Beyond Bare Metal: How Bud AI Foundry Helps Cloud Service Providers Move from Bare Metal to AI-First Services

The rapid rise of Generative AI (GenAI) is sparking a new wave of global change, a movement that can only be described as the AI transformation. Much like the digital transformation that preceded it, this shift is forcing organizations to fundamentally rethink how they operate and innovate. As companies embark on their AI transformation with […]

NxtGen’s M for Coding, Powered by Bud— India’s Alternative to Claude Code

Together with NxtGen Cloud, we’re excited to introduce M for Coding — a coding assistant launched under NxtGen Cloud’s M GenAI platform and powered by Bud’s code-generation models and infrastructure. This is India’s alternative to Claude Code, delivering the same powerful coding experience at a much more cost-effective rate. India’s Alternative to Claude Code India […]

A case against AI wrapper companies & proprietary API-based models for Enterprise AI

Over the past couple of years, we’ve seen a wave of “wrapper” AI companies pop up. These are the startups that don’t train their own models, but instead sit on top of foundation models like GPT or Claude, adding prompts, workflows, and lightweight integrations to make them feel like full products. They were the easiest […]

We Just Released the World’s Largest Open Dataset for AI Guardrails

Ensuring that language models behave safely, ethically, and within intended boundaries is one of the most pressing challenges in AI today. That’s why we’re excited to share the release of the largest open dataset ever published for AI guardrails: budecosystem/guardrail-training-data. Why Guardrails Matter The rise of language models has unlocked extraordinary possibilities: they can write […]

From GenAI Pilot to Production: Best Practices and Evals That Matter

Many GenAI initiatives shine in the pilot phase but struggle when scaled to production. A common reason is that teams often focus narrowly on metrics like time-to-first-token (TTFT) or latency in the early stages, while overlooking deeper evaluations that truly determine long-term success. In production environments, it’s not enough for models to respond quickly—they must […]

From GenAI Pilot to Production: Why 95% of Projects Fail—and How to Beat the Odds

GenAI pilots are proliferating across industries, yet advancing these initiatives into full-scale production remains a major challenge. A recent MIT study revealed that 95% of generative AI projects fail to move beyond the pilot stage. During the early stages, organizations often concentrate narrowly on model accuracy and performance. While these measures are important, they are […]

I Built BlazeText — It’s 10X Faster Than HuggingFace’s Tokenizer

A few weeks ago, while working on implementing a guardrail engine, I found myself staring at a performance graph that didn’t make any sense. Guardrail actions, like input sanitization, policy enforcement, hallucination checks, bias mitigation, audit logging: each layer adds complexity and latency. Left unchecked, those extra hops can nudge your p95 from tolerable to […]

Open Source Update : Bud Symbolic AI

This week we published a new open-source project — Bud Symbolic AI, an open-source framework designed to bridge traditional pattern matching (like regex and Cucumber expressions) with semantic understanding driven by embeddings. It delivers a unified expression framework that intelligently handles single words, multi‑word phrases, dynamic parameters, and context‑aware validation by leveraging FAISS for efficient […]

What’s New in LLM Inference Optimization: Recent Advances and Techniques

Large Language Models (LLMs) are resource-intensive. Open-source models like LLaMA 2, Mistral 7B, Falcon 40B, and others offer flexibility for deployment on cloud, edge, or on-premise setups. However, for cost-effective deployments, inference optimization is a necessity. This report surveys recent inference optimization methods and best practices, focusing on open-source LLMs. We cover techniques to reduce […]

A Survey of parallelism strategies that can deliver better efficiency for your GenAI deployments.

Generative AI unlocks incredible capabilities, but it doesn’t come cheap. Training and deploying large models like LLMs or diffusion models demand massive compute, making the total cost of ownership (TCO) a serious concern for teams building production-grade systems. To make GenAI cost-effective and scalable, you need to squeeze out every bit of performance from your […]

Product Update: Bud’s LLM Evaluation Framework 2.0

We have a major upgrade to our LLM Evaluation Framework — making it even more powerful, transparent, and scalable for enterprise AI workflows. As the adoption of LLMs accelerates, evaluating their performance rigorously and reliably across real-world tasks has never been more critical. Our new framework brings unprecedented flexibility and depth to benchmarking LLMs at […]

A Survey on LLM Guardrails: Part 2, Guardrail Testing, Validating, Tools and Frameworks

