Understanding model distillation: make your LLM run fast on a laptop without cloud costs

I remember the first time I tried to run a modern language model on my laptop: it was slow, memory-starved, and I spent more time swapping RAM than actually getting useful responses. Since then I’ve tested pruning, quantization, on-device runtimes and — most importantly — model distillation. Distillation is the technique that finally let me run capable models locally without paying cloud fees or sacrificing privacy. In this piece I’ll explain what distillation actually is, why it helps, the tradeoffs, and a practical roadmap to get a distilled LLM running on a modest laptop.

What is model distillation, in plain terms?

At its core, distillation is a compression method. You take a large, typically high-performing “teacher” model and train a smaller “student” model to imitate the teacher’s behavior. The student learns from the teacher’s outputs (probabilities, logits, or hidden representations) rather than only from raw training data. The result: a smaller model that behaves like the larger one on many tasks, but runs faster and consumes less memory.

Think of it like an expert mentoring a junior: the expert explains not just the correct answer but also how confident they are, which signals are important, and subtle patterns. The junior learns those patterns and becomes much more capable than if they only memorized final answers.

Why distillation works better than naïve model shrinking

There are simpler ways to reduce model size — prune weights, lower precision (quantization), or train a smaller model from scratch. Those methods help, but distillation tends to preserve behavior that matters in practice. That’s because the student gets supervision from the teacher’s softened outputs, capturing nuanced output distributions and generalization cues the original training labels might not convey.

In practice I combine techniques: distillation as the primary compression, followed by aggressive quantization (8-bit, 4-bit, or even 3-bit depending on hardware) and optimized on-device runtimes such as GGML-based libraries or ONNX with quantization. Distillation makes quantization less harmful because the student learns to be robust to approximate representations.

Types of distillation you’ll encounter

Distillation isn’t a single algorithm. Here are common flavors you should know:

Sequence-level distillation is particularly practical for LLMs used in generation: you generate text from the teacher and train the student with standard language modeling on those generated sequences. It’s straightforward and scales well.

What you lose and what you keep

Distillation is not magic. Typical tradeoffs include:

For many real-world workflows — chat assistants, code completion, note summarization — the drop in peak capability is acceptable. I personally prefer a fast, private, local model that’s slightly less capable rather than a slower, cloud-hosted one that leaks my data and costs me per-token fees.

Practical guide: distill an LLM for your laptop

Below is a condensed roadmap I use when distilling models to run locally. It assumes some familiarity with Python, PyTorch/Transformers, and access to a decent GPU for training (distillation training is cheaper than training from scratch but still benefits from GPU acceleration).

Pick a teacher (e.g., Llama 2 13B, OPT, or other open models) and a student size that fits your laptop. Common student sizes are 1.3B, 2.7B and 7B parameters depending on RAM/VRAM. Hugging Face hosts many checkpoints and conversion scripts.

Sequence distillation is easiest: prompt the teacher with diverse prompts and collect outputs. Mix in your own domain-specific prompts. I typically generate 1–5 million tokens for small students; quality matters more than raw size. Use instruction and conversational prompts if you want assistant-like behavior.

Use teacher-generated outputs as targets and standard cross-entropy or KL-divergence losses. Optionally add a loss that aligns hidden states (feature distillation). Training recipes from projects like DistilBERT or TinyLlama offer helpful defaults.

After training, apply quantization. Tools: Hugging Face bitsandbytes (for 8-bit), llama.cpp/ggml for CPU-friendly lower-bit formats, or ONNX quantization. I often convert to 4-bit using QLoRA-style toolchains for a good speed/quality balance.

On CPU laptops, llama.cpp (GGML) or GGUF formats are great. For Apple Silicon, try alpaca.cpp or Ollama-style runtimes that use Apple’s ML accelerators. On Windows/Linux with x86 CPUs, build and use ggml/llama.cpp with multithreading. For integrated GPUs (Intel/AMD), ONNX + OpenVINO sometimes offers speedups.

Measure latency, memory, and task accuracy. Compare against the teacher on a validation set. Adjust data, student size, and distillation loss weights until you hit your desired balance.

Helpful tools and projects I use

These open-source projects have saved me days of setup:

Commands and snippets (illustrative)

Here are conceptual commands I use; replace model names and paths for your setup. These are illustrative — check project docs for exact flags.

When to avoid distillation

Distillation is not always the right tool. If you need absolute state-of-the-art reasoning or access to up-to-the-minute knowledge, a larger cloud model may be necessary. Also, if you don’t have any GPU access for the distillation training phase and the teacher is huge, the project cost might not be worth it compared to using an API for occasional heavy queries.

That said, for 90% of everyday producer-level tasks — summarization, drafting, code assistance, personal assistant affordances — a well-distilled 3B–7B model running locally is transformative: low latency, lower cost, and much better privacy.

If you want, I can share a tailored checklist for your laptop specs (RAM, CPU/GPU, OS) and suggest a concrete teacher/student pair and exact commands to get you started. I’ve distilled a handful of models myself and can point you to scripts and datasets I used — that often cuts setup time from weeks to an afternoon.