Nvidia reportedly delays its next AI chip due to a design flaw
Nvidia has delayed production of its "Blackwell" B200 AI chips due to a design flaw, pushing shipments to early next year. Major clients have placed substantial orders, totaling billions.
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Nvidia has announced a delay in the production of its upcoming "Blackwell" B200 AI chips due to a design flaw identified late in the production process. This delay is expected to extend the timeline for large-scale shipments by at least three months, pushing the availability of the chips into the first quarter of next year. The B200 chips are intended to follow the highly sought-after H100 chips, which have significantly contributed to Nvidia's market value. Reports indicate that major clients, including Microsoft, Google, and Meta, have placed substantial orders for these chips, amounting to tens of billions of dollars. Nvidia is currently conducting additional test runs with Taiwan Semiconductor Manufacturing Company to address the design issues. The company had previously stated that Blackwell-based products would be available from partners starting in 2024, and it aims to establish a yearly release cycle for new AI chips. Despite the setback, Nvidia remains optimistic about ramping up production in the second half of the year.
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- Some commenters express hope for innovation from smaller systems rather than large clusters.
- There are questions about the implications of design flaws, with some suggesting they could be a cover for market dynamics or competition issues.
- Concerns about export restrictions and the potential impact on Nvidia's market strategy are raised.
- Several users speculate on the future of AI technology and the demand for Nvidia chips, particularly in relation to competitors.
- Some comments hint at a broader trend of delays and defects in the tech industry, suggesting a potential "AI bust."
17 commentsI also wonder - is the compute the main limiting factor today ? Let's imagine there is an unlimited number of NVidia chips available right now and energy is cheap - would using a cluster x100 of current biggest one result in a significant improvement ? My naive intuition is that not really.
Gina is going to be busy.
AI bust is coming
With inference tasks it'd be nice to have something not as performance heavy and instead like 24gb vram or more.
I know there are tricks in CUDA to allow access to system ram as a proxy but limited success in duplicating this across various setups.
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