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Training massive AI models has dominated headlines for years. GPT-4, Claude, Gemini—these models cost millions to train and capture our imagination. But here's what Silicon Valley whispers about in private: training is becoming commoditized, while inference is the new battleground.

Why? Because inference—the process of running a trained model to generate outputs—happens billions of times per day. Every ChatGPT response, every Midjourney image, every AI code completion runs inference. As AI becomes embedded in every application, inference costs can make or break a business model.

Quantization reduces the precision of model weights and activations. Think of it like compressing a high-resolution photo—you lose some detail, but the image remains recognizable and the file size drops dramatically.

How it works: Neural networks typically use 32-bit floating-point numbers (FP32) for calculations. Quantization converts these to 8-bit integers (INT8) or even 4-bit representations. A model that once required 16GB of memory can shrink to 4GB or less.

The math: An FP32 number uses 32 bits of memory. An INT8 number uses 8 bits. That's a 4x reduction in memory usage and bandwidth requirements, which translates directly to faster inference and lower costs.

Real-world impact: Meta's Llama models support 4-bit quantization, enabling a 70-billion parameter model to run on consumer GPUs that would normally struggle with models a fraction of that size. Companies like Hugging Face report inference cost reductions of 50-75% using quantization techniques like GPTQ and GGUF.

The tradeoff: Aggressive quantization can hurt accuracy on complex tasks. The art lies in finding the sweet spot—often INT8 for most applications, with selective FP16 precision for critical layers.

Knowledge distillation trains a smaller "student" model to mimic a larger "teacher" model's behavior. The student learns not just from the training data, but from the teacher's nuanced predictions.

How it works: Instead of training on hard labels ("this is a cat"), the student learns from the teacher's soft probabilities ("85% cat, 10% dog, 5% fox"). This richer signal captures the teacher's understanding of ambiguous cases and edge cases.

Real-world impact: Google's DistilBERT retains 97% of BERT's language understanding while being 40% smaller and running 60% faster. OpenAI likely uses distillation to create GPT-3.5 from GPT-4, dramatically reducing costs while maintaining quality for most use cases.

The innovation: Recent techniques like "on-policy distillation" have students generate their own training data, which the teacher then scores. This creates student models that sometimes exceed teacher performance on specific tasks.

Not every query needs your most powerful model. Routing intelligently directs simple requests to small, fast models and complex queries to larger models.

How it works: A lightweight classifier analyzes incoming requests and assigns them to the appropriate model tier. Simple factual queries might go to a 7B parameter model, while complex reasoning tasks route to a 70B model.

The economics: If 70% of queries can be handled by a model that's 10x cheaper to run, you've just cut inference costs by more than half—even accounting for the routing overhead.

Real-world implementation: Anthropic's approach with Claude implicitly uses routing concepts—different model sizes for different use cases. Startups like Martian and Not Diamond build explicit routing layers that can reduce costs by 60-85% while maintaining quality thresholds.

Advanced routing: Some systems use cascade routing, where queries first hit a tiny model. If confidence is low, they cascade to progressively larger models. This minimizes expensive inference calls.

Edge inference runs models directly on devices—phones, laptops, cars, IoT sensors—rather than in the cloud. This represents a fundamental shift in AI architecture.

Why it matters:

The real magic happens when companies layer these techniques:

Companies like Meta are pioneering this stack. Their Llama models support aggressive quantization, third-party developers create distilled variants, and Meta is actively working on edge deployment for WhatsApp and Instagram features.

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_{Image by Brian Penny on Pixabay.}

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