Stories about building the worldwide compute grid

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AI Inference

GPU Optimization in Kubernetes: How to Get More From Every GPU

GPU Optimization in Kubernetes: How to Get More From Every GPU

GPU demand doesn't behave like traditional CPU demand. When an expensive GPU is allocated to a workload that only uses a small portion of its compute or memory, the unused capacity becomes a recurring cost. This is why GPU optimization in Kubernetes is becoming less about simply monitoring utilization and more about how GPUs are allocated, shared, scheduled and automatically scaled.

Vishwas Narayana

AI Inference

GPU Net in Ghost Mode: What AI Infrastructure Needs to Run Efficiently at Scale

GPU Net in Ghost Mode: What AI Infrastructure Needs to Run Efficiently at Scale

AI infrastructure is entering a phase where simply having enough GPUs is no longer the main challenge. The harder question is whether those GPUs are actually doing useful work. Ghost Mode focuses on the capacity that is present but not producing enough useful work — and what power, cooling, networking, batching, partitioning, profiling and autoscaling can do about it.

Vishwas Narayana

AI Inference

What Is GPU.net? A Practical Overview of the Decentralized GPU Network

What Is GPU.net? A Practical Overview of the Decentralized GPU Network

GPU.net is a real, operating project, distinct from the generic GPU cost optimization concepts covered elsewhere in this series. This piece is a factual overview of what it is, how it's structured — node network, GAN Chain, the $GPU token, the marketplace, the enterprise offering and Subnets — and what to weigh before treating it as infrastructure.

Vishwas Narayana

More Stories

AI Inference

Running GPU Workloads on Kubernetes: Where Networks Like GPU.net Fit In

Running GPU Workloads on Kubernetes: Where Networks Like GPU.net Fit In

Kubernetes was never really designed with GPUs in mind. It gives you placement, not insight — it'll happily schedule eight GPU replicas for you, but it has no opinion on whether you needed eight. That gap, between what Kubernetes manages and what it actually understands about GPU usage, is exactly where a layer like GPU Net fits. This is Part 4 of the Core GPU Cost Series.

Vishwas Narayana

AI Inference

Measure Before You Scale

Measure Before You Scale

Your proof of concept passed. The natural next move is to scale up — more GPUs, bigger model, longer runs. Don't. Not yet. The fix for a compute-bound workload and the fix for an I/O-bound one are almost opposite, and scaling the wrong axis just buys you a bigger version of the same bottleneck. This piece is about the metrics that tell you which one you have.

Vishwas Narayana

AI Inference

Start With No GPU

Start With No GPU

The most expensive GPU in your fleet is often the one that was never needed in the first place. A surprising amount of an ML workload's lifecycle — the part where bugs actually get found — is CPU work wearing a GPU costume. This piece is a practical guide to validating on CPU first, and knowing exactly where that stops working.

Vishwas Narayana

AI Inference

The Human-in-the-Loop Approach

The Human-in-the-Loop Approach

Most GPU cost overruns aren't the result of a bad decision. They're the result of no decision at all — spend that happens by default because nobody was explicitly asked to approve it at the moment it mattered. The fix isn't more process for its own sake. It's putting a human judgment call at the specific points where GPU spend is about to change in kind, and automating execution, not judgment.

Vishwas Narayana

AI Inference

GPU Net in Ghost Mode: How to Get More Inference From Every GPU

GPU Net in Ghost Mode: How to Get More Inference From Every GPU

Running an AI model in production is very different from running it in a notebook. How many requests can one GPU handle? Why is an expensive GPU sitting idle? How much are we actually paying for every inference? Ghost Mode is about making the invisible gaps between allocated and consumed GPU capacity visible, and turning unused capacity into useful inference.

Vishwas Narayana

AI Inference

Why GPU Development Gets Expensive

Why GPU Development Gets Expensive

If you've ever budgeted for a GPU-heavy project — training a model, running inference at scale, or just standing up a research cluster — you've probably noticed the number on the invoice rarely matches the number you expected. GPU costs don't creep; they compound. Here's where the money actually goes.

