Author: shakticloud

Yotta Powers PARAM-1: India’s Own Foundation Model for AI Age

Posted on by shakticloud

Artificial Intelligence has emerged as the defining technology of our era, transforming industries, economies, and the way people live and work. But despite their remarkable capabilities, today’s most powerful foundation models – such as GPT and LLaMA – are predominantly built and fine-tuned for Western languages, cultures, and contexts. They excel in English and a few global tongues yet struggle to adapt to India’s vast linguistic diversity and rich cultural nuances.

For over a billion Indians, this creates a serious challenge. The very technology designed to democratise access to knowledge, boost productivity, and drive innovation often feels distant, inaccurate, or inaccessible. Imagine a farmer seeking crop guidance in Marathi, a student learning in Tamil, or a policymaker analysing data in Bengali- the AI gap becomes clear and urgent.

To solve this, BharatGen, India’s first government-funded, indigenously developed multimodal large language model (LLM) initiative, set out with a bold vision: to build an AI that not only speaks India’s languages but also understands its cultural and social context.

Through its research, BharatGen identified three core shortcomings in how global AI models engage with India:

1. Linguistic Fragmentation – Indic languages are morphologically rich and complex. Conventional tokenizers often split words incorrectly, leading to poor comprehension and broken outputs.

2. Cultural Disconnect – With little exposure to Indian cultural data, most models generate responses that are irrelevant or even inappropriate in local contexts.

3. Code-Mixing Blind Spots – Everyday Indian communication blends English with regional languages (e.g., Hinglish, Tanglish), a nuance that mainstream models fail to handle effectively.

It was to address these very challenges that BharatGen created PARAM-1 – a foundation model built from the ground up for the Indian ecosystem. PARAM-1 is not just about making AI more powerful; it is about making AI truly inclusive, giving India a model that reflects its languages, culture, and people.

PARAM-1

PARAM-1 was designed with three guiding principles:

1. Representation – At least 25% of training data dedicated to Indic languages across multiple scripts and domains.

2. Tokenization Fairness – A custom multilingual SentencePiece tokenizer optimised for Indic morphology to reduce word fragmentation.

3. Evaluation Alignment – Benchmarked against India-specific tests like IndicQA, code-mixed reasoning, and socio-linguistic robustness.

Yotta’s Shakti Cloud Powers PARAM-1 Training

BharatGen executed the training of the PARAM-1 foundation model on Yotta’s managed SLURM-cluster—an engineering marvel built on 64 NVIDIA HGX H100 nodes. Each node unleashes the raw power of 8× H100 Tensor Core GPUs, seamlessly interwoven through a fully meshed NVLink/NVSwitch fabric that delivers blistering terabytes-per-second of bandwidth at sub-microsecond latencies. This bleeding-edge architecture annihilates communication bottlenecks and achieves near-perfect linear scaling, propelling distributed training workloads into a new realm of performance and efficiency. For inter-node communication, the cluster leverages a high-speed InfiniBand fabric optimized for low-latency GPU-to-GPU transfers, a setup critical for efficiently scaling large model training across multiple nodes. Each compute node also has high-throughput storage, enabling smooth handling of massive multilingual corpora during data streaming and checkpointing.

The training workflow was orchestrated using SLURM for job scheduling, combined with NVIDIA’s NCCL for collective GPU communication. This robust infrastructure provided a scalable and reliable foundation for pretraining PARAM-1 over tens of trillions of tokens using thousands of H100 GPUs in parallel.

 

A Step Towards AI Sovereignty

PARAM-1 is more than a model – it’s a statement. India’s languages, culture, and context now have a place at the heart of the AI era. From government services and education to healthcare, agriculture, and creative industries, PARAM-1 can power applications that truly serve over a billion people.

Powered by Shakti AI Factory; secure, high-performance infrastructures purpose-built to accelerate frontier AI workloads. This backbone ensures that BharatGen can scale with unmatched speed and reliability. Through this partnership, India is not merely a participant in the sovereign AI revolution – it is positioning itself at the very forefront, shaping and leading it.

 

Source: https://bharatgen.com/param-revolutionizing-ai-for-india/

Shakti Studio: Where AI Dreams Go Live

Posted on by shakticloud

Every enterprise today wants a piece of the AI revolution — to build smarter, move faster, and scale. But the road from idea to production is a battlefield. You start with inspiration, but before long, you’re neck-deep in rate limits, tangled infrastructure, and weeks of setup that feel more like survival than innovation.

