IntroductionIntroduction

As artificial intelligence continues to advance, large language models like GPT-4 and BERT have demonstrated remarkable capabilities. However, teams deploying these models face real challenges like high GPU costs, long inference times and heavy memory requirements.

Model distillation offers a practical way to address these problems. It involves transferring the knowledge from a large, complex “teacher” model to a smaller, more efficient “student” model. This process enables the student model to perform tasks with comparable accuracy while being more resource-efficient.

A practical example comes from Walmart Global Tech, where researchers distilled a large interaction-based ranking model into a smaller DistilBERT student model for e-commerce search. The distilled model not only improved relevance metrics but also boosted real-world outcomes. This shows how distillation can deliver both efficiency and measurable business value.

GPUs speed up model training, cutting both time and computing power. This is especially useful with large teacher models, since it lets you train data faster and fine-tune more quickly.

This article discusses the importance of distilling models using GPU compute and facilitates the deployment of advanced AI systems in practical scenarios.

What is model distillationWhat is model distillation

Model distillation, also known as knowledge distillation, is a technique in which a smaller student model learns from a large teacher model’s outputs, instead of learning directly from raw data labels. These outputs include probability scores for each possible prediction, which contain more information than just the correct answer.

The technique was introduced by Geoffrey Hinton in 2015 in his work “Distilling the Knowledge in a Neural Network”. The main goal of model distillation is to create efficient models that maintain strong performance while being faster and less costly to run, which is especially important for deploying models in real-world environments.

This process is valuable for LLMs that can have millions or even billions of parameters. Companies can deploy AI systems in real-time applications and resource-constrained environments by distilling them without sacrificing performance.

General termsGeneral terms

Model distillation transfers knowledge from a teacher to a student model. The student mimics the teacher’s outputs, learning from probability distributions (soft targets) rather than just correct labels (hard targets).

For example, instead of copying hard labels like “cat” or “dog, ” the student sees probability distributions such as 80% cat, 15% dog, 5% fox. This “soft” information carries richer signals, allowing the student to approximate the teacher’s decision process more effectively.

Why AI model distillation existsWhy AI model distillation exists

The gap between training and deployment is a major challenge. Large models are trained on powerful, specialized hardware, but they often need to be deployed in environments with limited resources. Other deployment challenges include:

• High memory requirements: Running large models requires significant RAM or GPU memory. For example, serving a 175B parameter LLM can take over 350GB of GPU memory.

• Slow inference: Large models are slow to respond, making real-time applications difficult.

• High operational cost: Running these models continuously requires substantial energy and computing resources.

Model distillation helps by transferring the complex “knowledge” of the large model into a smaller and more manageable package. This compression makes high-performing AI models more accessible and useful in production, while reducing both infrastructure demands and operational expenses.

How model distillation worksHow model distillation works

The process of model distillation involves creating a teacher-generated dataset, where the teacher model produces outputs for a wide range of inputs. These outputs are also called soft targets and they contain rich information about the decision-making patterns and confidence levels of the teacher across classes. The student model then learns from these outputs and absorbs the teacher’s knowledge without requiring the full training data or model size.

Training setupTraining setup

The student is trained using temperature scaling, which softens the teacher’s output probabilities and makes them easier to learn from. Training usually combines two losses:

• Hard loss: Measures the difference between the student’s predictions and the true labels.

• Soft loss: Measures the difference between the student’s predictions and the teacher’s softened outputs.

The student model learns to approximate the teacher’s behavior while being more efficient to minimize the weighted sum of these losses.

Practical example in NLPPractical example in NLP

A well-known example is DistilBERT, which is a smaller version of BERT. The teacher model (BERT) generates soft targets for a large text corpus. DistilBERT is trained on this dataset, learning to predict text with similar accuracy while being 40% smaller and 60% faster.

Similarly, large GPT models can be distilled into compact versions for tasks like text generation, summarization or question answering. These distilled models retain most of the teacher’s performance but require less memory and computation. This makes them more practical for production and edge deployment.

Why GPU compute matters for distillationWhy GPU compute matters for distillation

GPU compute is crucial for model distillation because even though the goal is a smaller model, the process still involves considerable computation. Using GPUs makes training faster and more efficient, which is essential for teams working with large models.

Acceleration of training loopsAcceleration of training loops

Even with smaller student models, training still involves many forward and backward passes through the network. GPUs are designed for parallel processing that can dramatically reduce the time required for these operations. This means that distillation that might take weeks on a CPU can often be completed in days or hours on a GPU. This results in faster experimentation and iteration.

Efficiency with large teachersEfficiency with large teachers

For extremely large LLMs like GPT-3 or LLaMa 4, generating the teacher’s outputs for the training dataset alone can be computationally expensive. GPUs handle these large-scale inference tasks efficiently. It enables the student model to learn from rich and high-dimensional outputs without bottlenecks. This parallelism can help maintain speed and reduce operational costs.

Multi-GPU setupsMulti-GPU setups

A single GPU may not be efficient for companies working at scale or distilling extensive models. Multi-GPU setups allow the workload to be distributed across multiple devices. This ensures faster training and better memory management. This approach is useful for enterprise-scale projects or when multiple student models are being trained simultaneously.

