The OpenAI-NVIDIA Dynamic
The relationship between OpenAI and NVIDIA is one of the most consequential partnerships in technology. OpenAI, the company behind ChatGPT and GPT-4, is one of NVIDIA’s largest single consumers of GPU hardware. Training frontier language models requires tens of thousands of GPUs running continuously for months — and for most of AI’s history, that has meant NVIDIA GPUs running on CUDA.
But the relationship is evolving. OpenAI has been reported to be developing custom in-house AI chips to reduce dependency on NVIDIA. A reported $100 billion partnership deal between the two companies reportedly fell through. Meanwhile, NVIDIA faces increasing competition from AMD and custom silicon, while OpenAI explores alternative hardware for the growing inference portion of its compute needs.
Understanding this dynamic matters because what happens between AI’s largest consumer and its primary hardware supplier ripples across the entire GPU compute market — affecting prices, availability, and the economics of GPU rental for everyone else.
How OpenAI Uses NVIDIA GPUs: Scale and Architecture
OpenAI’s compute requirements are staggering in scale:
- GPT-4 training reportedly used 10,000+ NVIDIA A100 GPUs running for months
- GPT-5 and beyond are expected to require even larger clusters — potentially 50,000–100,000+ H100 or B200 GPUs
- ChatGPT inference serves hundreds of millions of users, requiring thousands of GPUs running 24/7 for real-time token generation
- Total compute spend is estimated in the billions of dollars annually
This scale of consumption has several market effects. First, it concentrates a significant share of NVIDIA’s production output in the hands of a single customer. Second, it creates a reference architecture — what OpenAI uses sets the standard for the industry. Third, it creates a dependency that both companies are actively trying to manage.
NVIDIA benefits from OpenAI’s volume purchases but risks over-reliance on a single customer. OpenAI benefits from NVIDIA’s hardware but faces pricing power and supply allocation challenges. This tension drives both companies toward diversification.
The Training vs. Inference Compute Split
The most important structural shift in AI compute is the transition from training-dominated to inference-dominated workloads.
According to Deloitte’s TMT Predictions 2026, inference now accounts for roughly two-thirds of all AI compute, up from one-third in 2023. This inversion has profound implications:
Training is concentrated. Training a frontier model requires thousands of GPUs communicating at high speed via NVLink and InfiniBand within a single cluster. Only hyperscalers and a few large AI labs have clusters of this size. Training demand concentrates at the top of the market.
Inference is distributed. Serving a model to users does not require a massive cluster — it requires many individual GPUs or small GPU groups processing requests independently. Inference workloads can run across distributed data centers, on different GPU models, and through marketplace platforms that aggregate capacity from hundreds of providers.
This shift benefits the broader GPU rental market. As inference grows, more of AI’s compute demand can be served by distributed GPU capacity — exactly what GPU marketplaces provide. The days when “AI compute” meant “one giant cluster” are giving way to a more distributed model.
Impact on GPU Rental Rates and Availability
The interplay between massive AI spending and increasing GPU supply is reshaping rental economics.
Prices Are Falling
GPU rental prices have dropped significantly. H100 cloud pricing fell 44% between June 2024 and June 2025 as supply expanded and competition intensified:
| Period | H100 Typical Rate | Change |
|---|---|---|
| Mid 2024 | $4.00–$8.00/hr | Peak pricing |
| Late 2024 | $3.00–$6.00/hr | Supply expanding |
| Mid 2025 | $2.00–$4.00/hr | Marketplace competition |
| Early 2026 | $1.49–$3.90/hr | Current market |
The price decline is driven by three forces: NVIDIA increasing H100 production, AMD adding competitive capacity (MI300X, MI355X), and GPU marketplaces aggregating distributed supply that previously sat idle.
Supply Remains Tight for Training
Despite falling prices for individual GPU instances, bulk allocation for large training clusters remains constrained. Organizations seeking 1,000+ H100s for continuous training face:
- 6+ month lead times for dedicated clusters
- Premium pricing for guaranteed, long-term allocations
- Competition from hyperscalers who prioritize their own AI services
This two-tier market — abundant spot/marketplace supply for inference, constrained supply for large-scale training — reflects the training-to-inference shift. Inference GPUs are easier to provision because the workload is distributed. Training clusters require physical co-location and high-speed interconnects, creating a supply bottleneck.
The HBM Supply Constraint
High-bandwidth memory (HBM) remains the critical bottleneck in GPU production. HBM costs rose over 30% in Q4 2025, with additional 20% increases in early 2026. SK Hynix, Samsung, and Micron — the three HBM manufacturers — are all running at maximum capacity. Relief is not expected until late 2027 at the earliest.
This memory bottleneck constrains total GPU production regardless of NVIDIA’s or AMD’s chip manufacturing capacity. Even with sufficient GPU dies, the lack of HBM limits how many complete data center GPUs can be assembled.
Broader Market Implications: Who Benefits, Who Struggles
The OpenAI-NVIDIA dynamic and the broader AI infrastructure buildout create distinct winners and losers across the GPU ecosystem.
