Distilling... is our Level 2 Game Plan
May 8, 2025
Distilled domain aligned models are the future of enterprise AI. Distilling brings compact models that can power smarter, faster, and scalable AI for every industry. TWO AI is leading this shift with its D3-driven second-layer models that brings best of both worlds - performance and efficiency.
In enterprise AI, one-size-fits-all models fall short. A bank’s AI assistant should understand financial regulations, a retail assistant should navigate product catalogs, and a hospital’s assistant must speak the language of medicine. For years, companies tried to make this work by fine-tuning large language models on domain-specific data. But fine-tuning alone can be expensive, slow, and inefficient to scale across industries.
At TWO AI, we believe the natural evolution for scalable, high-performance enterprise AI lies in a new direction—what we call D3: Distilling, Domain, and Data. This roadmap drives our second-layer model architecture, enabling smaller, smarter models that deliver real-world value across sectors.
Fine-Tuning vs. Distillation – A Simple Difference
Fine-tuning means continuing to train a large model, like LLaMA 4 or SUTRA, on domain-specific data. The model adapts to your industry’s vocabulary and logic, but its size and compute requirements remain the same.
Distillation is a different strategy. Instead of fine-tuning a massive model for every use case, we use it as a teacher to train a smaller model – a student – that learns by mimicking the teacher’s outputs. This process produces a new model that is smaller, faster, and surprisingly effective. Models like DeepSeek R1, Gemma, and SUTRA all support this approach. In fact, recent benchmarks show that well-distilled models can match or even exceed larger models on domain-specific tasks while using fewer resources.
Recent studies, including the one from arXiv:2001.08361 and arXiv:2502.08606, reveal that scaling laws not only govern raw pretraining but also hold remarkably well for distilled models. As shown below, test loss scales predictably with compute, dataset size, and parameters—even in distilled students—enabling accurate forecasts of model performance and guiding efficient training. Notably, with enough distillation data, students can even outperform their teachers.
At TWO AI, we call this Domain-Aligned Distillation – DAD. The result is a Domain-Distilled Model, or DDM – an efficient second-layer model trained on top of foundational models.
Why Distillation Is a Better Fit for Enterprise Needs
Fine-tuning can deliver strong results, but it doesn’t scale well when an enterprise needs different models for different departments, domains, or languages. Distillation offers a more flexible approach:
Smaller models are easier to deploy. A distilled model with 7B parameters can often match the performance of a 70B general model in a specific task but can run on smaller hardware, including on-prem servers or edge devices.
It’s cheaper. Instead of re-training large models repeatedly, one large model can teach many small, domain-specific models. This one-to-many setup reduces both training and inference costs.
It’s faster. Enterprises can distill new models quickly as business needs change. Updating a compliance model or spinning up a new one for a product category becomes a fast, iterative process.
Performance remains high. Studies by DeepSeek and Google show that distilled models can outperform their teacher models on focused tasks. TWO AI has seen the same with our SUTRA-based second-layer models, particularly in multilingual and regulated industries.
Smarter Models, Less Data
Fine-tuning traditionally requires large amounts of labeled data. With distillation, much of the training data can be generated by the teacher model itself. This reduces the need for human-annotated datasets and speeds up the training process.
Google’s recent work showed that a 770M distilled model could outperform a 540B model on reasoning tasks using only 80 percent of the training data. Similarly, TWO AI’s internal benchmarks on SUTRA show that distilled models can achieve high accuracy with limited real-world data, especially when paired with synthetic data and expert prompts.
This is a powerful advantage for industries like healthcare and finance where labeled data is scarce or sensitive. Distillation enables enterprises to build smarter models without risking privacy or spending millions on annotation.
TWO AI’s Approach: D3-Powered Second-Layer Intelligence
At TWO AI, we are building a portfolio of second-layer models that sit on top of foundation models like SUTRA. These models are distilled, multilingual, and domain-specialized—trained using our D3 framework: Distilling, Domain, and Data. Here’s how D3 shapes our roadmap:
Distilling lets us compress the intelligence of large foundation models into lean, efficient students.
Domain alignment ensures each model speaks the language of a specific industry—finance, healthcare, retail, and more.
Data from both real-world sources and teacher-generated synthetic examples helps the models learn quickly and effectively, even with limited supervision.
D3 finance model understands balance sheets, compliance documents, and can assist with audit prep. D3 healthcare model can interpret medical notes in multiple languages. D3 retail model speaks both product data and regional customer sentiment. Because these are distilled models, they are small enough to deploy anywhere, yet smart enough to match large models on specialized tasks. And because they’re multilingual, they can serve users across regions with equal fluency.
Fine-tuning and RAG brought AI closer to the needs of businesses. Distillation brings it within reach—economically, technically, and operationally. At TWO AI, our domain-distilled models (D3) represent a leap forward: models that are small, smart, multilingual, and made for your industry. This is not just about saving cost. It’s about building models that perform and scale better where it matters.
Tech @ TWO
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