Imagine skipping all that.

Imagine a world where your models spring to life instantly, where scaling happens in milliseconds, and where your biggest worry isn’t infrastructure; it’s what to build next.

Shakti Studio is the AI inference and deployment platform that turns bold ideas into production-grade AI, faster than ever.

The Power Behind the Curtain

Shakti Studio isn’t just another MLOps tool, it’s the stage where your AI takes center spotlight. Whether it’s LLMs, diffusion algorithm or a custom pipeline, Shakti Studio lets you run it all instantly. No waiting, no wiring, no scaling panic. Just plug in, deploy, and watch your models perform in full throttle.

At its core, Shakti Studio fuses the flexibility of cloud-native operations with the brute power of NVIDIA L40S and H100 GPUs, giving enterprises a high-performance launchpad to train, fine-tune, and deploy large models seamlessly.

Why Enterprises Love It: Shakti Studio was designed for teams that don’t want to spend months “getting ready.” It’s for builders for those who want to go live now.

With Shakti Studio, you get: 

1. Enterprise Grade AI APIs – Fire up endpoints for LLMs, ASR, TTS, and Image Generation instantly.
2. Serverless GPU Scaling – Access GPU power on demand. No cluster management. No cooldowns.
3. Bring Your Own Model (BYOM) – Deploy your Hugging Face or Docker-based checkpoints effortlessly.
4. Production Reliability – SLA-backed uptime, real-time logs, and built-in monitoring for every workload.

The Three Pillars of AI Excellence 

At the heart of Shakti Studio lies three defining forces: Serverless GPUs, AI Endpoints, and Fine-Tuning, each crafted to simplify one stage of your AI lifecycle.

Shakti Serverless GPUs

Skip the hassle of cluster management. Spin up elastic GPU compute in seconds, scale automatically, pay fractionally and observe everything in real time. TensorFlow, PyTorch, Hugging Face – it’s all there, ready to roll. With SLA enforcement, real-time observability, and zero friction, this is GPU power reimagined for modern AI ops.

Shakti AI Endpoints

Plug, Play, Produce  With Shakti AI Endpoints, bringing AI to production is as easy as calling an API. These GPU-optimised, low-latency endpoints bring production-ready AI straight to your applications. From digital assistants to content generation, from drug discovery to retail analytics, you can now infuse intelligence into every workflow with an OpenAI-compatible API that scales automatically, secures data, and bills per use.

Shakti Fine-Tuning

Custom AI, Your Way. Generic models are yesterday’s story. With Shakti Fine-Tuning, you sculpt AI that speaks your language, understands your data, and works your way. Leverage LoRA, QLoRA, and DPO techniques to fine-tune giants like Llama and Qwen up to 15× faster on distributed GPUs. Your data stays private, your models stay secure, and your deployments go live in minutes. From conversational bots to industry-specific intelligence, Shakti Fine-Tuning brings personalisation to the heart of enterprise AI.

The Shakti Studio Experience

What sets Shakti Studio apart is not just its power, but its poise. Developers can deploy straight from the UI or CLI. Data scientists can run experiments without waiting for a single GPU slot. Enterprises get full observability, compliance, and cost transparency, right out of the box. Every workload, every log, every rate limit – fully visible and fully controlled. Whether you love clicking buttons or scripting commands, Shakti Studio adapts to your flow; UI, CLI, or API.

From Prototype to Production – In Record Time. Speed isn’t a luxury — it’s survival. Shakti Studio collapses weeks of setup into minutes, bringing the full power of MLOps, inference, and scaling into one frictionless flow.

So whether you’re building a next-gen chatbot, a creative content engine, or an AI-powered enterprise dashboard, Shakti Studio ensures one thing above all; your AI moves from idea to impact faster than ever.

Shakti Studio — Build Bold. Deploy Fast. Scale Infinite.

When innovation meets performance, you get Shakti Studio; the place where AI is not just trained, but unleashed.

