Solving the AI compute crisis: Why decentralization is the only way

AI has already changed the world. But, for it to reach its full potential, issues of accessibility and affordability need to be addressed. It needs to happen soon, before the industry leaves a huge swath of devs and builders from around the world behind. AI teams need infrastructure that allows them to get their product to market, not burn through their runway before they ever get off the ground. 

Models are growing exponentially. Llama 3.1 405B requires 16,000 H100 GPUs for training, GPT-4 takes an estimated 25,000 A100s, and Stable Diffusion 3 demands multi-GPU clusters just to fine-tune the model. Every AI company with a product roadmap is either training models, deploying inference at scale, or planning to do both. The thing is, none of them can get enough compute, and the hyperscalers (AWS, GCP, Azure) just can’t keep up with the demand. 

Right now, if you’re an AI startup, research team, or enterprise client, you’ll hit a series of speed bumps in provisioning your GPU. For one, you’ll likely spend 3-6 months on a H100, then wait weeks for quota approval, before getting hit with pricing that is 50-70% above market rates because demand outstrips supply by orders of magnitude. Remember, NVIDIA will ship millions of GPUs this year but there is still predicted to be a 20% + defecit.

This is what you call an AI compute crisis. And instead of easing up with all of the AI data center buildouts, the crisis is actually accelerating. 

Because more mega data centers aren’t a sustainable solution. The way out of this crisis is by decentralizing the entire AI stack. That is, aggregating underutilized GPUs from enterprise clusters, independent providers, and consumer hardware into a global, on-demand network that scales with demand instead of fighting it.

That's exactly what VOLT built.

Below, we’ll go into why our work building a decentralized GPU marketplace matters for startups and enterprises who are leveraging LLMs and AI research for products and operations. We’ll also explore where we think the industry is headed, and what sets VOLT apart in a space full of promises but painfully short on real, impactful delivery.

Demand vs. reality in the AI compute landscape

As we are currently seeing, demand for AI infrastructure is exponential, while supply has remained basically linear. With AI growth, the workloads are compounding. In just the last 24 months, we’ve seen:

• GPT-4 training runs cost $100 million in compute – Fine-tuning a 70B model takes 8 A100s for 72 hours. Inference is batch-based, while teams pre-provision capacity and hope usage doesn't spike.
• Foundation models routinely exceed 400B parameters – Fine-tuning is continuous, while RAG pipelines retrain daily on fresh data. Inference is real-time and agentic, with AI tools needing to burst from 10 requests/sec to 10,000 requests/sec without lag. Every SaaS product has an AI co-pilot that needs GPU capacity 24/7.

All of this results in AI teams needing 10x more compute than they did just two years ago. However, hyperscalers aren’t able to keep pace with GPU availability. 

The reason for this has nothing to do with chip fabrication and everything to do with infrastructure builds and deployment. Put another way, NVIDIA can ship GPUs faster than hyperscalers like AWS, GCP, and Azure can rack them, cool them, and make them available to customers.

The hyperscaler dilemma: quota approvals, waitlists, and monopoly pricing

If your AI startup, research team, or enterprise wants an H100 on AWS today, you’re in for a rude awakening. Here's what happens:

1. Submit a quota request – AWS requires manual approval for H100 instances. New accounts are automatically denied. As you might expect, enterprise customers with big budgets and spending history get priority. Your project will get stuck waiting. 
2. Wait 3-7 days (or longer) – Even after submitting your quota request, approval won’t be guaranteed. And even with approval, availability is region-locked, with H100s existing in only 2-3 US zones. Even worse, they're constantly sold out. 
3. Pay hyperscaler pricing – H100 80GB costs $5-7/hr on AWS, while an A100 80GB costs $4.55/hr. These prices are 50-70% above what independent GPU clouds like VOLT charge. AI teams pay this inflated price because there's no perceived alternative. 
4. Lock into 8-GPU minimums – AWS forces full-node reservations. So, if you need one H100 for a quick training job, too bad. You’ll be renting eight of them at $35-50/hr or the job doesn’t get done. 

GCP and Azure run the same playbook. These monopolistic business tactics have a very real impact: they create a market where compute access is rationed, pricing is inflated, and teams with urgent workloads have nowhere to go. Or so they think (more on this below). 

The neo-cloud solution: enterprise-only, limited scale

Neo-clouds like CoreWeave and Lambda Labs emerged to fill the hyperscaler gap. On balance, that’s been a good development, as they offer better pricing ($2.25-2.75/hr for H100s) and faster provisioning without quota approvals or waitlists for existing customers.

