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Practical guides to fine-tuning, distillation, and deploying small language models.
What Latency Can You Expect from an SLM?
Every SLM latency figure distil labs has published, reported with the model, runtime, hardware and load it was measured under, plus why a single per-model number doesn't exist and what actually moves the value on your setup.
Which SLM Fits in 4GB of VRAM?
Weight-size arithmetic for every student model in the distil labs catalog at BF16, INT8, and 4-bit, with the calculation shown so you can check it against your own parameter counts.
Which Teacher Model Should You Pick?
The full distil labs teacher catalog compared, with a measured five-teacher head-to-head: which teachers support tool calling, which impose a temperature constraint, and why the documented default isn't the current pick.
Best Small Language Model for Fine-Tuning in 2025: Qwen vs Llama vs Gemma
A head-to-head comparison of Qwen 3, Llama 3.2, and Gemma 3 for fine-tuning across classification, QA, NER, and tool-calling tasks, with benchmark data to back every claim.
Distillation vs Fine-Tuning: What's the Difference?
Knowledge distillation and fine-tuning are related but distinct techniques. Learn how they differ, when to use each, and how combining them produces the best results for production AI.
Distillation vs Quantization: Which Shrinks Your Model Better?
Distillation and quantization both reduce model size, but they work in fundamentally different ways. Learn the trade-offs and when to use each approach, or combine them.
LoRA vs Full Fine-Tuning: When to Use What
Compare LoRA and full fine-tuning for small language models. Learn the trade-offs in accuracy, speed, and memory so you can pick the right approach for your project.
Build vs Buy: Training Your Own SLM
Building means owning synthetic data generation, hyperparameter search and evaluation yourself. A stage-by-stage breakdown of what each path costs and the cases where building is clearly right.
Calculating the ROI of a Task-Specific Model
A five-step procedure for turning a task-specific model into a break-even volume, using real CLI commands and a free evaluation gate before you commit any budget.