From Edge to Excellence: The Shakti LLM Revolution in Enterprise AI

The Shakti LLM series from SandLogic Technologies, with scalable configurations ranging from 100 million to 8 billion parameters, redefines what’s possible in edge AI and enterprise-scale solutions. Built with a device-first approach, Shakti LLM powers on-device intelligence while seamlessly addressing enterprise use cases across cloud and on-premise deployments.

This article is designed for CTOs, CIOs, CDOs, LLM Engineering Managers, and LLM Researchers who are leading their organizations' AI strategies. Backed by innovations like VGQA, RoPE, Sliding Window Inference, and RLHF, Shakti stands out in enabling low-latency performance, energy efficiency, and domain-specific optimization for diverse applications.

Let’s explore the configurations, benchmark highlights, and real-world use cases that make Shakti LLM a game-changer.

Shakti LLM Series: Configurations and Benchmarks

Benchmarks That Matter

The Shakti-LLM 2.5B model consistently ranks among the top-performing models in critical benchmarks:

• Reasoning (PIQA): 86.2% - highest among peers, showcasing exceptional problem-solving capabilities.
• Medical Knowledge (MedQA): 60.3% - second only to Mistral 8x7B, validating its domain-specific strength.
• Social Understanding (Social QA): 79.2% - highest score, demonstrating superior conversational AI capabilities.
• Truthful QA: 68.4% - outperforming models like Phi-3 Mini and Llama 3.

These benchmarks highlight Shakti’s balance of efficiency and accuracy, making it ideal for enterprise applications that require precise, high-quality responses.

Throughput Efficiency Across Platforms

When it comes to throughput and efficiency, Shakti excels across GPU, CPU, and Mac environments:

This comparison done on Shakti 2.5B model

• On GPUs, Shakti achieves 331 tokens/sec, nearly 2x faster than Phi-3 models.
• On CPUs, Shakti processes 18.93 tokens/sec, more than double Phi-3’s performance, enabling cost-effective scaling on standard hardware.
• Even on Mac systems, Shakti maintains an impressive throughput of 128 tokens/sec, making it a viable choice for diverse environments.

This throughput efficiency underscores Shakti’s edge in low-latency, high-throughput tasks, making it highly adaptable to real-time enterprise applications.

Detailed Configurations and Use Cases

The Shakti LLM series is engineered to address a diverse spectrum of enterprise requirements, from lightweight edge applications to complex, large-scale multimodal analytics. Each configuration is uniquely optimized with advanced technologies like VGQA, RoPE, and RLHF, ensuring precision, scalability, and real-time responsiveness. By leveraging domain-specific datasets and industry-aligned fine-tuning, Shakti models excel across Healthcare, finance, legal,?retail, and?e-commerce verticals. Below is an in-depth look at each configuration, its features, and real-world use cases highlighting its transformative potential for enterprises.

1. Shakti 100M: Lightweight NLP for Edge Devices

Key Features:

• Optimized for low-power environments like IoT devices, smartphones, and wearables.
• Sliding Window Inference for real-time processing.

Example Use Cases:

1. Retail Chatbots: Automating product inquiries and FAQs in retail apps with minimal hardware requirements. Example: A mobile app assistant providing product recommendations in a grocery store.
2. Smart Assistants for IoT Devices: Enabling voice commands in smart home systems with real-time responses. Example: A thermostat assistant offering energy-saving tips in response to user queries.
3. E-commerce Search Optimization: Handling product queries with domain-specific accuracy. Example: Filtering results for specific customer needs on an e-commerce platform.

2. Shakti 250M: Mid-Level NLP for Industry Automation

Key Features:

• Domain-specific fine-tuning for industries like healthcare, finance, and legal.
• Scalable deployment across cloud and edge environments.

