NVIDIA GPU Prices Surge Over 20% in Two Years, Renting Becomes a Cost-Effective Alternative

Soaring Demand for AI Accelerates GPU Shortages

The price of NVIDIA GPUs has surged more than 20% over the past two years, driven by the widespread adoption of generative AI and an ongoing supply shortage, according to a report from Nikkei Asia on February 18. With these price hikes, renting NVIDIA GPUs is now a more economical choice than buying, leading to a growing demand for GPU rental services.

NVIDIA GPU Prices Hit Record Highs

Japanese enterprises and AI developers are experiencing significant cost increases when acquiring high-performance NVIDIA GPUs. A source from a Japanese company noted that the current market price for the NVIDIA H100 NVL, a flagship model, was approximately 4.4 million yen ($29,000) at the end of 2024, marking a 25% price increase compared to two years ago. Today, the price has climbed further to around 5.2 million yen ($34,300).

Additionally, NVIDIA's next-generation Blackwell-series B200 GPU, expected to start shipping soon, is rumored to have a staggering price tag of 7.5 million yen ($49,400) or more, further reflecting the escalating cost of AI acceleration hardware.

Renting vs. Buying: A More Cost-Effective Model?

Given the high acquisition costs, renting high-performance NVIDIA GPUs is increasingly seen as a more financially viable solution.

According to GDEP Solutions, a company specializing in GPU rental services, purchasing a server equipped with four NVIDIA H100 GPUs requires an investment of approximately 26 million yen ($171,500). However, renting such a server for a year costs less than half of that amount. Over a two-year period, renting proves to be even more cost-efficient than purchasing outright.

Why Businesses Are Choosing GPU Rentals

Several key factors are driving the shift toward renting instead of purchasing NVIDIA GPUs:

Frequent GPU Release Cycles: NVIDIA typically introduces new GPU architectures every two years. Businesses looking to stay ahead with cutting-edge performance often find renting more flexible than making a large one-time purchase for hardware that will soon be outdated.

High Upfront Costs: The initial investment for top-tier GPUs is becoming prohibitively expensive, pushing companies to seek alternative operational expenditure (OpEx) models rather than capital expenditures (CapEx).

Scalability Needs: AI startups and enterprises running AI training and inference workloads can scale their computing power dynamically without committing to large-scale hardware investments.

Cloud Computing Integration: With the rise of AI cloud services, many organizations are opting for cloud-based GPU rentals rather than managing on-premises infrastructure.

NVIDIA’s Dominance in the AI GPU Market

NVIDIA continues to dominate the AI hardware market, with its GPUs being the backbone of AI models, including ChatGPT and other LLMs. However, the rising demand and production constraints have driven prices to record levels, making it difficult for enterprises to secure high-end GPUs at affordable prices.

With NVIDIA’s Blackwell series on the horizon and next-generation AI workloads requiring even more computational power, the trend toward GPU leasing and cloud-based AI services is expected to accelerate further in 2025 and beyond.