Yotta’s Shakti Cloud Delivers Peak Performance for LLM Training 

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High-performance GPUs are becoming the standard for training modern AI models, but real innovation depends on the infrastructure behind them. At Yotta, we’ve engineered a platform that delivers scalable, consistent, and production-grade performance for demanding AI workloads. To demonstrate its capabilities, we chose Llama 3.1 70B, one of the most trusted benchmarks in the LLM ecosystem, and ran a full training run on a 256-GPU NVIDIA H100 cluster powered by Shakti Bare Metal.

Shakti Bare Metal provides dedicated access to NVIDIA H100 and L40S GPUs with direct hardware control, low-latency performance, and enterprise-grade security. It supports seamless scaling from single nodes to large clusters, making it ideal for AI and HPC workloads.

The Results

We benchmarked our performance against NVIDIA’s published speed of light numbers. Here’s how Yotta’s infrastructure stacked up:

Training Step Time:
– 14.96 seconds per step (vs NVIDIA’s 14.72 seconds)
– 99.5% alignment with reference

FLOPs Utilisation (BF16 Dense):
– 525.83 TFLOPs out of a theoretical 989 TFLOPs
– 53.16% utilisation (vs NVIDIA’s 54.24%)

These were achieved in production on our Shakti Bare Metal platform. This benchmark shows that our infrastructure performs almost identically to NVIDIA’s internal systems under real-world conditions.

How We Got There

Delivering this level of performance is the result of end-to-end system engineering and optimisation. Here’s what powers our performance:

1. High-Bandwidth Interconnects: We used RDMA and NVLink to ensure fast, low-latency GPU communication – critical for scaling deep learning workloads. This architecture minimises latency and maximises bandwidth, ensuring that data flows efficiently across all GPUs – even under heavy load.

2. Advanced Parallelism Techniques: Our setup combined tensor, pipeline, and data parallelism – finely tuned for LLM training using tools like Megatron and DeepSpeed.

3. Intelligent Orchestration Stack: SLURM-based orchestration enabled flexible resource allocation and high availability, with tight runtime controls and minimal scheduling overhead.

Built for What’s Next in AI

Training a model like Llama 3.1 70B is no small feat. It requires vast compute power, precision engineering, and weeks of effort. Our benchmark proves that we can not only handle this scale, but we can also do it with world-class efficiency.

– We’ve trained a state-of-the-art LLM on production infrastructure
– We’ve delivered performance that closely aligns with NVIDIA’s published reference numbers
– We’re ready to support the next wave of AI innovation at scale

Training large language models requires more than powerful GPUs. It demands a tightly optimized, end-to-end system. From compute density and GPU interconnects to orchestration, scheduling, and data pipeline efficiency – every layer impacts how fast you can train, how far you can scale, and how effectively you manage cost.

With Shakti Bare Metal, we’ve engineered a platform built on three foundational pillars designed for real-world AI outcomes:

Performance That’s Proven

We don’t just promise benchmarks – we deliver them. Real workloads, real infrastructure, and numbers that speak for themselves.

Scalability That’s Linear

Whether you’re running on 8 GPUs or 256+, our architecture ensures that performance doesn’t fall off a cliff as you scale.

Value That Scales With You

We combine bare metal efficiency, transparent pricing, and hyperscaler-grade support – so you can grow without unexpected costs or hidden complexity.

AI Builders, This Is Your Platform

For teams building frontier models, enterprise copilots, or domain-specific LLMs, Yotta offers an infrastructure layer that’s ready for tomorrow. These benchmarks confirm that our systems can match the best in the world – giving you the foundation to innovate faster, scale smarter, and stay ahead.

And we’re not stopping here. We’ve got NVIDIA B200 GPUs on the way, further expanding our capabilities to support next-gen AI workloads with even greater efficiency and scale.

Whether you’re in finance, healthcare, manufacturing, or AI research, the time it takes to train a model, the cost per run, and the throughput of your infrastructure all determine your speed to impact. With Yotta’s Shakti Cloud, you don’t have to compromise.

HPC Driving Deep Innovations High-Performance Computing (HPC): Powering Deep Innovations Across Industries

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High-Performance Computing (HPC) stands out as a transformative force. Tasks that were once deemed impractical, such as complex simulations, data analyses, and modelling, have now become not only feasible but instrumental in driving significant advancements. HPC’s prowess is particularly evident in fields like astrophysics, climate science, and materials research, where its capacity to process extensive datasets and execute intricate calculations proves invaluable. The simulation of celestial phenomena, climate change models, and the exploration of material properties at the atomic level collectively propel the limits of human understanding, marking HPC as a pivotal catalyst in scientific exploration.

