Model distillation for LLMs: A practical guide to smaller, faster AI

Your LLM performs brilliantly on benchmarks. Then you check the inference bill. Frontier models like GPT-5 cost materially more per token than smaller tiers like GPT-5-mini or GPT-5-nano, and that adds up fast when you're processing millions of requests. Those costs scale linearly with usage while your revenue probably doesn't.

Model distillation offers a promising approach for compressing large language models. Through knowledge transfer from a "teacher" model to a smaller "student" model, distillation can maintain much of the original model's accuracy. DistilBERT, for example, achieves notable size reduction and faster inference compared to its larger counterparts. The result? LLM apps that cost less to run, respond faster, and can even deploy on edge devices while preserving most of the capabilities your users expect.

This guide covers how distillation works in practice, when to use it versus other optimization techniques like quantization and pruning, and how to combine these approaches with infrastructure-level caching for maximum efficiency.

What is model distillation for LLMs & why does it matter?

Model distillation is a compression technique where a large, capable model teaches a smaller model to mimic its behavior. The "soft" probability distributions from a teacher model contain richer information than simple correct/incorrect labels.

Here's the intuition behind why distillation works: when a large model predicts the next token, it doesn't just output "Paris." It produces a probability distribution. Maybe 92% "Paris," 5% "Lyon," 3% "France." That 5% confidence in "Lyon" tells the student model something important about geographic alternatives that a hard label would never reveal. These "soft targets" often provide richer information per training example than hard labels alone.

The production challenge comes down to computational costs: deploying a full ensemble of models may not be cost-effective for large-scale production, so optimizing with a carefully selected subset of models is often more efficient. Distillation lets you compress that knowledge into a single, deployable model.

Why ML engineers care

These efficiency gains matter for real-world deployments:

• Inference speed: Distilled models typically run faster by reducing billions of parameters to millions, though actual speedups depend on architecture and hardware.
• Memory footprint: TinyBERT-4 achieves ~13.3% of BERT-base parameters, representing an 86.7% reduction in this specific case.
• Cost reduction: Smaller models generally mean reduced GPU requirements and decreased energy consumption.
• Maintained performance: DistilBERT achieves 97% accuracy retention while being 40% smaller.

Understanding these benefits helps explain the practical workflow behind distillation.

How does LLM model distillation work in practice?

Distillation is often described with a general two-stage idea (training a teacher, then training a student with the teacher's guidance), but in practice workflows and step order vary substantially across methods and domains.

Step 1: Select your teacher model

Start with a pre-trained large model that performs well on your target task. This model typically stays frozen during distillation, meaning its weights don't change.

Step 2: Design the student architecture

Your student model needs to be notably smaller than the teacher model, which typically results in fewer parameters and faster inference, while still retaining the capacity to learn effectively from the teacher.

Step 3: Generate soft labels

Run your training data through the teacher model to get probability distributions (soft labels) rather than just hard predictions.

Step 4: Train with combined loss

The student trains on a combined loss: Total Loss = α × Distillation Loss + (1-α) × Student Loss. The distillation loss measures how closely the student's predictions match the teacher's soft probability distributions. Alpha balances how much the student learns from the teacher versus the ground truth labels. A "temperature" parameter softens the probability distributions to reveal the teacher's confidence levels across alternatives—higher temperatures have been explored (Hinton's original work used temperatures as high as 20) to extract more information from the teacher's outputs.

Step 5: Validate performance

Compare teacher accuracy (upper bound), student accuracy without distillation (baseline), and student accuracy with distillation (result). Success generally means the distilled student outperforms one trained without teacher guidance.

When should you use model distillation?

Model distillation serves different purposes than other optimization techniques. Here's how teams typically think about choosing each approach:

Quantization tends to fit when

• Fast deployment optimization is needed
• Reducing memory usage is important
• Multi-fold speedups with modest quality loss are acceptable

Pruning tends to fit when

• Reducing floating point operations (FLOPs) is a consideration
• Achieving 50-60% sparsity with minimal accuracy loss is acceptable, especially for large models
• Integrating with other compression techniques like knowledge distillation and quantization

Knowledge distillation tends to fit when

• Capability preservation matters most during compression
• You can invest in retraining for long-term inference benefits
• You need optimized model performance while maintaining comparable capabilities

The P-KD-Q sequence tends to fit when

• High compression with quality preservation is desired
• You're targeting maximum compression through combined methods

In practice, most teams experiment with multiple approaches. The right choice depends on your latency requirements, deployment constraints, and how much accuracy loss you can tolerate.

Practical decision framework

These use cases inform a broader framework for choosing between techniques. Distillation isn't always the right choice.

Avoid distillation when

Your teacher model isn't performing well enough on the target task—you can't transfer knowledge the teacher doesn't have. Similarly, if you lack adequate training data for the distillation process, the student model is unlikely to learn effectively. And if your deployment constraints don't actually require a smaller model, the training investment may not pay off.

Deployment target matters

Quantization in cloud deployments can reduce resource consumption and operational costs, often working best alongside other techniques like pruning or distillation. Edge and mobile deployments typically benefit most from distillation, as the large size reduction makes on-device inference feasible. Internet of Things (IoT) and embedded systems often use knowledge distillation combined with other techniques to manage strict memory constraints.

