Nvidia to raise AI server prices over 15% in early 2027 as memory costs soar

Reviewed byNidhi Govil

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Nvidia has notified its largest customers including Microsoft, Google, and Oracle of price increases exceeding 15% on AI servers shipping from early 2027. The hikes affect systems with Vera Rubin and Grace Blackwell chips, driven by unprecedented memory chip costs that Nvidia is passing through rather than absorbing despite its 75% gross margin.

Nvidia Announces Price Increases on AI Servers

Nvidia has informed some of its largest customers that AI servers containing its chips will see price increases exceeding 15% in many cases, with the hikes taking effect on systems shipped from early 2027

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. The AI-related price hikes will impact systems built around the flagship Vera Rubin and Grace Blackwell chips, with the exact increase depending on chip generation and memory configurations

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. Companies that assemble servers under contract for major data center operators including Microsoft, Google, and Oracle have recently notified their customers of the forthcoming increases

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Source: Benzinga

Source: Benzinga

Soaring Memory Costs Drive AI Hardware Costs Higher

The price increases stem from unprecedented memory chip costs that have gripped the DRAM market in what analysts call "RAMageddon"

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. Contract prices for conventional DRAM are projected to climb 58% to 63% quarter-over-quarter in Q2 2026, following a Q1 surge of 90% to 95%, as suppliers reallocated capacity toward High Bandwidth Memory and server products

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. SK hynix announced in October last year that it had already sold out its entire 2026 memory production capacity, while Samsung and SK hynix raised 2026 HBM3E supply prices by close to 20% before the year began

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. The three major memory makers—Samsung, SK hynix, and Micron—account for most of the world's DRAM production and have unprecedented leverage as demand for AI infrastructure surges

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Memory Becomes Largest Cost Component in AI Infrastructure

AI systems carry enormous memory loadouts that make memory one of the largest line items in an AI server's bill of materials

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. Nvidia's Rubin GPU ships with up to 288GB of HBM4 per package, and the NVL72 rack-scale system combines 72 of those GPUs, putting more than 20TB of HBM in a single rack before accounting for the LPDDR attached to its Vera CPUs

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. With HBM production consuming roughly four times the wafer area of equivalent conventional DRAM, these costs continue rising at a stratospheric pace

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. A 15% rise on rack-scale systems that sell for several million dollars each adds hundreds of thousands of dollars per rack across deployments that run to thousands of racks

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Source: Tom's Hardware

Source: Tom's Hardware

Nvidia Passes Costs to Customers Despite High Margins

Despite running a gross margin of roughly 75% non-GAAP, among the highest in the semiconductor industry, Nvidia is passing memory cost inflation on to Nvidia customers rather than absorbing it

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. The inability of the industry's most dominant company to hold the line on prices shows how much leverage memory chip makers have amid surging demand for AI infrastructure

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. Supply of Nvidia's accelerators from TSMC still can't meet demand, which limits buyers' immediate leverage

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. Nvidia has already passed rising costs through to consumers, raising prices on GeForce graphics cards earlier this month

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Impact on Hyperscalers and AI Data Center Build-Outs

The price increases reach every buyer including Europe's planned AI gigafactories, where the EU has committed around €20bn to a set of facilities, and a French consortium has bid $10bn for one site

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. Those bids and budgets were built on last year's hardware prices, making a 15% increase a material change to plans drawn up before the memory market tightened

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. Whether the increases push hyperscalers further toward AMD's accelerators or their own custom silicon will depend on how quickly those alternatives can absorb displaced demand, though all of them draw HBM from the same three constrained suppliers

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. Major customers like Amazon, Microsoft, Google and Meta are pursuing their own in-house chip programs but remain dependent on Nvidia purchases for their data center build-outs

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. The price increases are likely to add complexity to the industry's massive AI data center build-out ambitions, which already face project delays, labor shortages, tightening capital markets and community resistance

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Source: Fortune

Source: Fortune

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