Model distillation with compute: How to set it up
Model distillation with compute: How to set it up
Model distillation is a practical way to shrink large models into efficient versions that run faster and cost less. As parameter counts climb into the billions, model distillation LLM makes it possible to cut GPU memory use, speed inference and simplify deployment. In this blog we’ll explain how the method works, why GPU compute matters, and what to keep in mind when moving from research models to production systems.
What is model distillation?
Model distillation is the process of training a smaller student model to mimic a larger teacher model, typically by learning from the teacher’s soft outputs (probability distributions) rather than hard labels.
This process significantly reduces memory usage, infrastructure costs and makes models faster — all without sacrificing accuracy. It’s a pragmatic technique for teams deploying specialized models in real world systems.
Why do we need to distill large models?
State of the art LLMs (for example, 175 billion parameter models) are both expensive to run and resource intensive, requiring hundreds of gigabytes of GPU memory.
By contrast, distillation allows teams to deploy smaller models tailored to specific tasks, offering lower latency during deployment and cost-effective scaling.
How do GPUs make distillation more effective?
Distillation involves running both teacher and student models through enormous datasets, with repeated forward and backward passes.
Using GPU accelerated model distillation LLM hardware dramatically shortens training times and improves convergence and accuracy compared to CPU workflows.
Where TensorRT-based optimziers are used, inference speeds up teacher output generation and GPU acceleration shortens training time. Final accuracy and convergence depend on the training setup, not the hardware alone.
Why is this important for teams deploying large models?
GPU-powered distillation bridges research-grade models and production-ready systems, all while retaining most of the teacher’s performance. The process:
- Unlocks real-time inference
- Minimizes GPU memory footprint
- Reduces operational costs
- Supports deployment on constrained hardware
The concept behind model distillation
Model distillation is the process of transferring knowledge from a large, high-capacity teacher model to a smaller student model.
Instead of training only on ground-truth labels — that is, the accurate, human-labeled representation data used to train and evaluate AI models — the student learns from the teacher’s soft outputs.
Soft outputs are the probability distributions across classes, and these distributions encode richer information than hard labels, including relative confidence levels and relationships between classes.
By mimicking soft outputs, the student learns not only the correct answers, but also the teacher’s reasoning patterns. This approach enables smaller models to approximate the performance of their larger counterparts while being faster and lighter.
Why AI model distillation exists
LLMs achieve state-of-the-art results, but can present major deployment challenges such as:
- Slow inference
- High GPU memory requirements
- Significant energy and cost overheads
Running a large parameter model at scale can demand hundreds of gigabytes of memory and clusters of GPUs.
Model distillation addresses this by compressing the teacher’s knowledge into a compact student model that is faster, cheaper and easier to deploy. The distilled model maintains most of the accuracy — while dramatically lowering latency and resource consumption — making advanced AI practical for busy production environments or where organizations need to save money.
How model distillation works
Model distillation compresses a large teacher model into a smaller student model by transferring knowledge from one to the other.
Rather than learning only from ground-truth labels, the student also learns from the teacher’s soft outputs — that is, the probability distributions across predictions.
These outputs contain richer signals about relationships between classes and model confidence. By imitating them, the student can capture the teacher’s reasoning patterns and the result is a compact model that is faster and lighter, but still retains much of the teacher’s performance.
Training setup
In a standard distillation pipeline, the teacher model generates outputs on a dataset, producing probability distributions rather than final labels. The student model is then trained to match these outputs as closely as possible.
Many implementations also use temperature scaling — which adjusts the randomness of a model’s output — to soften the teacher’s predictions, in turn highlighting relationships between classes that may otherwise be hidden.
Training typically combines two processes: one comparing student predictions to ground-truth labels and another aligning them with the teacher’s outputs. This blended objective ensures the student learns both accuracy and generalization behavior.
Practical example in NLP
A well-known application of distillation is in natural language processing.
Models such as BERT or GPT achieve excellent performance, but are prohibitively large for many deployment scenarios. Through distillation, these models can be compressed into smaller variants — such as DistilBERT, which retains about 95% of BERT’s language understanding capability while being 40% smaller and 60% faster.
This makes the student model more efficient to serve in production, enabling low-latency inference for tasks such as search, chatbots and classification. By distilling knowledge, teams gain models that are not only practical but also competitive in accuracy.
Why GPU compute matters for distillation
Although distillation reduces model size for deployment, the model training process itself is compute-intensive. Both teacher and student models must process large datasets repeatedly, involving millions of forward and backward passes.