Example Use Cases:

1. Healthcare Triage Bots: Providing quick preliminary diagnostics based on symptoms. Example: An app assisting patients in identifying the need for a doctor’s consultation.
2. Financial Advisors: Automating customer queries about investment products and market insights. Example: A chatbot for retail banks guiding customers on mutual fund investments.
3. Legal Document Summarization: Extracting summaries from lengthy legal documents. Example: A tool for paralegals to prepare quick case briefs.

3. Shakti 500M: High-Demand Conversational AI

Key Features:

• Block Sparse Attention for efficient query handling.
• RLHF for user-aligned output generation.

Example Use Cases:

1. Virtual Assistants in Customer Support: Handling complex multi-turn dialogues in sectors like insurance. Example: A virtual agent processing customer complaints and updating claim statuses.
2. Legal Advisory Bots: Answering complex legal questions for law firms. Example: Assisting clients with understanding contractual clauses.
3. Health Coaching: Interactive coaching on fitness and diet plans. Example: A chatbot offering daily health tips to users.

4. Shakti 1B: Advanced Multimodal Processing

Key Features:

• Integration of text, images, and charts for holistic insights.
• Fine-tuned on domain-specific data like radiology and financial charts.

Example Use Cases:

1. Radiology Image Analysis: Assisting doctors with diagnostic insights from medical images. Example: Highlighting potential abnormalities in chest X-rays.
2. Financial Chart Analysis: Automating insights from complex financial reports. Example: Identifying trends in quarterly earnings reports for analysts.
3. Retail Shelf Management: Real-time analytics on product placement from store images. Example: Flagging misplaced items in supermarkets.

5. Shakti 2.5B: Enterprise-Level Multilingual NLP

Key Features:

• VGQA for efficient memory management and inference.
• Sliding Window Inference for real-time applications.

Example Use Cases:

1. Multilingual Call Center Automation: Supporting conversations in diverse languages for global enterprises. Example: Real-time transcription and response generation in customer service.
2. Healthcare Diagnostic Reports: Generating detailed diagnostic summaries from structured and unstructured data. Example: Summarizing patient medical histories for doctors.
3. E-commerce Product Analytics: Aggregating and summarizing customer reviews across regions. Example: Identifying trends from customer feedback on international e-commerce platforms.

6. Shakti 5B: Business Analytics and Decision Support

Key Features:

• Block Sparse Attention for handling large datasets.
• Task-specific training on datasets like financial and clinical case files.

Example Use Cases:

1. Market Predictions: Analyzing market data to predict trends for investment firms. Example: Forecasting stock performance based on historical data.
2. E-commerce Insights: Real-time analytics on purchase behaviors to boost sales. Example: Identifying frequently bought products during specific seasons.
3. Clinical Decision Support: Offering treatment recommendations based on patient data. Example: Recommending medication options to doctors during consultations.

7. Shakti 8B: Apex AI for Complex Enterprise Applications

Key Features:

• Multi-modal capabilities for scientific research and enterprise insights.
• Designed for large-scale tasks like personalized financial planning and scientific research.

Example Use Cases:

1. Legal Case Analytics: Extracting insights from thousands of legal documents. Example: Analyzing case laws to identify precedents.
2. Scientific Research Support: Summarizing research papers for R&D teams. Example: Condensing findings from pharmacological studies.
3. Personalized Wealth Management: Tailored financial advice for high-net-worth individuals. Example: Portfolio analysis for personalized investment strategies.

The Shakti LLM series is built for enterprises aiming to scale their AI capabilities across domains and complexity levels. Whether it’s about enabling on-edge intelligence on small devices or empowering multi-modal analytics, Shakti delivers on its promise of efficiency, scalability, and innovation. With its tailored configurations and domain-specific optimizations, Shakti provides a robust foundation for enterprises to stay ahead in the competitive AI landscape.

Let’s redefine your enterprise AI strategy with Shakti. Connect with us to explore tailored solutions for your business needs.

In my next article, I will cover the tools we’ve built, our Responsible AI framework, and how we enable domain model training of LLMs and task-specific agentic models.

Stay tuned!