Why Less Can Be More: The Science of Building Enterprise-Grade LLMs

As the demand for AI models grows, the challenge of building compute-efficient, high-performance models becomes ever more pressing. Chinchilla’s Law, which balances model size and the amount of training data, offers a blueprint for achieving this efficiency. This is particularly relevant as we look at models like Shakti LLM, which are designed to serve enterprise needs with domain-specific capabilities.

The recent developments from the Smol Model release, emphasizing the importance of cleaned datasets, have reignited the discussion on how quality data is often more critical than simply increasing parameter size. This aligns with our vision at SandLogic: to build models that excel not just in general-purpose tasks but also in specialized, domain-specific applications.

How ShaktiLLM’s Principled Approach is Redefining Enterprise AI

In the current AI landscape, there’s a common misconception that more data and larger models automatically lead to better performance. This belief has led to a race for building increasingly massive models trained on ever-larger datasets. However, at SandLogic, we took a different approach with ShaktiLLM—one grounded in mathematical principles and focused on enterprise needs rather than headline-grabbing parameter counts.

The Mathematics Behind Optimal Model Training

When we began developing Shakti LLM, we anchored our approach in Chinchilla’s Law, which provides a clear mathematical framework for optimal model training. This law establishes a crucial relationship between model size and the optimal amount of training data:

For every 250 billion parameters, a model needs approximately 5 trillion tokens of training data.

This means:

A 2.5B parameter model optimally needs 50B tokens.
An 8B parameter model optimally needs 160B tokens.

This isn’t just theoretical—it’s a fundamental principle that guided our development from day one. When we occasionally hear claims about ShaktiLLM “hallucinating” on certain benchmark datasets, it reflects a misunderstanding of this principle. We deliberately limit our training data in accordance with these mathematical ratios, even when more data is available. This isn’t a limitation—it’s a feature that prevents information overload and ensures optimal performance.

Breaking Down Enterprise AI Myths

Till now around 1000 developers & researchers tried our Shakti LLM, a researcher claimed Shakti LLM was underperforming because it hadn’t been exposed to certain benchmark datasets. This feedback misses a crucial point: in accordance with Chinchilla’s Law, we intentionally don’t train on every available dataset. This selective approach isn’t a weakness—it’s precisely what enables Shakti LLM to maintain reliability and prevent hallucinations in enterprise settings.

Before we go deep into this, let’s see how Shakti performed on various benchmark datasets

The recent success of models using cleaned, curated datasets over larger, noisier ones validates our long-standing approach. While this has become a trending topic in the AI community, at SandLogic, this was our foundation from the beginning.

The Three-Tier Enterprise AI Architecture

What truly sets Shakti LLM apart is our three-tier approach to building enterprise-ready AI:

1. Foundation Training: Building on Solid Ground

Selective training on high-quality public datasets Focus on fundamental language understanding Strict adherence to optimal data-to-parameter ratios

2. Domain Specialization

Careful curation of industry-specific datasets Integration of expert knowledge Optimization for vertical-specific tasks

3. Enterprise Customization

Fine-tuning on company-specific data Adaptation to unique business workflows Real-world performance optimization

Advanced Architecture for Real-World Performance

ShaktiLLM’s architecture incorporates five key innovations specifically designed for enterprise needs:

1. Variable Grouped Query Attention (VGQA)

VGQA in Shakti LLM dynamically groups related queries, which significantly improves how it handles conversations that involve complex back-and-forth exchanges. This is especially useful in multi-turn scenarios, where the model needs to maintain the logical flow of the conversation and understand relationships between different parts of the dialogue.

Consider a complex financial advisory scenario:

Client Meeting Scenario:
- Client discusses retirement goals
- References previous portfolio performance
- Asks about market conditions
- Requests investment recommendations

Shakti LLM processes all these contexts simultaneously, maintaining relationships while optimizing compute resources.

2. Rotary Positional Embeddings (RoPE)

RoPE enables Shakti LLM to handle long text sequences while retaining position and context within those sequences. This is crucial for maintaining the flow in multi-turn conversations, especially when the AI must remember key points from earlier interactions or extended dialogue.