Likewise, HPC facilitates the scaling of simulations by adjusting various parameters, resulting in reduced wall-clock time, and delivering faster with more precise outcomes. Its capacity to swiftly process intricate workloads and analyse extensive datasets surpasses the capabilities of on-premises computers. The versatility of HPC extends across diverse industries, proving invaluable in resolving intricate mathematical and science-based problems. Below are the following advancements in various industries:

Aerospace and Defence:

In aerospace, HPC facilitates intricate simulations of aerodynamics, structural mechanics, and fluid dynamics, allowing engineers to optimize aircraft design, improve fuel efficiency, and enhance overall performance. This accelerates the development of next-generation aircraft and spacecraft, fostering advancements in aviation technology.

In the defence sector, HPC plays a pivotal role in developing cutting-edge technologies, from sophisticated missile systems to advanced radar simulations. The ability to process vast amounts of data in real-time enables defence analysts to model complex scenarios, enhancing strategic planning and decision-making. Moreover, HPC is instrumental in addressing cybersecurity challenges, ensuring the resilience of critical defence systems against evolving cyber threats. The fusion of HPC with artificial intelligence further augments threat detection and response capabilities, safeguarding sensitive information.

Automotive Industry:

HPC enables complex real-time processing of vast datasets from sensors and cameras, allowing vehicles to make split-second decisions and navigate dynamic environments with unprecedented accuracy. Simulation and testing of autonomous systems, powered by HPC, have become instrumental in enhancing the reliability and safety of self-driving technologies. HPC accelerates the development of electric vehicles (EVs) by optimising battery design and energy management systems. Computational simulations, powered by HPC, model the behavior of batteries under various conditions, leading to innovations that extend battery life, enhance charging efficiency, and ultimately drive the widespread adoption of electric mobility.

Life Sciences and Healthcare Transformation:

HPC is revolutionising healthcare by facilitating precision medicine. Analysing vast genomic datasets, identifying personalised treatment plans, and simulating drug interactions are made possible by the computational muscle of HPC. Researchers and healthcare professionals can now delve into the intricacies of individual patient profiles, leading to more targeted therapies, reduced side effects, and improved patient outcomes. It is also accelerating the pace of drug discovery, making it more efficient and cost-effective.

Financial Services:

In the financial sector, HPC is a driving force behind sophisticated modelling and risk analysis. Complex algorithms for market predictions, portfolio optimisation, and risk assessment demand immense computational power, which HPC provides. Traders, financial analysts, and institutions leverage HPC to process vast amounts of financial data in real-time, enabling quicker decision-making and enhancing overall market efficiency. The ability to simulate various market scenarios aids in mitigating risks and optimising investment strategies.

Energy Exploration and Climate Modelling:

The energy sector benefits significantly from HPC in various ways. Simulating oil reservoirs, optimising renewable energy sources, and modelling climate scenarios for more sustainable practices are all made possible through HPC. The ability to process massive datasets and simulate complex interactions allows for better decision-making in resource exploration, energy production, and environmental management. It is instrumental in developing cleaner and more efficient energy solutions.

Government and Public Sector:

HPC’s computational capabilities empower government agencies to analyse vast datasets efficiently, leading to informed decision-making and policy formulation. From optimising public transportation systems to modelling the potential impact of policy changes, HPC enables authorities to navigate complex challenges with precision and foresight.

In the field of public safety and national security, HPC plays a critical role in areas such as threat analysis, emergency response planning, and cybersecurity. The ability to process and analyse large volumes of data in real-time enhances the effectiveness of intelligence agencies and ensures the resilience of critical infrastructure against cyber threats.

Climate and Weather Modelling:

Climate and weather modelling using HPC allows scientists to simulate intricate atmospheric processes, including temperature variations, wind patterns, and precipitation cycles, with unprecedented detail. These simulations provide valuable insights into long-term climate trends, extreme weather events, and the potential impact of climate change on various regions.

HPC enables researchers to create higher-resolution models, improving the precision of weather forecasts and enhancing our ability to predict severe weather conditions such as hurricanes, tornadoes, and heatwaves. Real-time simulations, powered by HPC, empower meteorologists to make more accurate and timely predictions, aiding in the preparation and response to natural disasters.