Investment considerations

Quantization can often be implemented relatively quickly, while distillation typically requires substantial retraining time. This upfront cost tends to make sense when you're deploying at scale and the long-term inference savings compound meaningfully, or when deployment constraints require smaller models that quantization alone can't achieve.

How model distillation compares to other optimization strategies

Each optimization technique offers different tradeoffs:

• Quantization reduces numerical precision (e.g., FP16 to INT4), often resulting in large memory savings and multi-fold inference speedups, though results vary by model, method, and hardware. One study on agent-style benchmarks observed 10-15% drops in real-world task success with 4-bit quantization, even when other metrics degraded less.
• Pruning removes unnecessary parameters. Pruning can reduce parameter count and, with structured pruning or sparse kernels, can reduce compute—but speedups depend on how sparsity is represented and executed.
• Distillation creates smaller models through knowledge transfer, often combining with other compression techniques like pruning for retraining.

A 2025 study on compression ordering found that Pruning → Distillation → Quantization (P-KD-Q) performed best among the sequences they tested, with the authors noting it "yields the best balance" for compression with preserved capabilities.

Why does P-KD-Q work

The sequence matters because each technique builds on the previous one. Pruning first removes redundant parameters and establishes the model's structural foundation. Distillation then retrains the pruned architecture to recover capabilities and optimize remaining parameters—this knowledge preservation step helps maintain quality. Quantization applies last to the already-optimized architecture without interfering with structural changes. Stacking techniques can compound compression, but the exact ratio depends on what you prune, the student architecture you choose, and the quantization format. The same compression ordering study found that sequences quantizing before distillation saw perplexity jump by an order of magnitude compared to sequences quantizing last.

How model distillation improves real-world LLM app speed & UX

DistilBERT has been widely adopted. The model is 40% smaller in terms of parameters than BERT-base, offers 60% faster inference under specific conditions, and retains about 97% of BERT's accuracy on the GLUE benchmark.

TinyBERT pushed these boundaries further: TinyBERT-4 achieved about 28% of the parameters and 31% of the inference time compared to distillation baselines like BERT4-PKD and DistilBERT4. Compared to BERT-Base itself, TinyBERT-4 used about 13.3% of the parameters and 10.6% of the inference time. TinyBERT-6 performed on-par with BERT-Base on the GLUE benchmark.

Production deployment scenarios

These improvements translate directly to specific production scenarios:

• Real-time chat apps need response latency low enough to feel fluid.
• Autocomplete features in search and code editors need even faster inference, where every keystroke triggers a prediction.
• Document processing pipelines benefit from throughput gains when processing thousands of documents, where 3× speedup means processing the same workload with one-third the compute cost.

TinyBERT's smaller parameter count and faster inference make it more practical for edge and mobile scenarios than full BERT, especially where memory and compute are constrained. On-device processing means lower latency compared to cloud round-trips, reduced bandwidth costs, and functionality that works offline. For privacy-sensitive apps, keeping inference on-device can reduce data transmission concerns, though app architecture determines overall privacy characteristics.

Recent advances in distillation techniques

The field has evolved notably from early task-specific approaches to general-purpose compression strategies. Recent research has shown that some distillation methods can achieve effective knowledge transfer using less than 3% of the original training data, a dramatic reduction compared to earlier approaches that required much larger retraining datasets.

Recent work on compression ordering suggests the P-KD-Q sequence (Pruning → Knowledge Distillation → Quantization) can achieve strong compression with preserved quality. As inference costs grow, more teams are exploring combinations of compression techniques and infrastructure optimizations. Reducing inference costs has become increasingly important for production AI economics, especially at scale—and infrastructure-level optimizations like semantic caching complement model-level compression.

Build your optimized LLM stack

Model distillation optimizes the model itself, but production LLM apps need infrastructure that maximizes those gains. Even a distilled model wastes compute if it's answering the same semantically similar queries repeatedly. Redis complements distillation by adding a layer before your model: semantic caching recognizes when incoming queries match previous ones in meaning, serving cached responses without invoking inference at all.

Think of LLM optimization as three layers working together: Semantic caching reduces redundant LLM calls for semantically similar queries. Vector search retrieves relevant context in retrieval-augmented generation (RAG) implementations. And distilled models handle the actual inference when cache misses occur, providing faster and more cost-effective performance than full-size models.

Redis provides built-in vector search with Hierarchical Navigable Small World (HNSW) and FLAT index types. RedisVL gives you a SemanticCache interface you can run on Redis yourself. If you want a managed semantic caching service, Redis LangCache provides that via API (currently in preview). In a billion-scale benchmark, Redis reported 90% precision with 200ms median latency when retrieving the top 100 nearest neighbors under 50 concurrent queries.

For ML engineers building production LLM apps: distill your models for efficient execution, prune and quantize for additional compression, then deploy behind infrastructure that caches semantically similar queries. Your distilled models perform even better when they're invoked less frequently for genuinely new queries.

Ready to see how infrastructure optimization complements your model optimization strategy? Try Redis free to experiment with semantic caching and vector search capabilities, or talk to our team about architecting your LLM deployment stack.