Large Language Models (LLMs) are transforming enterprise workflows and unlocking opportunities in customer support, content generation, data analysis, and beyond. These models’ versatility—handling everything from summarization to API orchestration—makes them indispensable for modern businesses.
But there’s a catch: LLMs, particularly the largest and most advanced ones, are resource-intensive. Their costs, latency, and sometimes excessive generalization can become roadblocks for enterprise adoption.
Enter LLM distillation, a powerful technique that helps enterprises balance performance, cost efficiency, and task-specific optimization. By distilling large frontier LLMs like Llama 3.1 405B into smaller, task-optimized models, enterprises can reduce costs, improve performance, and align models with specific goals.
In this article, we’ll explore the challenges enterprises face with LLMs, how distillation solves them, and why it’s a must-have in your AI workflow toolkit.
For all their potential, deploying large LLMs in enterprise settings comes with significant challenges.
Large LLMs, particularly frontier models, are expensive to operate. Whether you’re paying per-token fees for hosted models or investing in high-performance GPUs for self-hosted deployments, the costs add up quickly—especially at scale.
A full-sized Llama 3.1 405B instance requires more than 800GB of GPU memory just to host the model for inference, and those memory requirements rise as you consider increased context lengths and other support infrastructure. The 8B variety, in contrast, requires just 16GB of GPU memory for inference and demands far less hardware for its extended context windows. Depending on the support setup, that means that the 8B variety could run for as little as 2% of the cost of the full-sized variety.
With size comes complexity. Larger models require significantly more computational resources, leading to longer response times. For use cases like customer support or real-time analytics, these delays can degrade user experiences.
According to research from Artificial Analysis, the 8B variety of Llama 3.1 outputs tokens nearly six times faster than the full-sized version of the model; Llama 3.2 1B beats Llama 3.1 405B’s output speed by almost 20x.
Frontier large language models are designed to excel at general-purpose tasks, from creative writing to complex reasoning. However, most enterprise applications require specialization; a customer support bot doesn’t need to write code or solve complex equations—it needs to reason effectively about customer queries and provide accurate answers.
LLM distillation is the process of compressing task-specific capabilities of a large, general-purpose “teacher” model into a smaller, more efficient “student” model. By building on the smaller foundation model, a business can optimize its AI workflows for specific goals without incurring the full costs or complexity of running massive models.
An off-the-shelf 405-billion parameter model will outperform its 7-billion or 1-billion parameter cousins on every task. By distilling a frontier LLM’s performance on a specific task, the smaller LLM will “forget” many others. It will closely mirror (or exceed!) the larger LLM’s performance on the target task, but it will no longer generate competent computer code or witty sonnets.
Properly deployed and guardrailed, this doesn’t present a problem. Snorkel AI’s experts and those at other firms have found that 7-billion parameter LLMs typically have sufficient capacity to handle an enterprise’s targeted tasks. However, enterprise data leaders should keep in mind that distilled LLMs will underperform if deployed to handle tasks outside of their original training target.
Distillation can improve enterprise AI workflows by packaging a frontier LLM’s state-of-the-art performance into a smaller LLM format. Enterprise data science teams can use the largest available LLMs to generate training data to fine-tune a chosen smaller LLM. This process retains the larger model’s capabilities on the user’s targeted specialty while minimizing computational requirements.
Here’s roughly what that process looks like.
Frontier models can serve as versatile data-generation engines. Through highly engineered prompts, enterprises can guide these models to produce high-quality, domain-specific training data. They can, for example, simulate customer service conversations to train a query-answering bot or generate summaries of technical documents for internal knowledge systems.
Data scientists can extract the “teacher” LLM’s knowledge in two different ways:
Soft targets can yield better results but require more work on the part of the data scientist. They also may not always be an option; some frontier LLM providers (most notably OpenAI) don’t offer visibility into their token probability layer.
Regardless of the data scientists’ approach, prompting the chosen frontier model in a targeted way should yield a wealth of training data to “teach” the smaller LLM through fine-tuning.
In this phase, data scientists use the prompts submitted to the frontier model as well as its outputs (whether hard or soft targets) to fine-tune a smaller LLM. Done properly, this should result in a small model that delivers performance comparable to the teacher model for the desired use case—at a fraction of the cost.
To maximize the benefits of distillation, enterprises should adopt best practices for data generation and compression.
High-quality prompts are essential for extracting task-specific data from frontier LLMs. While data scientists can extract acceptable training pairs from well-crafted one-shot questions, they can improve results by engineering and deconstructing more advanced prompts.
For example, a data scientist could give the frontier model a few-shot learning prompt—one that offers a few examples of potential queries and correct responses before giving the targeted query. This generally yields a higher-quality response. When transferring this prompt/response pair to the smaller model, the data scientist can remove the few-shot portion, leaving only the targeted query and the high-quality response. Fine-tuned on hundreds or thousands of these examples, the smaller LLM learns to yield high-quality responses without the few-shot examples. In this way, the distilled model’s one-shot performance can exceed that of the larger model.
Data scientists can further improve this process by injecting internal documentation into their highly-engineered prompts. This can instill organization-specific understanding into the smaller model, which is not present in the frontier model.
Using direct distillation—even with highly engineered prompts and injected business knowledge—will only get you so far. At best, it will compress the optimal performance of the teacher model into a smaller format, which may not meet your organization’s production needs.
In a Snorkel case study, Google’s PaLM 2 model achieved a baseline F1 score of 50 on a high-cardinality classification problem. Advanced prompting elevated that score to 69, still short of the production threshold of 85. To further boost the F1 score, our engineer used programmatic labeling in the Snorkel Flow AI data development platform.
Snorkel Flow enables data scientists to collaborate with SMEs to approximate SME judgment in a scalable way—either through a response quality model or an LLM-as-judge.
In an LLM-as-judge setup, the team develops a template that prompts an LLM to rate responses as good or bad. They iterate on that template until the LLM agrees with SMEs most of the time. In the response quality route, SMEs hand-label a small amount of data and document the logic behind their labels. Data scientists then encode this logic into labeling functions that apply to thousands of examples instantly. This allows the team to train a small neural network to mimic SME intuition.
Both approaches allow Snorkel Flow users to filter frontier model responses to only the best training examples. This can drive the student model’s performance to exceed that of the teacher.
In Snorkel Flow’s LLM evaluation suite, this SME proxy becomes an enduring asset. After fine-tuning the student LLM on filtered training data, the SME proxy assesses the quality of the distilled model’s output according to targeted data slices. This highlights where the distilled model needs further refinement, helping the team target their next round of iterative data development.
While distillation is powerful, data scientists focused on edge applications can compress models further. Techniques like pruning (removing less impactful parameters) and quantization (lowering precision) reduce model size and resource consumption—though at some penalty to performance.
While fine-tuning a frontier model is possible, the costs and complexity can be prohibitive. By distilling to a smaller LLM, enterprises achieve a cost-effective balance of performance, scalability, and task-specificity.
While this post has focused on distilling large LLMs into smaller LLMs, data scientists can also leverage frontier LLMs to create training data for simpler models like a small neural network or even logistic regression. This approach is helpful in simple classification applications where a frontier LLM performs well, but using it at high volume would be prohibitively expensive.
LLM distillation offers enterprises the best of both worlds: the power of advanced language models and the efficiency of smaller, optimized systems.
By pairing techniques like prompt-engineered data generation with distillation, businesses can achieve remarkable outcomes—faster responses, lower costs, and task-specific excellence.
As the AI landscape evolves, enterprises must embrace innovative workflows like distillation to stay competitive.
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