But neo-clouds have their own constraints:

• Enterprise-first models – CoreWeave's target customers are OpenAI, Mistral, and Anthropic; all teams spending millions per month. Small AI labs and startups just don't get priority access.
• Limited geographic reach – Most neo-clouds operate 2-4 US data centers. Teams in APAC, LATAM, and Europe face latency issues or can't access capacity at all.
• Capacity ceilings – When demand spikes (new model release, inference burst, seasonal load), even neo-clouds hit capacity limits.

Neo-clouds undoubtedly fixed aspects of the hyperscaler dilemma. But in the final analysis, they're still centralized infrastructure fighting the same supply problem. Quite simply, they can't scale fast enough to meet the market.

Why decentralization Is the only solution that scales

Centralized infrastructure, both hyperscalers and neo-clouds, is inherently supply-constrained. Every GPU has to be purchased, racked, cooled, and maintained in a data center before it can serve workloads. That process takes months and even years. Compute demand grows faster than centralized providers can possibly deliver it. 

Decentralized compute flips the model entirely. Instead of building new data centers, decentralized compute marketplaces aggregate underutilized GPUs that already exist, and make it available to the AI startups and enterprises who need it.

Where the supply actually is

Millions of GPUs are sitting idle right now:

• Enterprise clusters – Companies bought 8x H100 nodes for a training job that finished. The hardware is paid for, racked, and powered but it's sitting idle 60-80% of the time. Renting it out generates ROI on sunk capital.
• Independent data centers –  Smaller providers have A100s, H100s, and L40s that aren't running at full utilization. They want to monetize that capacity but don't have the sales infrastructure to reach customers.
• Consumer hardware – Gamers and enthusiasts own RTX 4090s that sit idle when not gaming. These GPUs deliver 80% of A100 performance for inference and fine-tuning at 10% of the cost.

This is what’s known as “latent supply”, and you won’t see it listed on AWS, GCP, or Azure. It's also not in CoreWeave’s or Lambda Labs’ data centers. But it's real, it's distributed globally, and it's underutilized.

A decentralized network turns it into on-demand compute.

Why decentralized networks scale better than hyperscalers

Decentralized GPU networks aren’t engaged in fighting supply constraints. What they do is aggregate existing supply and scale with demand, so that AI teams and enterprises experience:

• No infrastructure lag – Adding capacity to a decentralized network takes mere days instead of many months. Suppliers connect hardware, pass verification, and start serving workloads. Even better, there is no need to construct new data centers with 12-24 month buildouts.
• Geographic diversity – Decentralized supply is by its nature distributed. GPUs come online in North America, Europe, APAC, and LATAM simultaneously, giving customers low-latency access no matter where they are in the world.
• Market-driven pricing – While centralized providers set prices, decentralized GPU networks let supply and demand find equilibrium. This results in 35-70% cost savings versus hyperscalers, all without sacrificing performance.

In the decentralized GPU market model, we no longer need to think about how fast we can build data centers. The only real concern is how quickly we can onboard new GPU suppliers. What this does is create a market that can scale exponentially, which is something centralized models simply cannot do. 

Where VOLT fits: the most used decentralized GPU network

VOLT is the execution of this idea at scale. As the most used decentralized GPU network in production, VOLT serves real enterprise workloads with verifiable on-chain revenue.

Here’s what that network looks like today:

• Thousands of GPUs online across 138+ countries – H100 SXM, A100 80GB, RTX 4090, and L40S GPUs, giving customers enterprise and consumer hardware distributed globally.
• Instant availability – No quota approvals or waitlists. Your team simply spins up a GPU cluster in under two minutes and gets rolling.
• 35-70% cost savings vs AWS/GCP/Azure – H100 at $1.49-2.20/hr (vs $4.99-6.98/hr). A100 at $2.30/hr (vs $4.55/hr). RTX 4090 at $0.28/hr (not available on hyperscalers).
• Real enterprise revenue – Real companies, contracts, and compute demand, with revenue completely verifiable on-chain.

VOLT has no interest in competing with hyperscalers by building centralized data centers. Instead, our decentralized GPU marketplace is outcompeting hyperscalers by making underutilized GPUs accessible, affordable, and reliable enough for production workloads.

How VOLT sets itself apart from hypersclaers  

1. Single-GPU access – AWS and GCP force 8-GPU minimums, while VOLT lets teams rent exactly what they need, whether it’s one H100 for a quick fine-tune, three A100s for a prototype, or ten RTX 4090s for inference. Pay for what you use, not what the hyperscaler makes you reserve.
2. Consumer GPU support – RTX 4090s deliver 80% of A100 performance at 20% of the cost, yet hyperscalers and neo-clouds don't offer them. VOLT does. Why? Because decentralized supply includes consumer hardware that centralized providers can't access.
3. Verifiable revenue and sustainable tokenomics – Every dollar of compute revenue is recorded on-chain. Token burns are tied to actual network earnings through the Incentive-Driven Emissions (IDE) model. You don't have to trust VOLT's numbers because you can verify them.
4. Confidential Compute for regulated industries – Healthcare, finance, and legal teams need GPU access but can't expose sensitive data. VOLT's Confidential Compute offering provides encrypted enclaves, secure boot, and verifiable computation. 
5. Truly global reach – Thousands of GPUs distributed across 138+ countries. Teams in São Paulo, Singapore, and Berlin get the same low-latency access as teams in San Francisco. Hyperscalers can't match this without building regional data centers, which is a process that takes years.