In legal document analysis:

Contract Review Process:
- 50-page document analysis
- Multiple cross-references
- Historical precedent consideration
- Clause relationship mapping

RoPE enables Shakti LLM to maintain context across the entire document while understanding relationships between different sections.

3. SwiGLU Activations

SwiGLU activations in Shakti LLM ensure the model remains stable during both training and inference, particularly in high-load, multi-turn interactions. Enterprises relying on AI for real-time customer service, financial advice, or legal support need a model that can handle long, complex conversations without performance dips or response degradation.

Critical for enterprise deployment:

Ensures stable performance under varying loads
Maintains consistency during peak usage
Optimizes resource utilization

4. Direct Preference Optimization (DPO)

DPO fine-tunes Shakti LLM based on ranked human feedback, which allows the model to adjust its conversational responses in line with human expectations. This is critical in customer service or advisory roles, where not only the content of the response but also the tone and appropriateness matter.

Example from healthcare:

Patient Consultation:
Initial Response: "Your symptoms indicate..."
After DPO: "Given your medical history from previous visits, and considering your current medication regimen, these symptoms suggest..."

5. Sliding Window Attention

Shakti LLM employs a Sliding Window mechanism, allowing it to manage and maintain context across multiple turns, even in extended conversations. This technique ensures that the model can “look back” at earlier parts of the conversation while focusing on the most relevant information at hand. The windowing process helps the model avoid context loss, making its responses more coherent and contextually aware.

Essential for long-form interactions:

Technical Support Scenario:
User: "Following up on our previous ticket about the database optimization..."
[Shakti LLM maintains context from previous interactions while focusing on current issue resolution]

Enterprise Impact: Why These Choices Matter

For CTOs and AI architects, our approach translates to three key benefits:

1. Predictable Performance

Mathematically optimal training prevents overfitting
Consistent response quality
Reliable handling of complex queries

2. Resource Efficiency

No wasted compute on unnecessary data
Optimized training processes
Efficient deployment and scaling

3. Domain Expertise

Deep understanding of industry-specific contexts
Reduced hallucination risk
Better alignment with business needs

Multi-Turn Excellence: A Critical Enterprise Capability

Enterprise AI isn’t about single-shot queries—it’s about maintaining context through complex interactions.

In enterprise environments, AI interactions rarely consist of simple, one-shot queries. Instead, they involve complex, ongoing dialogues where context builds upon previous exchanges—much like a prolonged business conversation. Multi-turn capability refers to an LLM’s ability to maintain context, remember relevant details, and build coherent understanding across a series of related interactions, rather than treating each query in isolation.

Consider a financial advisor consulting with a client: The conversation might start with portfolio performance, move to risk tolerance, reference previous investment choices, and culminate in specific recommendations. Each turn in this conversation builds upon previous exchanges. Without strong multi-turn capabilities, an LLM would treat each query independently, losing the crucial context that makes the interaction meaningful and productive.

This capability is particularly critical for enterprises because:

Complex Decision Making: Enterprise decisions rarely come from single queries—they evolve through detailed discussions and iterative refinement.
Context Retention: Business processes often require referencing information from earlier in the conversation, sometimes spanning multiple sessions.
Workflow Integration: Enterprise tasks typically involve multiple steps that build upon each other, requiring the AI to maintain awareness of the entire process.

Consider these scenarios where Shakti LLM excels:

1. Financial Advisory

Client: "How has my portfolio performed?"
Shakti LLM: [Analyzes historical data]
Client: "Given that performance, should I adjust my retirement plans?"
Shakti LLM: [Maintains context from previous analysis while considering long-term goals]

2. Healthcare

Doctor: "Review patient history for similar symptoms"
Shakti LLM: [Analyzes records]
Doctor: "Compare with current presentation"
Shakti LLM: [Integrates historical context with current data]

3. Legal Analysis

Attorney: "Find precedents for this case"
Shakti LLM: [Searches relevant cases]
Attorney: "How do they apply to our current situation?"
Shakti LLM: [Maintains context while drawing specific parallels]

What Drives Shakti LLM’s Superior Performance?

