This post is continuous to the question someone asked in academic webinar, 𝐰𝐡𝐲 𝐢 𝐜𝐚𝐧𝐧𝐨𝐭 𝐮𝐬𝐞 𝐦𝐲 𝐥𝐚𝐩𝐭𝐨𝐩 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐚𝐥𝐥 𝐋𝐋𝐌𝐬, 𝐚𝐬𝐤 𝐪𝐮𝐞𝐫𝐲, 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠, 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐢𝐧𝐠, 𝐞𝐭𝐜 𝐫𝐚𝐭𝐡𝐞𝐫 𝐠𝐨𝐢𝐧𝐠 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐩𝐫𝐞𝐦𝐢𝐬𝐞𝐬!
Because, It’s not just one thing.
There are 3 distinct layers, each with very different costs, infrastructure, and challenges 👇
1. 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠
This is where foundation models are created.
Trained on massive, internet-scale datasets
Requires thousands of GPUs/TPUs running for weeks or months
Costs = $$$$$ (tens to hundreds of millions)
Storage: terabytes to petabytes (data + checkpoints)
Only few organizations work at this layer.
2. 𝐌𝐨𝐝𝐞𝐥 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞
This is what we interact with daily.
Chat, Q&A, copilots, automation
Runs in real-time → latency is critical
Can run on CPUs, GPUs, or optimized accelerators
At scale: requires heavy optimization (batching, caching, quantization)
This is where performance, scale, and cost per request matter most.
3. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 / 𝐑𝐀𝐆
This is where most businesses unlock value.
Fine-tuning: adapting models using techniques like LoRA
RAG: grounding AI with enterprise data via embeddings + vector DBs
Doesn’t always require massive compute
Transforms generic models into AI that understands your data, workflows, and domain
This is where real ROI and differentiation happen.
𝘐𝘯 𝘢 𝘯𝘶𝘵𝘴𝘩𝘦𝘭𝘭:
Training = Massive investment + research
Inference = Real-time system engineering
Fine-tuning/RAG = Business value layer
If you’re building in AI:
𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 → 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞 → 𝐒𝐜𝐚𝐥𝐞
You can use your laptop, but it depends on the use case.
Would love to hear your point of view, correction or feedback are always welcome!
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