Part 1 : Methods, Best Practices and Optimisations Part 2: Guardrail Testing, Validating, Tools and Frameworks (This article) As large language models (LLMs) become more powerful, robust guardrail systems are essential to ensure their outputs remain safe and policy-compliant. Guardrails are control mechanisms (rules, filters, classifiers, etc.) that operate during deployment to monitor and constrain an […]

A Survey on LLM Guardrails: Part 1, Methods, Best Practices and Optimisations

Part 1 : Methods, Best Practices and Optimisations (This article)Part 2: Guardrail Testing, Validating, Tools and Frameworks As organizations embrace large language models (LLMs) in critical applications, guardrails have become essential to ensure safe and compliant model behavior. Guardrails are external control mechanisms that monitor and filter LLM inputs and outputs in real time, enforcing […]

Sovereign AI Framework for Developing Nations

The global AI landscape shows a significant gap in infrastructure between developed and developing countries. For instance, the United States has about 21 times more data center capacity than India. This research shows that software-based optimization strategies, architectural innovations, and alternative deployment models can greatly reduce reliance on large infrastructure. By analyzing current capacity data, […]

Automating License Analysis: A Small Feature That Solves a Big Problem

In the fast-moving world of Generative AI, where innovation often outpaces regulation, licensing has emerged as an increasingly critical—yet overlooked—challenge. Every AI model you use, whether open-source or proprietary, comes with its own set of licensing terms, permissions, and limitations. These licenses determine what you can do with a model, who can use it, how […]

Why Over-Engineering LLM Inference Is Costing You Big Money: SLO-Driven Optimization Explained

When deploying Generative AI models in production, achieving optimal performance isn’t just about raw speed—it’s about aligning compute with user experience while staying cost-effective. Whether you’re building chatbots, code assistants, RAG applications, or summarizers, you must tune your inference stack based on workload behavior, user expectations, and your cost-performance tradeoffs. But let’s face it—finding the […]

Introducing Bud Agent; An Agent to automate GenAI Systems Management

Beyond the high costs associated with adopting Generative AI (GenAI), one of the biggest challenges organizations face is the lack of know-how to build and scale these systems effectively. Many companies lack in-house AI expertise, cultural readiness, and the operational knowledge needed to integrate GenAI into their workflows. Based on a survey of over 125 […]

Why You Should Choose On-Prem Over Cloud for Your GenAI Deployments

Generative AI adoption is skyrocketing across industries, but organizations face a critical choice in how to deploy these models. Many use third-party cloud AI services (e.g. OpenAI’s APIs) where they pay per token for a hosted model, while others are investing in Private AI – running AI models on-premises or in hybrid private clouds. There […]

Introducing Hex-1: A Fully Open-Source LLM for Indic Languages

India, being one of the most linguistically diverse nations in the world, faces a major roadblock in harnessing the full potential of Generative AI. With only about 10% of the population fluent in English, the remaining 90% are effectively left behind—unable to engage with GenAI tools that are predominantly built for English-speaking users. Most leading […]

Introducing Bud SENTRY – Secure Evaluation and Runtime Trust for Your Models

Open-source large language models (LLMs) have become foundational to modern enterprise AI strategies. Their accessibility, performance, and flexibility make them an attractive choice for developers and businesses alike. However, as adoption grows, so does a quiet but serious threat: supply chain attacks via model downloads & execution. When you pull a model from Hugging Face […]

Optimising Cost Efficiency in LLM Serving Using Heterogeneous Hardware Inferencing

Summary: The current industry practice of deploying GenAI-based solutions relies solely on high-end GPU infrastructure. However, several analyses have uncovered that this approach leads to resource wastage, as high-end GPUs are used for inference tasks that could be handled by a CPU or a commodity GPU at a much lower cost. Bud Runtime’s heterogeneous inference […]

Exploring Transformed Multi-Head Latent Attention for Cost-Effective Enterprise GenAI

Deepseek’s latest innovation, R1, marks a significant milestone in the GenAI market. The company has achieved performance comparable to OpenAI’s o1, yet claims to have done so at a much lower training cost—a major breakthrough for the industry. However, with 671 billion parameters, R1 remains too large for cost-effective enterprise deployment. While impressive, such massive […]

SLMs fine-tuned like DeepSeek’s R1 + Bud Inference = Most Cost-effective Enterprise GenAI