Model distillation LLM GPUs are designed to accelerate these operations, drastically cutting training time compared to CPUs. For ML engineers and infrastructure teams, GPU compute transforms distillation from a lengthy, resource-heavy task into a practical, repeatable workflow — making it possible to efficiently compress large models into smaller ones suitable for real-world deployment.
Acceleration of training loops
Distillation requires significant training effort. Even though the outcome is a smaller student model, the process involves repeated forward and backward passes on large datasets.
This makes GPUs essential: their massively parallel architecture speeds up matrix multiplications, enabling higher throughput and shorter training cycles. Instead of weeks on CPU clusters, GPU acceleration can reduce distillation time to days or even hours.
Faster loops mean engineers can experiment more quickly with architectures, loss weighting or temperature settings, ultimately producing better student models.
Efficiency with large teachers
When the teacher model itself is large, the computational challenge begins before training as generating outputs for distillation requires extensive inference.
Running these models repeatedly is prohibitively slow on CPUs, but GPUs address this bottleneck through parallelism, high memory bandwidth and optimized tensor operations.
This allows the teacher to efficiently produce soft targets for the student across massive datasets. Without GPU acceleration, the cost and time of generating this training data would make distillation impractical for models at foundation scale.
You can read more about inference optimization techniques and solutions in Nebius.
Multi-GPU setups
For organizations distilling large models or conducting distillation at enterprise scale, a single GPU is rarely sufficient.
Multi-GPU training allows workloads to be distributed across devices, enabling larger batch sizes, faster processing and the ability to handle teacher models that exceed single-GPU memory limits.
Solutions such as those provided by Nebius provide infrastructure for distributed distillation, ensuring synchronization across GPUs. This setup shortens training times and makes large-scale distillation feasible, giving teams the ability to compress massive models into deployable forms without overwhelming infrastructure resources.
Use cases of model distillation in LLMs
Model distillation LLM is especially compelling in real deployments, where memory, latency and cost are limiting. This comparison table shows how distilled student models can excel in real-world applications.
| Large teacher model (full LLM) | Distilled student model (smaller LLM) | |
|---|---|---|
| Accuracy | Highest accuracy, captures subtle nuances | Slightly lower, but sufficient for many production tasks. |
| Latency | High inference latency, especially at scale | Low latency, enabling near real-time responses |
| Memory footprint | Requires hundreds of GBs of GPU memory | Fits on smaller GPU clusters or even edge devices |
| Compute cost | Expensive to train and run, high energy consumption | Lower operational cost, efficient use of GPU resources |
| Deployment fit | Suited to research, experimentation and complex reasoning | Ideal for production apps, APIs, mobile or edge and cost-sensitive use cases |
| Scalability | Limited by infrastructure and budget | Easier to scale across workloads and user bases |
Deploying LLMs in production
Deploying full-scale LLMs directly into production is challenging: they demand high GPU memory, long inference times and significant energy usage.
Distillation mitigates these barriers by reducing the model footprint while preserving strong performance.
A distilled LLM offers lower latency, higher throughput and more predictable scaling across workloads. This efficiency allows organizations to integrate language capabilities into applications such as chatbots, summarization services or semantic search without prohibitive infrastructure costs. In production settings where user experience, speed and cost all matter, distillation enables advanced AI to operate at enterprise scale.
Edge AI and mobile deployment
Edge and mobile environments face strict limitations in memory, compute and power. Running a 100 billion-parameter LLM on such hardware is unrealistic.
Distillation addresses this by compressing teacher knowledge into smaller students that can run locally. This enables applications such as offline translation, voice assistants and document summarization without constant cloud connectivity.
Distilled models improve latency and increase privacy by keeping inference on-device and reduce reliance on external infrastructure. By lowering resource requirements, distillation unlocks LLM functionality in constrained environments, making advanced AI feasible for mobile and Internet of Things (IoT) applications where efficiency is critical.
Budget-conscious inference at scale
Serving foundation-scale LLMs via APIs or cloud platforms is expensive, as every inference consumes significant GPU resources. At scale, these costs quickly become unsustainable.
Distillation allows providers to deploy smaller, faster models that maintain competitive accuracy while dramatically reducing GPU hours per request. This translates into lower operational costs and more affordable offerings for customers.
For platforms supporting millions of daily calls, the savings are substantial. Distilled models play a critical role in making large-scale AI delivery cost-effective, ensuring that powerful LLMs can be served reliably and sustainably.
