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Groq founder Jonathan Ross explains why their architecture is better for inference than GPUs. Inference doesn't require as much compute power as training. What matters in inference is speed, cost, and energy consumption. $NVDA was going to lose market share as AI workloads shift to inference. They saw this... show more

Oguz Erkan

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97,186 Aufrufe • vor 6 Monaten •via X (Twitter)

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So now everyone is talking about how AMD is going to have edges over NVDA in inference? Listen to Jense explain inference problem and how Nvidia is also dominating it.

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This is why Nvidia bought Groq. If Groq deploys a ton of low-cost inference chips, demand for training will increase massively. The more inference you have, the more training you need, and vice versa. Nvidia can keep its high margins selling chips for training forever.

This is why Nvidia bought Groq. If Groq deploys a ton of low-cost inference chips, demand for training will increase massively. The more inference you have, the more training you need, and vice versa. Nvidia can keep its high margins selling chips for training forever.

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20,558 Aufrufe • vor 5 Monaten

No wonder Jensen got so defensive over $NVDA GPUs against custom chips. $GOOG will start using different custom chips for inference and training. $AMZN has already adopted this approach as they have Trainium for training and Inferentia for inference. Hyperscalers obviously see better price/performance in this approach, and this is where the industry is headed. $AVGO and $MRVL are poised to be big winners, while $NVDA dominance will be under increasing pressure.

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The market won’t be able to keep $NVDA below $200 much longer. Jensen is clearly building for the inference era with Rubin handling attention-heavy workloads and Groq powering low-latency token generation. The AI engine just keeps humming.

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Jensen basically confirmed my takes earlier about the Groq deal.. if it was a much better inference stack than NVDA GPU then they wouldn't have sold it for $20bn, also not simply about the SRAM architecture that NVDA can replicate in no time.

Jensen basically confirmed my takes earlier about the Groq deal.. if it was a much better inference stack than NVDA GPU then they wouldn't have sold it for $20bn, also not simply about the SRAM architecture that NVDA can replicate in no time.

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AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

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1/ $AMD is a 10x opportunity everybody ignores. Lisa Su believes that inference will be a bigger market than training. Training is the cost center of AI workload while inference is where you make money. $AMD has an advantage here. Let me explain why: 🧵

1/ $AMD is a 10x opportunity everybody ignores. Lisa Su believes that inference will be a bigger market than training. Training is the cost center of AI workload while inference is where you make money. $AMD has an advantage here. Let me explain why: 🧵

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Peter Thiel says the quiet part loud: AI chips will get commoditized. This is why I avoid $NVDA. It made an immense amount of money marking up its GPUs by 10x and others had to pay as alternatives weren’t good enough. Now they are getting good enough. $AMD has already caught up with $NVDA in hardware performance, and ASICS are proving unexpectedly competitive due to their efficiency. $GOOG trained Gemini 3 solely on TPUs, and Anthropic is running a significant part of its training and inference workloads on Trainium clusters. As alternatives become even stronger, $NVDA margins and volumes will erode, and profits will shift from hardware to the application layer.

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Oguz Erkan

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Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.

Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.

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OpenAI's Noam Brown says that while AI model performance scales roughly equivalently with more training or inference compute, the cost of inference is on the order of 100 billion times cheaper

OpenAI's Noam Brown says that while AI model performance scales roughly equivalently with more training or inference compute, the cost of inference is on the order of 100 billion times cheaper

Tsarathustra

250,828 Aufrufe • vor 1 Jahr

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

TBPN

17,057 Aufrufe • vor 9 Monaten