Who Benefits
GPU marketplace providers. The inference shift means more compute demand can be served by distributed GPU capacity. Marketplaces that aggregate supply from many providers are well-positioned to capture this growing segment — offering lower prices than hyperscalers through competitive supply dynamics.
Hardware owners with idle GPUs. As inference demand grows and marketplace infrastructure matures, hardware owners can monetize idle GPU capacity more effectively. Even consumer GPUs (RTX 4090, RTX 5090) are useful for inference workloads that do not require data center-grade hardware. For a guide to renting out your GPU, see our passive income guide.
Cost-conscious AI teams. Falling rental prices and expanding marketplace supply mean AI teams at startups, research labs, and universities can access more compute for less money. An H100 that cost $8/hr in 2024 now costs $1.49–$3.15/hr — a 50–80% cost reduction in under two years.
AMD. The inference shift plays to AMD’s strength. Inference workloads are less dependent on CUDA and NVLink than training, reducing NVIDIA’s ecosystem moat. AMD’s competitive pricing and growing ROCm support make it an increasingly viable alternative for inference-focused teams. For a detailed comparison, see our NVIDIA vs AMD analysis.
Who Struggles
Small companies competing for training clusters. Hyperscalers and large AI labs absorb the majority of high-end GPU allocation. Smaller companies face longer wait times and higher prices for dedicated training capacity.
GPU owners banking on stable pricing. Rental rates are falling as supply expands. Hardware purchased at 2024 prices is depreciating faster than historical norms as cheaper rental alternatives emerge.
NVIDIA’s pricing power (marginally). More competition from AMD, custom chips (Google TPU, custom silicon from OpenAI and Meta), and distributed marketplaces creates gradual downward pressure on NVIDIA’s premium pricing. NVIDIA remains dominant, but its ability to charge 3–5x premiums over cost is being tested.
Practical Actions for GPU Renters and Providers
If You Rent GPUs
Lock in current rates for predictable workloads. Rental prices are falling but may stabilize as demand catches up. Reserved capacity commitments at today’s rates can protect against future price fluctuations.
Diversify across providers. Do not depend on a single cloud provider. Use GPU marketplaces for cost-effective inference, hyperscalers for training when you need guaranteed capacity, and specialized clouds for middle-ground workloads. For detailed pricing across providers, see our cloud GPU pricing comparison.
Optimize inference separately from training. Use different GPU models for different tasks. Training requires H100/B200-class hardware. Inference often runs effectively on A100, L40S, or even RTX 4090 at a fraction of the cost. See our best GPU for AI guide for workload-specific recommendations.
If You Provide GPUs
H100 hardware holds value through 2026. Despite falling rental rates, H100s remain the most in-demand GPU for both training and inference. Depreciation will accelerate in 2027 as Rubin-architecture GPUs become available, but through 2026, H100 providers can still generate strong returns.
Consumer GPUs are increasingly viable for rental. The inference shift means platforms are accepting more consumer GPUs (RTX 4090, RTX 5090) for distributed inference workloads. If you have idle consumer hardware, the barrier to entry for rental is lower than ever.
Invest in reliability over raw hardware. Platforms increasingly reward uptime, response time, and consistency over raw specs. A reliably available A100 earns more over time than an intermittently available H100.
Frequently Asked Questions
Does OpenAI only use NVIDIA GPUs?
Primarily, yes — NVIDIA GPUs power the vast majority of OpenAI’s training and inference infrastructure. However, OpenAI has been reported to be developing custom AI chips and exploring alternative hardware to reduce NVIDIA dependency. The custom chip effort is still in early stages and is not expected to significantly reduce NVIDIA reliance before 2027–2028.
Will GPU rental prices keep falling?
Prices have fallen significantly (44% for H100 from 2024 to 2025) and will likely continue declining gradually as supply expands and competition intensifies. However, the rate of decline is slowing. Demand continues to grow at 4–5x per year, which will eventually absorb expanded supply. The most likely trajectory is gradual price reduction for older GPU models and premium pricing for the latest generation.
What does the training-to-inference shift mean for me?
If you are an AI developer: inference becoming 2/3 of all AI compute means more options for distributed, cost-effective GPU access through marketplaces. You are less dependent on massive training clusters and more able to leverage competitive pricing for inference workloads. If you are a GPU owner: inference workloads are more compatible with consumer hardware and distributed setups, expanding the pool of potential renters for your hardware.
How much does OpenAI spend on GPU compute?
Exact figures are not public, but estimates place OpenAI’s annual compute spend in the billions of dollars. Training GPT-4 alone reportedly cost over $100 million in compute. Inference for hundreds of millions of ChatGPT users adds significant ongoing cost. Microsoft Azure, OpenAI’s primary infrastructure partner, has invested over $10 billion in the partnership.
Should I invest in GPU hardware now or wait?
If you need compute now, rent from a marketplace to avoid depreciation risk. If you are considering a hardware purchase for rental income, the H100 remains a strong choice through 2026 but will depreciate sharply in 2027 as Rubin-architecture GPUs arrive. For hardware purchases, see our rent vs. buy analysis for detailed break-even calculations.
For a lower-barrier way to earn from the growing AI compute market, you can buy a GPU package on GPUnex starting at $59 to receive daily revenue without managing hardware or dealing with depreciation.