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Practical guides to fine-tuning, distillation, and deploying small language models.
Q4 vs Q8 vs FP16 for Your SLM
A side-by-side of the three precisions you will actually choose between when serving a distilled model, with the documented bits-per-weight figures, the size arithmetic for real model tiers, and the case for each.
Qwen vs Llama vs Gemma for Tool Calling
Which of the three big open model families you can actually use for tool-calling tasks, what the measured results say about each, and why the family matters less than whether you fine-tuned at all.
Qwen3 1.7B vs Gemma 3 1B for Classification
Head-to-head on the two most common 1B-class students: specs, published fine-tuning ranks, the task-compatibility difference that decides most cases, and which one to train first.
Self-Hosted vs Managed Inference
Comparing running your own SLM endpoint against paying someone to run it, across control, cost at volume, operational burden and data residency — with the published per-million-request figures for both sides.
Trace Formats: OpenAI Messages vs Langfuse
A field-by-field comparison of the two trace formats accepted by distil labs, when to pick each, and the two additional observation formats for images and unstructured context.
Traces vs Synthetic Data: Which Trains a Better Model?
Benchmark results comparing a student fine-tuned directly on production traces against one trained on synthetic data generated from the same traces, across five corruption scenarios.
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.