Vishwas Narayana

AI Inference

The GPU Bill Nobody Planned For

The GPU Bill Nobody Planned For

Every infra team has had this moment: someone opens the cloud bill, lands on GPUs, and stops scrolling. The number is bigger than anyone remembers approving. This is the GPU bill nobody planned for - and it's not a budgeting failure, it's a visibility failure. Here's how ghost GPUs hide behind aggregate utilization, and how GPU Net turns them into capacity you can actually recover.

Vishwas Narayana

Community Program

Compute Is Already an Asset Class. Tokenization Decides Who Gets to Own It.

Compute Is Already an Asset Class. Tokenization Decides Who Gets to Own It.

Wall Street spent three years quietly rebuilding GPUs into investment-grade collateral. Tokenization is the layer that decides whether you're on the cap table or watching from outside and GPUnet's RWA Pool puts real GPU hardware within reach.

GPUNET

AI Inference

Tenstorrent in the Real World: Benchmarks, Customers, and the Inference Bet That's Starting to Pay Off

Tenstorrent in the Real World: Benchmarks, Customers, and the Inference Bet That's Starting to Pay Off

Across five pieces, we have built up a picture: an inference market growing from USD 106 billion in 2025 to a projected USD 255 billion by 2030; a NVIDIA-dominated landscape with CUDA lock-in and pricing pressure; an AMD that has caught up on silicon but still trails on software; and a Tenstorrent that is betting on architectural divergence to break the GPU paradigm for inference. The question for this final piece is the only one buyers actually care about: is it working? What do the published benchmarks say, who is buying or licensing, and what is the real-world impact on user-facing inference workloads? This is the closing Part 6 of the AI Inference Hardware series.

Yashwanth

AI Inference

NVIDIA vs AMD vs Tenstorrent: An Architectural Deep Dive on Inference

NVIDIA vs AMD vs Tenstorrent: An Architectural Deep Dive on Inference

This piece tries to do the most: line up NVIDIA, AMD, and Tenstorrent side by side at the architectural level - execution model, memory hierarchy, interconnect, software stack, and the strategic shape of each company's bet - and explain why Tenstorrent's choices, while less proven, are particularly well-fitted to the direction inference workloads are heading. The question is not 'which is best today' but 'which architectural lineage is best matched to where inference is going, and why does that matter for buyers and operators?' This is Part 5 of the AI Inference Hardware series.

Yashwanth

AI Inference

Inside the Tenstorrent Chips: Grayskull, Wormhole, Blackhole, and Galaxy

Inside the Tenstorrent Chips: Grayskull, Wormhole, Blackhole, and Galaxy

A company can have the most compelling thesis in the world, but it lives or dies on the silicon. This piece walks through the actual Tenstorrent chips - what they are, what they cost, and what the published, mostly open-source benchmarks say about how they stack up against NVIDIA and AMD. We cover the full product ladder from Grayskull to Galaxy Blackhole, explain what makes a Tensix core architecturally different, and walk through the published benchmarks honestly - including where the numbers come from and what caveats apply. This is Part 4 of the AI Inference Hardware series.

Yashwanth

AI Inference

Tenstorrent: The Jim Keller Bet to Break NVIDIA's Inference Monopoly

Tenstorrent: The Jim Keller Bet to Break NVIDIA's Inference Monopoly

If you are going to challenge NVIDIA, the first question is: what could you possibly be doing differently? CUDA has 18 years of compounding investment. The H100 to B200 to Rubin roadmap is on track. AMD is the closest thing to a credible second source and is still climbing. Tenstorrent's answer is not 'do GPUs better.' It is 'GPUs are the wrong architecture for what's coming, the software is closed, and the moat is built on a moat - let's pull every leg out at once.' This is Part 3 of the AI Inference Hardware series, walking through the Jim Keller pedigree, the four-part architectural thesis, the chip-and-IP hybrid business model, and the sovereign AI play.