The recent launch of DeepSeek’s R1 model has made waves in the AI industry—not just for its technological advancements but also for its wider market impact, including a drop in tech stock valuations. However, those who have been closely following the GenAI space knew this moment was inevitable. For the past one and a half […]

Introducing Maxwell TCS v0.2: A Lightweight SOTA Model for Prompt Complexity Scoring

We are excited to announce the open-source release of Maxwell Task Complexity Scorer v0.2, a breakthrough in efficient instruction complexity scoring. Maxwell represents a significant advancement in task complexity analysis, offering State-of-the-Art performance in a remarkably efficient package. Maxwell leverages a ModernBERT-Large backbone to deliver sophisticated complexity scoring while maintaining exceptional efficiency. With a dense […]

Adaptive Caching and Scheduling for Many-Adapter LLM Inference Environments

As organizations experiment with proof-of-concept and pilot projects for enterprise-grade Generative AI applications, the primary focus often remains on developing functionality rather than optimizing for operational efficiency. However, when transitioning from experimental phases to deploying production-ready GenAI applications, business leaders quickly realize that efficiency is paramount. This is because the total cost of ownership (TCO) […]

The Cost Conundrum Essays, Part 1 : The Goose Chase for Cost Effective LLMs

In recent years, Generative Large Language Models have become a centerpiece in the domain of NLP, catching the attention of researchers and non-researchers alike for their impressive capabilities. Their ability to capture context and generate human-like text has revolutionized the way we interact with machines, and opened up creative possibilities for applications in various domains, […]

How Enterprises That Are Serious About Their ESG Goals Should Approach GenAI Adoption

Environmental, Social, and Governance (ESG) goals have become a top priority for most large enterprises in recent years. Stakeholders, regulators, and consumers alike expect organizations to not only pursue profits but also demonstrate ethical, environmental, and social responsibility. In response, many companies have introduced a variety of initiatives—such as going paperless, planting trees, and implementing […]

Should You Replace Third-party LLM Services with Open Source SLMs? A Cost-Benefit Analysis

As artificial intelligence (AI) becomes an integral part of business operations, companies are increasingly leveraging powerful language models to create innovative products. Third-party LLM services, such as OpenAI’s GPT-4 and Claude, have become go-to solutions for many businesses, particularly for pilot projects and proof-of-concept initiatives. Their ease of use and fast implementation make them attractive […]

An Equitable Governance Framework For Balancing AI Innovation and Ethical Regulation

NOTE: This is an ongoing research and we invite fellow researchers to collaborate on this project. If you are currently working on a related topic or have a general interest in this field and would like to collaborate on this research, please get in touch with us via this form: Research Collaboration AI innovations have […]

Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models

As LLMs continue to grow, boasting billions to trillions of parameters, they offer unprecedented capabilities in natural language understanding and generation. However, their immense size also introduces major challenges related to memory usage, processing power, and energy consumption. To tackle these issues, researchers have turned to strategies like the Sparse Mixture-of-Experts (SMoE) architecture, which has […]

Fast yet Safe: Early-Exit Neural Networks with Risk Control for Optimal Performance

Large Language Models, with their increased parameter sizes, often achieve higher accuracy and better performance across a variety of tasks. However, this increased performance comes with a significant trade-off: inference, or the process of making predictions, becomes slower and more resource-intensive. For many practical applications, the time and computational resources required to get predictions from […]

LiveMind: Low-latency Large Language Models with Simultaneous Inference

In the rapidly evolving world of artificial intelligence, large language models (LLMs) are making headlines for their remarkable ability to understand and generate human-like text. These advanced models, built on sophisticated transformer architectures, have demonstrated extraordinary skills in tasks such as answering questions, drafting emails, and even composing essays. Their success is largely attributed to […]

Reducing LLM Ops Costs through Hybrid Inference with SLMs on Intel CPUs and Cloud LLMs

Despite the transformative potential of generative AI, its adoption in enterprises is lagging significantly. One major reason for this slow uptake is that many businesses are not seeing the expected ROI from their initiatives; in fact, recent research indicates that at least 30% of GenAI projects will be abandoned after proof of concept by the end of […]

Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting

In the research paper “Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting,” the authors introduce a new framework called Kangaroo designed to make large language models (LLMs) run faster. This framework enables the training of a smaller, lightweight model in a cost-effective way. This new framework is introduced to speedup the text generation process of […]