Use cases of model distillation in LLMsUse cases of model distillation in LLMs

Model distillation has many practical applications, especially for LLMs. Understanding these real-world use cases can help highlight why distillation is important for making AI models more accessible and efficient.

Model distillation LLM workflows allow developers to deploy large language models in production environments without incurring high latency or excessive compute costs. Smaller models respond faster and use less memory, making them suitable for applications that need real-time or near-real-time responses.

Edge AI and mobile deploymentEdge AI and mobile deployment

Memory and compute are limited in edge devices or mobile apps. Distilled models are small enough to run locally and enable AI features without relying on constant cloud access. This improves speed, reduces latency and boosts user privacy.

Google used distillation techniques in MobileBER, which runs efficiently on smartphones. It enables features like on-device text prediction and voice assistants, which give users AI functionality without heavy reliance on cloud processing.

Budget-conscious inference at scaleBudget-conscious inference at scale

AI platforms and API providers often serve thousands of users simultaneously. Using distilled models reduces GPU usage and energy costs while maintaining acceptable performance. This makes scaling AI services more affordable and sustainable.

Alibaba has applied Privileged Features Distillation (PFD) in its Taobao recommendation system. This helped them achieve measurable gains in click-through and conversion rates. In 2025, Alibaba also released EasyDistill. It is a toolkit for compressing large models for NLP tasks on its PAI platform. While not all details of its LLM deployment are public, these efforts show how distillation improves scalability and reduces GPU costs.

Benefits of AI model distillationBenefits of AI model distillation

Model distillation offers several important advantages that make AI models more practical and effective for various applications. This section highlights the key benefits that teams gain by using distillation to optimize large models.

Faster inferenceFaster inference

Distilled models can typically achieve 2–8x faster inference compared to their teacher models, depending on the architecture and hardware optimization. This efficiency is important for:

• Real-time services like conversational agents, fraud detection pipelines or recommendation engines.
• Batch inference in high-volume environments where reducing per-query latency directly increases throughput.

For engineers, this translates into lower GPU requirements for the same workload or significantly higher query capacity on existing hardware.

Lower resource consumptionLower resource consumption

Distillation can help to lower VRAM usage, memory bandwidth demand and power draw by reducing model parameters. This translates into:

• Running models on edge devices like smartphones, wearables and IoT sensors without cloud offloading.

• More efficient multi-tenant cloud deployments, where compute isolation and utilization matter.

• Better energy efficiency per inference round that align with green AI and data center sustainability initiatives.

Direct cost reductionDirect cost reduction

GPU time is one of the key cost drivers in AI production. Distilled models help by:

• Cutting inference cost per token/query is critical for LLM APIs handling millions of requests.

• Reducing scaling overhead, since fewer GPU clusters are needed to meet latency SLAs.

• Making cloud spend more predictable, as workloads demand fewer elastic GPU bursts.

When to use distillation in your ML workflowWhen to use distillation in your ML workflow

Distillation is most effective when you need to transfer knowledge from a large model to a smaller one without retraining from scratch. The timing depends on your workflow stage and deployment goals.

After pretraining or fine-tuningAfter pretraining or fine-tuning

Distillation is most effective when applied after a model has been fully pretrained or fine-tuned on a specific task. At this stage, the teacher model has already captured rich task-specific knowledge and learned complex data patterns. Transferring this refined knowledge to a smaller student model helps preserve most of teacher’s performance in a compact form.

Studies reveal that distillation after fine-tuning can maintain up to 95% of the teacher’s accuracy, while drastically reducing model size and inference time. This practice ensures that distilled models are task-optimized and suitable for deployment without re-training large models from scratch.

Before deployment or open-sourcingBefore deployment or open-sourcing

Apply distillation before model release to reduce serving costs and meet hardware limits. Smaller models consume less memory, start faster and scale better in multi-tenant environments. For example, in the case of DeepSeek-R1, researchers published distilled versions that proved more efficient on benchmarks to give developers ready-to-deploy models without the overhead of full-size infrastructure.

For replacing overly large modelsFor replacing overly large models

Use distillation when a general-purpose model is overkill for production. Instead of hosting a massive LLM for a narrow task like intent detection, distill its knowledge into a compact task-specific student. This preserves task accuracy while cutting latency and inference costs dramatically.

For example, large GPT-style models can be distilled into compact models designed only for summarization or classification, reducing costs while keeping accuracy.

Wrapping upWrapping up

Model distillation is a vital strategy for making AI more practical and cost-effective. It bridges the gap between the expensive and high-performance models that are used in research and the efficient models for real-world applications.

GPU-backed infrastructure allows teams to run distillation smoothly and quickly while enabling faster innovation and deployment. For machine learning practitioners and teams looking to build efficient and high-performance models, distillation workflows are highly recommended. Combining distillation with powerful GPU resources can deliver advanced AI applications while managing costs and resources effectively.

As model sizes continue to grow, distillation will remain an essential tool for practitioners aiming to balance capability with efficiency. Nebius AI Cloud offers easy-to-use cloud GPU services tailored for AI workloads. It provides scalable and flexible GPU resources on demand, so you can train and distill models without worrying about complex infrastructure.