Manufacturing and Engineering Advancements:

HPC plays a pivotal role in transforming manufacturing and engineering processes. Computational fluid dynamics, structural simulations, and virtual prototyping are all made more efficient and accurate through HPC. This enables engineers to design and test products in a virtual environment before physical prototypes are even created, significantly reducing development time and costs. From optimising aerodynamics in automotive design to predicting material fatigue in aerospace engineering, HPC is at the forefront of innovation.

Media and Entertainment:

The rise of streaming platforms and on-demand services has been facilitated by HPC. The ability to process and deliver vast amounts of video content to global audiences in real-time requires robust computing infrastructure. HPC ensures seamless streaming experiences, high-quality video resolution, and efficient content delivery across various devices. In live events, sports broadcasts, and news coverage, HPC enables real-time graphics rendering, enhancing the visual experience for viewers. This capability is particularly evident in sports broadcasts, where complex graphics, statistics, and augmented reality elements are seamlessly integrated.

Telecommunications:

The deployment of 5G networks, with their increased data transfer speeds and low latency, relies heavily on HPC. HPC accelerates the testing and development of 5G technologies, ensuring a seamless transition to the next generation of wireless communication with enhanced capacity and connectivity. With the proliferation of the Internet of Things (IoT), telecommunications companies manage vast amounts of data generated by interconnected devices. HPC processes this data efficiently, enabling telecom providers to offer reliable IoT services and support the growing ecosystem of smart devices. Telecommunications infrastructure is a prime target for cyber threats. HPC plays a crucial role in cybersecurity by analysing network traffic patterns in real-time, detecting anomalies, and identifying potential security breaches. Hence, HPC is a cornerstone in the telecommunications industry, empowering providers to build robust, high-performance networks, offer innovative services, and adapt to the evolving demands of the digital age. As telecommunications continues to evolve, HPC will remain a key driver of technological advancements, shaping the future of global communication.

Academic Research:

The surge in artificial intelligence (AI) and machine learning (ML) applications is fueled by HPC. Training deep neural networks, processing datasets for pattern recognition, and developing sophisticated AI models all require the computational capabilities that HPC provides. From natural language processing to image recognition, HPC is pushing the boundaries of what AI can achieve, opening new possibilities for automation, optimisation, and innovation across industries. HPC has become an indispensable partner in the pursuit of knowledge across academic disciplines. As academic researchers continue to push the boundaries of what is possible, HPC remains a catalyst for innovation, providing the computational power needed to explore new frontiers and address some of the most pressing challenges facing humanity.

Agriculture:

HPC enables precision agriculture by analysing vast datasets, including satellite imagery, weather patterns, and soil conditions. Farmers can make informed decisions about crop management, irrigation, and fertilizer application, maximising resource efficiency and minimising environmental impact. It accelerates research in agricultural science, enabling scientists to explore innovative solutions to global challenges such as food scarcity and sustainable farming practices. This contributes to the development of resilient agricultural systems capable of meeting the needs of a growing global population. It also facilitates the modelling and simulation of crop growth, allowing researchers to analyse various scenarios and environmental factors. This aids in predicting crop yields, optimizing planting schedules, and mitigating the impact of climate variability on agricultural production.

Conclusion:

High-Performance Computing holds a lot of importance in the technological landscape, propelling deep innovations that touch every facet of our lives. From unravelling the mysteries of the universe to revolutionizing healthcare, finance, and manufacturing, HPC is a driving force behind progress. Shakti Cloud, India’s inaugural and authentically indigenous AI-HPC Cloud, is at the forefront of delivering advanced GPU computing infrastructure, platforms, and services. As industries continue to push the boundaries of what is possible, HPC will remain at the forefront of innovation, unlocking new possibilities and reshaping the future of human endeavour.

How AI and ML are Shaping Data Center Infrastructure and Operations

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The rapid evolution of cloud computing, edge computing, and the rising demands of AI-driven workloads have made efficient data center management increasingly complex. As data volumes surge and the need for faster processing grows, traditional data center infrastructure and operations are being stretched beyond their limits. In response, Artificial Intelligence (AI) and Machine Learning (ML) are driving a fundamental transformation in how data centers operate, from optimising resource allocation to improving energy efficiency and security.

