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Practical guides to fine-tuning, distillation, and deploying small language models.
What Does It Cost to Fine-Tune a Small Language Model?
The distil labs free tier covers two full training runs at no cost and credit packs are listed at $1,000 for 10 runs — plus the configuration parameters that actually determine what a run consumes.
What Does It Cost to Run a Small Language Model?
Published distil labs figures put a fine-tuned student at roughly $3 per million requests on one H100 — here is what that covers, how it was measured, and which assumptions move it.
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 does not 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 — showing the calculation 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 is not 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.