Benefits of AI model distillation
Beyond making large models deployable, distillation brings tangible benefits that improve both performance and efficiency.
By compressing the teacher into a smaller student, teams gain faster inference, reduced memory and energy usage and lower operating costs. These benefits matter at every level — from enterprises running production pipelines to edge devices with strict hardware constraints.
Distillation ensures that advanced language capabilities are not locked behind massive infrastructure requirements, but can instead be delivered in a practical, scalable and cost-conscious way.
Faster inference
A key benefit of distillation is reduced inference time. Because student models are smaller, they can process inputs with significantly less latency. This enables real-time responsiveness in applications such as conversational AI, recommendation systems or semantic search, where delays directly impact user experience.
Smaller models also support higher throughput, allowing a single GPU to serve more requests in parallel compared to a foundation-scale teacher. For ML engineers deploying at scale, these performance gains make it feasible to deliver advanced AI services that are both fast and reliable.
Lower resource consumption
Distilled models require fewer parameters, which translates into lower GPU memory use and reduced computational overhead. This efficiency minimizes energy draw and makes it possible to run language models in environments where hardware and power are limited, such as mobile devices or IoT endpoints.
At scale, the resource savings extend to cloud deployments, reducing overall infrastructure load and improving sustainability. Whether running locally on constrained devices or across data centers serving millions of queries, distilled models achieve competitive performance without overwhelming compute, memory or energy budgets.
Cost reduction
Operating large models is expensive because every inference consumes GPU cycles, memory bandwidth and power. For commercial LLM APIs or enterprise systems handling millions of requests, these costs quickly add up.
Distillation reduces the per-query footprint by shrinking models while maintaining accuracy. Fewer GPU hours are needed, lowering cloud costs and allowing providers to scale more predictably. This cost efficiency makes advanced AI economically sustainable, whether delivered as a cloud service or deployed in-house.
Distilled models are not only a technical optimization — they can also be a financial necessity.
When to use distillation in your ML workflow
Distillation is most effective when strategically applied within the model development lifecycle. While its purpose is consistent — compressing a large teacher into a smaller, more efficient student — the timing can vary depending on the goal.
Teams may distill after training to preserve performance, before deployment to simplify operations or as a replacement strategy for oversized models. Choosing the right moment ensures maximum efficiency, cost savings and practical usability, without compromising the accuracy or reliability needed for real-world applications.
After pretraining or fine-tuning
Distillation is commonly performed once a model has been pretrained or fine-tuned. At this point, the teacher has captured the necessary knowledge and task-specific patterns, making it an ideal candidate for transferring insights to a student.
By distilling after full training, teams can create smaller models that retain strong performance while being faster and cheaper to run. This approach ensures that the computational investment in pretraining or fine-tuning continues to pay dividends during deployment.
Before deployment or open-sourcing
Distillation is often applied before releasing models to production environments or publishing them for broader use. Large teacher models may be impractical for real-time inference or too costly for customers to operate.
By distilling first, organizations provide models that balance accuracy with accessibility. Smaller students are easier to integrate into production pipelines, deploy on constrained hardware or share through open-source repositories. This step not only improves efficiency but also ensures broader adoption by lowering the infrastructure burden on end users.
For replacing overly large models
In many cases, teams begin with foundation-scale models that are too large for their intended applications. Distillation offers a structured way to compress these general-purpose models into smaller, task-specific students.
For example, instead of serving a massive LLM directly, an organization might distill it into a focused model optimized for classification, summarization or conversational tasks. The resulting student maintains competitive accuracy while being faster and more resource-efficient. This replacement strategy allows enterprises to scale advanced AI features without carrying the operational costs of overly large teacher models.
Conclusion
Model distillation is a practical and powerful method for making large-scale AI systems more efficient.
By transferring the knowledge of a high-capacity teacher into a smaller student, teams can achieve faster inference, lower memory usage and reduced operational costs — all without sacrificing significant performance.
Yet distillation itself is computationally intensive, particularly when working with foundation-scale models.
This is where GPU compute proves essential. GPUs accelerate the forward and backward passes needed for training, streamline the generation of teacher outputs and make multi-GPU scaling possible for large workloads. For ML engineers, AI researchers and infrastructure teams, GPU-backed distillation is not just an optimization — it’s an essential step for deploying advanced AI at scale.
If your goal is to deliver high-performing models that are practical, accessible and sustainable in production, incorporating distillation workflows into your pipelines and leveraging GPU infrastructure should be considered essential steps. Chat with us today to find the right solution for your business.
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