What sets VOLT apart in the Decentralized Compute space

VOLT isn't the only decentralized GPU project. But it's the only one with this combination of scale, revenue, and product maturity.

Most used: production workloads, not testnet experiments

Most decentralized compute projects are early-stage with testnet GPUs, limited availability, and pilot customers. VOLT is the most used decentralized GPU network serving real enterprise workloads at scale.

Real revenue: verifiable on-chain vs. whitepaper promises

VOLT is a real business instead of speculative. That means actual companies pay for compute with that revenue recorded on-chain. Tokens are burned based on actual network earnings. The IDE dashboard is open to the public so that anyone, anywhere can verify the numbers.

Compare this to projects with roadmaps and token launches but no customers. VOLT already has product-market fit. Customers never have to ask the question "will this work?". It already does. So, the only real question your AI startup or enterprise should ask is this: "how fast can we scale?"

Product maturity: fully managed platform vs. bare-metal SSH access

Early decentralized networks made users SSH into raw GPU nodes and manage everything manually. VOLT ships a fully managed platform:

• Deploy Docker containers with one command
• Launch distributed training jobs with automated orchestration
• Spin up inference endpoints with load balancing and autoscaling
• Monitor jobs, track costs, and scale capacity from a web dashboard

Teams go from account creation to running their first GPU job in under 20 minutes. You get to skip DevOps and manual configuration. It’s AI compute that just works.

Trust and verification: transparent burns, open data, no black boxes

DePIN projects often ask users to trust their claims about total GPUs, network revenue, and token utility. VOLT makes everything verifiable:

• Revenue is on-chain – Every compute job generates a transaction and network earnings are public.
• Burns are transparent – The IDE model ties token burns to revenue. The burn wallet is public. The smart contracts are deployed. Track it in real time at ide.buildonvolt.com.
• GPU availability is provable – You can query the network and see exactly what's online, including H100 clusters, A100 pods, and RTX 4090 nodes.

Independently verifying infrastructure matters when you're scaling from prototype to production and need to know the network will still be here in 12 months.

Why this matters for AI teams

If you're training models, deploying inference, or building AI products, you’ve already seen a compute landscape in 2026 that is absolutely broken. Hyperscalers ration access, neo-clouds prioritize enterprise customers, and costs are terribly inflated. The only certainty is that availability is uncertain.

VOLT gives you an alternative that demonstrably works:

• Get GPU access in minutes, not months – Provision a GPU cluster and get your job running in minutes without quota approvals or lengthy waitlists. 
• Pay 35-70% less than AWS, GCP, or Azure – Same GPUs, same performance, with transparent pricing. You get market-driven rates instead of monopoly markups.
• Scale up or down instantly – Need one GPU for a prototype? Rent one. Need 100 GPUs for a training run? Spin them up. On VOLT, you’ll never have to deal with reservations or minimums. Simply pay for what you use. It’s that simple. 
• Run production workloads with confidence – We offer verifiable infrastructure and transparent tokenomics for production-grade compute that serves real companies.

The hyperscaler era is plateauing. The teams that win in AI will be the ones that are savvy enough to know they don’t have to wait 6 months for an H100 allocation or pay $7/hr when the market rate is $2.

What this means for the compute industry

Beyond individual teams, VOLT proves a bigger thesis: decentralized infrastructure can outcompete and outpace centralized incumbents on cost, availability, and transparency.

If VOLT keeps succeeding, the model expands, with other compute-intensive industries (rendering, scientific computing, genomics) adopting decentralized networks. This will break the hyperscaler monopoly, transforming infrastructure into  an actual market instead of a cartel.

That's the long-term vision. It starts with solving the current AI compute crisis. And VOLT, as the most used decentralized GPU network, has the scale, revenue, and product maturity to do it.

What comes next

VOLT is already the most used decentralized GPU network, so the next phase is about deepening that lead. We’ll be bringing even more capacity online, expanding geographic coverage, and supporting next-gen hardware (H200, GB200, Blackwell chips) as it becomes available.

The compute crisis isn't going to magically disappear, even with more AI data center buildouts. As AI workloads keep growing, models will keep scaling and hyperscalers just won’t be able to keep up.

But decentralized networks will. And VOLT is already leading the way.