AI is more accessible than ever, but scaling it isn’t cheap. For startups, GPU rentals offer a smarter path to performance without the upfront cost. Enter GPU rentals—a game-changing solution that's reshaping how businesses approach AI development. The GPU Dilemma: Buy vs. Rent When launching an

In today’s AI landscape, performance isn’t just about better algorithms—it’s about better hardware. GPUs (Graphics Processing Units) have become the core of modern AI infrastructure, powering everything from image generation to training massive language models. But if you're not a developer or cloud engineer,

Indie developers, open-source contributors, and lean teams are driving AI innovation at a pace that once seemed unthinkable. They’re not waiting on billion-dollar labs — they’re building powerful models, tools, and real-world applications from scratch. What gives them the edge? And why should platforms like Nebula Block care? Why

Meta’s LLaMA 3.3-70B stands out as one of the most powerful open-source language models to date — blending world-class reasoning, speed, and scalability with a permissive license for commercial and research use. In this quick-start guide, we’ll show you how to use LLaMA 3.3-70B on Nebula Block’

As AI systems scale, choosing the right infrastructure—centralized or decentralized—significantly impacts efficiency, security, and adaptability. While centralized architectures dominate enterprise AI workloads, decentralized AI is gaining traction for its privacy benefits and fault tolerance. This article explores key trade-offs and how Nebula Block’s flexible AI platform bridges

Natural Language Processing (NLP) has undergone significant transformations over the years, with a key transition from traditional statistical methods to embedding-based models. This shift has dramatically improved AI efficiency, enabling more sophisticated language understanding, contextual analysis, and downstream task performance. Let's explore the differences between these approaches and

The Evolution of AI Prompting Prompt engineering has become a critical factor in optimizing large language models (LLMs). As AI adoption grows, developers face challenges in structuring prompts effectively to ensure consistent, high-quality responses. Meta’s latest release, Llama Prompt Ops, addresses this issue by automating prompt optimization for Llama

AI’s Open-Source Shift The AI revolution is no longer confined to massive research labs—open-source models are reshaping the landscape. From independent developers fine-tuning models to enterprise-grade deployments, innovation is accelerating at an unprecedented pace. Nebula Block bridges the gap between raw open-source potential and production-ready AI. It offers

Introduction Diffusion large language models (LLMs) like LLaDA offer parallel text generation but suffer from slow inference due to lacking key-value (KV) caching and quality issues in parallel decoding. Fast-dLLM, a training-free framework introduced by NVIDIA, revolutionizes Diffusion LLMs by introducing KV caching and parallel decoding, significantly boosting throughput and

Introduction AI search engines demand low-latency, context-aware results, but high compute costs and infrastructure complexity often hinder performance. Sentence embeddings, which transform text into semantic vectors, enable smarter search by understanding user intent. Nebula Block’s serverless platform, powered by NVIDIA H100/H200 GPUs, delivers cost-efficient, scalable infrastructure, saving 30-70%

Introduction NVIDIA’s recent release of Llama Nemotron Nano VL marks a pivotal advancement in vision-language models optimized for document understanding. While Nemotron Nano VL sets new benchmarks for extracting complex data from PDFs, diagrams, and tables, emerging trends in AI point toward compact, energy-efficient models that empower real-world applications

Introduction Smart cities rely on real-time IoT data to optimize everything from traffic lights to air quality. Yet the reality is, traditional cloud solutions struggle to keep up—high compute costs and latency issues limit scalability. For example, Montreal’s traffic optimization initiative faces these exact challenges. Nebula Block solves

Introduction As machine learning (ML) workloads scale, GPUs like NVIDIA’s H100 have set the standard for training and inference. However, emerging AI accelerators—ASICs, FPGAs, photonic, and neuromorphic chips—are reshaping ML infrastructure with specialized performance and efficiency. Nebula Block’s serverless platform, powered by H100/H200 GPUs, offers

Introduction: The Challenge of Model Training Training large-scale AI models is both compute-intensive and time-consuming. For many research labs, the prolonged training cycles obstruct rapid innovation and delay product iterations. Enter Nebula Block, a serverless GPU cloud platform built to revolutionize AI compute. By leveraging high-end NVIDIA GPUs and dynamic

Introduction: Scaling NLP with Nebula Block High-volume Natural Language Processing (NLP) applications, such as semantic search or real-time chatbots, demand fast, scalable embedding generation. Nebula Block’s Embedding API, powered by NVIDIA GPUs and serverless architecture, delivers up to 30% cost savings while handling millions of requests daily. This guide