AI and ML are addressing key industry challenges such as scaling infrastructure to meet growing demands, reducing operational costs, minimising downtime, and enhancing system reliability. These technologies not only streamline the day-to-day operations of data centers but also lay the groundwork for the future of digital infrastructure—enabling more autonomous, adaptable, and sustainable systems.

AI and ML: Transforming Data Center Operations

1. AI-Driven Automation and Predictive Maintenance: Traditionally, data center management required extensive manual oversight, leading to inefficiencies and delays. However, AI-driven automation is reshaping this landscape by enabling real-time monitoring, self-healing systems, and predictive maintenance.

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AI-Driven Automation optimises workflows, reducing human intervention and ensuring more consistent performance. By automating repetitive tasks, staff can focus on higher-valueoperations. Self-healing systems autonomously detect, diagnose, and rectify faults without service disruption. Predictive Maintenance uses ML algorithms to analyse sensor data from servers, power supplies, and cooling systems to detect anomalies before failures occur. AI-powered digital twins analyse data silos, track facility components, and make real-time adjustments, enabling predictive maintenance and minimising operational disruption.

Sustainable operations are not just about cost savings; they are integral to meeting corporate and regulatory sustainability targets. AI enables data centers to achieve these goals while maintaining high operational efficiency

2. Energy Efficiency and Sustainable Operations: With increasing concerns about carbon footprints and rising operational costs, AI is playing a crucial role in enhancing energy efficiency in data centers. ML algorithms analyse historical power consumption patterns, enabling intelligent decision-making that optimises cooling, workload distribution, and power management to minimise energy waste. Dynamic cooling mechanisms, powered by AI, adjust cooling systems based on real-time data, such as server workload, external climate conditions, and humidity levels.

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Energy-efficient operations are not just about cost savings—they are also about meeting sustainability targets. Many data centers are now integrating renewable energy sources, with AI playing a critical role in balancing and optimising these resources. AI can predict power needs, helping data centers leverage renewables more effectively, thus reducing dependency on non-renewable sources.

3. Intelligent Workload and Resource Optimisation: AI and ML facilitate dynamic workload distribution, ensuring optimal allocation of resources such as compute, storage, and networking are allocated efficiently. These intelligent systems analyse workload patterns, redistribute resources dynamically, prevent bottlenecks, and improve overall system performance. This flexibility is critical as workloads become more diverse, particularly with the rise of AI workloads that require heavy computational power.

AI-driven orchestration tools empower data centers to scale workloads automatically based on demand. These tools optimise server utilisation, reducing unnecessary energy consumption, and preventing system overloads. As workloads become increasingly diverse, with the rise of AI-driven workloads such as real-time analytics, machine learning model inference, and AI training, it’s essential for data centers to utilise intelligent resource management to meet computational demands.

4. Enhanced Security and Threat Detection: As cybersecurity risks evolve, data centers are at the forefront of defense against increasingly sophisticated attacks. AI technologies are enhancing the security infrastructure by enabling real-time threat detection and faster response times.

AI-driven behavioural analytics can detect abnormal activity patterns indicative of cyberattacks or unauthorised access. These systems learn from historical data and continuously adapt to new attack vectors, ensuring more robust defenses against zero-day exploits and complex security breaches. By integrating ML-based security solutions, data centers can now protect against a wider range of threats, including DDoS attacks, insider threats, and ransomware. These systems can autonomously mitigate threats by triggering automatic responses such as isolating compromised systems or adjusting firewall settings.

Future of AI and ML in Data Centers

The future of AI and ML in data centers is poised to bring more advanced capabilities, including autonomous operations and edge computing. As AI continues to mature, we can expect smarter data centers that not only manage existing resources efficiently but also predict future needs. AI-powered edge computing will bring processing closer to data sources, reducing latency and improving response times. With the growth of IoT devices and edge deployments, AI will be integral in managing distributed infrastructure.

AI-driven data governance solutions will help hyperscale data centers meet compliance requirements and ensure data privacy. AI and ML are redefining data center infrastructure and operations by enhancing efficiency, optimising resource utilisation, improving security, and driving sustainability. Colocation data center companies like Yotta are leading the way in implementing these technologies to deliver state-of-the-art solutions, ensuring high performance, reliability, and cost-effectiveness.