The way that I look at this is it's a very interesting go to market channel for you, a sales channel. Think about all of the clients that IBM has and how you've tried to grow the company. Explain how people will access help use through this.Well, through the cloud matrix. Absolutely. It's an extraordinary opportunity for both of us.IBM is going to have their sellers sell across skew. And so now you'll be able to directly access our speed, the advantages that we offer. You could think of it a little bit like ...

Core Insights - The partnership between IBM and Grok aims to enhance AI deployment speed and reduce costs, with Grok providing significant performance improvements at a fraction of the cost [5][8][18] - IBM's AI business, particularly with Watson X, has a substantial book of business valued at $7.5 billion, indicating strong momentum in the AI sector [4][8] - The collaboration is expected to address the cost challenges associated with AI, with IBM projecting $4.5 billion in productivity gains by the end of the year [8] Partnership Dynamics - IBM will integrate Grok into its sales strategy, allowing clients to access Grok's capabilities through IBM's established channels [2][5] - There is a revenue-sharing model in place as part of the partnership, which is designed to benefit both companies [5][18] - The integration of Grok with IBM's Watson X is intended to be seamless, enhancing user experience without requiring significant changes from clients [9][10] Market Demand and Client Base - Financial services have been early adopters of Grok, but there is a growing trend towards multi-model AI solutions across various sectors [11][12] - The demand for faster inference and improved operational efficiency is driving interest in Grok's technology [7][10] - IBM's ability to fulfill client orders quickly is highlighted as a competitive advantage in a supply-constrained market [13][14] Technology and Innovation - Grok's technology is positioned to improve AI inference speed significantly, which is crucial for applications like call centers and supply chain management [7][10] - The partnership is open to collaboration with other AI technologies, indicating a flexible approach to enhancing AI capabilities [16][17] - IBM's existing relationships and trust in the market are leveraged to promote Grok's adoption among enterprise clients [18][19]

Core Insights - Oracle plans to build a new data center utilizing 50,000 AMD chips to enhance its AI capabilities, marking a significant move in the AI sector [1][2] - The partnership with AMD is seen as a strategic choice for Oracle, particularly in the inference space, as they have a long-standing relationship and a robust software stack [3][4] - Oracle's investment in AMD chips is a substantial commitment, reflecting founder Larry Ellison's vision for the company's competitive stance against major players like Microsoft, Amazon, and Google [5][6] Company Developments - Oracle is deploying AMD's latest AI chips to create a supercluster aimed at enabling customers to run larger and more complex AI models by the second half of next year [2] - The market is responding positively to AMD, with its shares rising as it is increasingly recognized as a viable competitor in the inference market, especially following OpenAI's recent investment in AMD [4] - Larry Ellison is expected to provide insights into a $300 billion deal with OpenAI and discuss Oracle's new cloud offerings that aim to enhance efficiency and speed for customers [5][6] Financial Considerations - Oracle's financial analyst day will reveal how the company plans to fund its significant chip purchases, indicating the importance of this week for Oracle's financial strategy [6]

6/Currently working on Gemma3 quantization, focusing on:- Learning the new model architecture- Adding KV cache support (which accelerates inference)- Implementing quantization support for some new operators-- Full operator support will require 1+ additional day, plus more time for accuracy testingStay tuned for more updates 🔥 ...

Dropout Mechanism - During training, the average neuron input is significantly lower compared to inference, potentially causing numerical instability due to activation scale misalignment [1] - Dropout addresses this by multiplying inputs during training by a factor of 1/(1-p), where 'p' is the dropout rate [2] - For example, with a dropout rate of 50%, an input of 50 is scaled to 100 (50 / (1 - 0.5) = 100) [2] - This scaling ensures coherence between training and inference stages for the neural network [2] Training vs Inference - Consider a layer with 100 neurons, each with an activation value of 1, and a weight of 1 from each neuron to neuron 'A' in the next layer [2] - With a 50% dropout rate, approximately 50 neurons are active during training [2] - During inference, all 100 neurons are active since Dropout is not used [2]

AI Infrastructure Investment & Scale - Building planetary-scale AI infrastructure requires both private and public sector resources [2] - The AI infrastructure buildout, encompassing energy exploration, power generation, transmission, data centers, supercomputers, and application layers, is estimated to be a multi-trillion dollar investment [5] - This infrastructure is considered a fundamental component of the future economy for the next 50 years [5] Demand & Monetization - The large deals announced by AI labs and hyperscalers indicate significant demand for compute [4] - The majority of compute being built is to serve inference, which represents the monetization of AI [11] - Hyperscalers are being paid to build this infrastructure to serve current and projected client demand [12] Bubble Concerns & Commercial Activity - The question of whether the large capital investments in AI will produce a significant return is being raised [10] - The flow of money into AI is supported by broad-based demand across the technology space as businesses integrate AI into their workflows [13] - The companies deploying capital in AI are among the largest and most successful, differentiating this from the dot-com bubble [14] Comparison to Dot-com Era - Unlike the dot-com era, which was a bolt-on to existing infrastructure, AI requires a new layer of power generation due to increased power consumption [7] - The disruption caused by AI is considered to be on the same order of magnitude as the advent of the internet [8]

Here's a hidden detail about Dropout that many people don't know.Assume that:- There are 100 neurons in a layer, and all activation values are 1.- The weight from 100 neurons to a neuron ‘A’ in the next layer is 1.- Dropout rate = 50%Computing the input of neuron ‘A’:- During training → Approx. 50 (since ~50% of values will be dropped).- During inference → 100 (since we don't use Dropout during inference).So essentially, during training, the average neuron input is significantly lower than that during infer ...

Core Insights - Nvidia and Broadcom are leading the headlines due to significant data center revenue growth driven by strong demand for AI infrastructure, but other chipmakers like AMD and Marvell also have substantial opportunities ahead [2] Group 1: Advanced Micro Devices (AMD) - AMD has historically been a secondary player to Nvidia in the GPU market, but it has a chance to gain market share as the focus shifts towards inference [3] - The demand for chips that handle inference is expected to rise as AI models grow larger and are deployed more widely, with AMD already serving a significant portion of inference traffic for major AI companies [4] - AMD's ROCm software platform has improved and allows for competitive pricing and efficiency, which could enable AMD to capture market share from Nvidia by lowering total costs for customers [5] - The UALink Consortium, founded by AMD, offers an open standard alternative to Nvidia's NVLink, potentially allowing for greater flexibility in multi-GPU systems [6] - Even small market share gains would be impactful for AMD, given its much smaller revenue base compared to Nvidia, which reported over $40 billion in data center revenue last quarter compared to AMD's approximately $3 billion [7] Group 2: Marvell Technology - Marvell is also positioned in the AI infrastructure market, winning custom AI chip designs with various customers, although it currently operates under the shadow of Nvidia and Broadcom [8]

I always like to go back to basics. Jonathan Like Grok stacks up against some video and Google's TPU and we're largely focused on inference. Why did you raise this round in that context.What is it going to allow you to do to be more competitive against those two giants. Well, the bottom line is that the demand for inference is insatiable. The total amount of capacity that people are trying to deploy is mind boggling. The numbers that people are putting up.And that's only growing. And in our case, we don't f ...

joined on set by Adair Fox Martin, CEO of Equinex. Adair, thank you for coming in. >> Thank you so much for having me.>> To the wilds of New Jersey. >> Delighted to be here. >> Fant.We're delighted you're here. So, you gave me a really good example before the show began of what you guys do. You compared it to an airport.Explain what that means and who your company is. >> Yeah. So, I think in the world of data center, there's a harmonious view of data centers, but actually there's different types.Um, and Equ ...