models. No ML PhD. Just systematic improvement of what you're already doing. GitHub: Microsoft/agent-lightning MIT License | 1,000+ teams testing Your competitors are implementing this quarter. What's stopping you?🚀

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

AI doesn’t have to be expensive. Most companies just use it… inefficiently. During my conversation with Eyal Gutkind from Red Hat, he shared one of the most practical insights I’ve heard at Amazon Web Services (AWS) re:Invent 2025: “You can save 30–40% of your AI cost on 80% of your workloads — today.” Here’s the key idea in simple terms: 💰 GPU instances are powerful — and extremely expensive But most AI workloads don’t need full, high-precision GPU power. Eyal explained that you can: 🔹 Train or run models on cheaper hardware (AWS Inferentia, Google TPU, etc.) 🔹 Use lower precision formats (like BF16) for 95% of your queries 🔹 Keep the expensive GPU runs only for the top 5–10% of cases that truly need full accuracy 🔹 Deploy all of this through a single Inference Server across multiple platforms The result? Massive cost reduction for the same business value. And the best part: These optimizations are not futuristic. They’re not theoretical. They work today. If you want more practical, real-world advice like this, the full Red Hat interview is packed with it. 👉 Watch the full conversation: https://lnkd.in/ensvKrkb Where do you see the biggest opportunity to reduce AI costs in your organization? #RedHatAmbassador #AWSAmbassador #AI #OpenSource #AWSreInvent #AICosts #ModelOptimization #CloudComputing #DigitalTransformation #Efficiency #ad