Yashwanth

AI Inference

The Real Cost of AI Inference in 2026: A Practical Breakdown

The Real Cost of AI Inference in 2026: A Practical Breakdown

If you take one thing from this piece, it should be this: the headline GPU hourly rate is almost never the right number to optimize. What matters is cost per million tokens, and that number is shaped by three multipliers - the GPU itself, the serving stack (vLLM, TensorRT-LLM, SGLang), and the precision and batching strategy you run. Get all three right and the same workload that costs USD 5 per million tokens can cost USD 0.20. This is Part 2 of the AI Inference Hardware series, walking through real cloud GPU rates, the cost-per-million-tokens math, the serving stacks that actually move the needle, and the self-host vs API breakeven point in 2026.

Yashwanth

AI Inference

The Inference Era: How NVIDIA and AMD Are Fighting for the Next AI Goldmine

The Inference Era: How NVIDIA and AMD Are Fighting for the Next AI Goldmine

For most of the AI boom so far, the headlines have belonged to training. But somewhere between GPT-4 and the agent-driven applications now shipping inside every product team's roadmap, the center of gravity quietly shifted. Training is still expensive, but inference - actually running those models in production, every second, against real user traffic - is where the money is now being spent. And it is where the hardware fight is heating up the fastest. This piece is Part 1 of a 6-part deep dive into the inference hardware landscape, walking through the market shape, NVIDIA's dominance, AMD's catch-up, and the cracks in the GPU paradigm that are opening doors for challengers.

Yashwanth

Tutorials

GPU.NET and AI Agents: Revolutionizing Decentralized Computing

GPU.NET and AI Agents: Revolutionizing Decentralized Computing

In an era where AI is reshaping industries, GPU.net emerges as a key player harnessing decentralized GPU resources to power innovative applications. At the core of this ecosystem are Subnets, specialized networks that leverage global GPU power for tasks like AI training, simulations, and creative projects.

Surya

Tutorials

Trading Guide: How to Buy & Sell on GVEX — The Liquidity Layer for GPUNET

Trading Guide: How to Buy & Sell on GVEX — The Liquidity Layer for GPUNET

GVEX is the native trading platform within the GPUNET ecosystem — designed to connect liquidity with compute infrastructure.

Djal

Tutorials

GPUNET Verifiable Exchange: The Next Frontier for $GPU, Nodes and Ecosystem [TEASER]

GPUNET Verifiable Exchange: The Next Frontier for $GPU, Nodes and Ecosystem [TEASER]

With the arrival of GVEX, the protocol takes a major step forward: Node holders can fully leverage trading capability on their operation business, by opting to buy Nodes from market and structure a stackable Node operations business, that's one of the perk for holding a Node.

Djal

Tutorials

State of GAN Chain - Pragmatic roadmap to everything GPU

State of GAN Chain - Pragmatic roadmap to everything GPU

At the heart of GAN Chain lies a simple design philosophy: growing bandwidth demands must always be balanced by the chain’s built-in economic value. The architecture is engineered to make GPU compute both verifiable and rewarding, with every layer reflecting this commitment.

DJAL

Tutorials

GPU Quest: Road to TGE

GPU Quest: Road to TGE

GPU.net is advancing decentralized computing by enabling GPU resource sharing, and its upcoming Token Generation Event (TGE) marks a significant milestone. The “Road to TGE” campaign on token.gpu.net provides a structured way for participants to earn rewards and engage with the project. This overview explains the campaign, its components, and how you can get involved in a professional and straightforward manner.

Surya Ranjith

Tutorials

GPU SUBNETS - A New Era

GPU SUBNETS - A New Era

In a world where centralized GPU computing is expensive and restrictive, Subnets on GAN Chain offer a decentralized revolution. By connecting creators, users, and investors to a global pool of GPU resources, Subnets deliver affordable, scalable, and community-governed computing power. With tools to simplify project deployment, incentives for participation, and AI-optimized resource allocation, GPU.NET is not just solving today's GPU challenges — it's building the future. Join the movement: create, innovate, and grow with Subnets on GAN Chain.

Surya Ranjith

Tutorials

GPUNET Q1 2025: Dev Updates, Product Progress & Ecosystem Recap

GPUNET Q1 2025: Dev Updates, Product Progress & Ecosystem Recap

As Q1 2025 wraps up, here’s a comprehensive recap of everything we’ve built, shipped, and grown at GPUNet—from the core protocol to the community layer.