The remarkable performance of Shakti LLM, as shown in the benchmarking results across GPU, CPU, and MAC, is no accident. It is the result of deliberate architectural choices and innovations designed to optimize both speed and efficiency. Several key aspects of Shakti LLM directly contribute to its high throughput and cross-platform adaptability:

VGQA (Variable Grouped Query Attention): VGQA allows Shakti LLM to handle long text sequences more efficiently by dynamically grouping related queries. This reduces the overall computational load on the attention mechanism, enabling faster processing of tokens, especially in high-context tasks such as document summarization or multi-turn dialogues. This is one reason why Shakti LLM achieves such high tokens-per-second rates on GPU, where the model’s attention mechanism can fully leverage the parallel processing power.
RoPE (Rotary Positional Embeddings): RoPE enables Shakti LLM to process long sequences of text without sacrificing speed. Unlike traditional positional encodings, RoPE is better suited for handling long contexts without bloating the model’s memory usage. This improves the model’s ability to process tokens quickly, particularly in complex, multi-turn conversations, which is crucial for real-time enterprise applications.
SwiGLU (Switching Gated Linear Units): SwiGLU activations provide Shakti LLM with a more stable and efficient training process. By stabilizing the gradient flow during both training and inference, SwiGLU ensures that Shakti LLM performs consistently well even under high computational loads. This makes the model more responsive, particularly in multi-threaded environments, where inference must happen at scale—explaining the model’s competitive performance on CPU and MAC platforms.
Optimized Quantization (Q4 and Q5): Shakti LLM uses advanced quantization techniques (Q4 and Q5 configurations), which significantly reduce the memory footprint of the model without a substantial loss in accuracy. This leads to faster inference times, as fewer resources are required to process the same number of tokens. These optimizations are particularly noticeable in edge devices like MAC systems, where resource constraints are often more stringent.
Efficient Sliding Window Attention: The Sliding Window attention mechanism in Shakti LLM ensures that it processes tokens in multi-turn dialogues or large document contexts more effectively by retaining relevant historical data. This makes Shakti LLM excel in tasks where the context window needs to span multiple interactions—offering not just accuracy but also faster token throughput compared to models like Phi-3.1-mini, which may lack this optimization.
Balanced Compute Utilization: By adhering to Chinchilla’s Law, Shakti LLM is optimized for the right balance between model size and training data, ensuring that it uses compute resources effectively. Models like Phi-3.1-mini or LLAMA may focus heavily on either scaling parameters or increasing datasets, often leading to inefficiencies. In contrast, Shakti LLM’s balance allows it to deliver high performance across different hardware setups without requiring excessive compute power.

Why Shakti LLM’s Optimizations Matter for Enterprises

These innovations aren’t just technical improvements—they’re directly responsible for the superior performance Shakti LLM delivers across GPU, CPU, and MAC platforms. For enterprises, these optimizations translate into:

Faster processing of customer queries, documents, and insights, improving response times and overall efficiency.
Cross-platform flexibility, allowing enterprises to deploy Shakti LLM on cloud, on-premise, or edge devices like Apple hardware without sacrificing performance.
Scalability across various applications, from real-time financial analysis to automated customer service, ensuring that the model can handle both simple tasks and complex multi-turn conversations at speed.

While models like Phi-3 4B, LLAMA 3B, and Mistral 7B focus on general capabilities, ShaktiLLM’s architecture is specifically optimized for enterprise use cases. Our deliberate choices in model size, training data, and architectural innovations create a system that excels where it matters most: real-world business applications.

For CTOs and AI architects evaluating AI solutions, the message is clear: look beyond parameter counts and dataset sizes. Focus on systems built with clear principles, optimal training, and enterprise-specific capabilities. That’s the path to real business value, and that’s what Shakti LLM delivers.