Ivish Sheldon

Tutorials

How to rent GPU's on our dApp

How to rent GPU's on our dApp

Seamlessly access high-performance GPUs for your AI, rendering, and compute-intensive tasks. Simply browse available nodes, choose the best fit for your needs, and start renting—all on a decentralized, secure platform. Experience cost-effective and scalable computing power like never before!

Ganesh Hegde

Provider Guide

Complete Guide on running a GPU Provider Nodes

Complete Guide on running a GPU Provider Nodes

This guide aims to minimize the friction in using documentation, providing you with a streamlined approach to set up your Provider GPU node. We'll walk you through the essential steps, ensuring you gather all the correct procedures effortlessly. With this guide, you'll have a clear path to running your Provider node efficiently. Let's dive into the steps and make the setup process as smooth as possible.

DJAL

Validator Guide

Complete Guide on running a GPU Validator Node

Complete Guide on running a GPU Validator Node

This guide aims to minimize the friction in using documentation, providing you with a streamlined approach to set up your validator GPU node. We'll walk you through the essential steps, ensuring you gather all the correct procedures effortlessly. With this guide, you'll have a clear path to running your validator node efficiently. Let's dive into the steps and make the setup process as smooth as possible.

DJAL

Community Program

Large Multimodal Models (LMMs) vs Large Language Models (LLMs)

Large Multimodal Models (LMMs) vs Large Language Models (LLMs)

Large multimodal models (LMMs) are a big change because they can handle different types of data like text, images, and audio. But they are complex and need a lot of data, which can be tricky at times. From the start, it was evident that AI would need to be multifunctional and serve as a single platform for various purposes, and LMM exactly is that.

Sujal Sripathi

Community Program

GPUNET Community Growth Program! ⚡

GPUNET Community Growth Program! ⚡

We’re excited to expand our community with all of our Guardians! With over 12,000 members already on Discord, your dedication means a lot to us. To keep this momentum going, we’re launching a Community Growth Program. Join our Discord Growth Sprint to share rewards worth 200 USDT.

Sujal Sripathi

HPC

Next-Gen GeForce RTX 50 ‘Blackwell’ Lineup Details Released

Next-Gen GeForce RTX 50 ‘Blackwell’ Lineup Details Released

Next year, AMD, Intel, Nvidia and other major brands are rolling out a slew of new chips. Big label chips with drastic improvements in architecture from it’s previous successors such as Blackwell from Nvidia, this jump is straight to the moon.

Sujal Sripathi

HPC

Supercomputing and High-Performance Computing: Understanding the Differences

Supercomputing and High-Performance Computing: Understanding the Differences

There is plenty of discussion about High Performance Computing (HPC) these days, especially because the demand for AI clusters has surged, leading to a greater emphasis on top notch computing power. For a long time, high performance computing has shown it can accurately model and predict many physical properties and events. Such performance have deeply impacted our world, helping create wealth and enhancing our quality of life.

Sujal Sripathi

NLPs

Understanding BERT: A State of the Art Model for NLP Using Deep Bidirectional Transformers

Understanding BERT: A State of the Art Model for NLP Using Deep Bidirectional Transformers

BERT recently got popular after its debut in 2018, courtesy of Google AI Language, short for Bidirectional Encoder Representations from Transformers. This new tool has become super important in the world of AI, especially for understanding human language. It’s like having a Swiss army knife for language related challenges, capable of handling tasks ranging from understanding sentiments in text to identifying important names and phrases.

Sujal Sripathi

Community Program

Assessing Large Language Models for Program Synthesis

Assessing Large Language Models for Program Synthesis

Can big computer programs make new ones? Some experts think they can, especially the really big ones. These programs are great at understanding language and creating complex computer code. People who know a lot about coding are impressed because these programs can write difficult programs easily. It shows how smart computers have become at understanding language and making new things with it. This is where prompt engineering comes in. Engineers use special instructions or prompts to help these programs learn to do cool things like creating new computer programs. By guiding them with exact directions, engineers make sure these programs can comprehend & write complex code right.

Sujal Sripathi
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