The hottest AI hardware Substack posts right now

And their main takeaways
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Groq Inference Tokenomics: Speed, But At What Cost?

SemiAnalysis • 10708 implied HN points • 21 Feb 24
🕹 Technology AI hardware Performance analysis Cost Analysis
  1. Groq AI hardware showcases impressive speed and cost efficiency, outperforming other inference services while charging less.
  2. While speed is vital, supply chain diversification plays a significant role in evaluating hardware's revolutionary potential.
  3. Understanding the total cost of ownership is crucial in deploying AI software, with significant impacts from chip microarchitecture and system architecture.

Qualcomm’s Cloud AI 100 PCIe: Now For All

More Than Moore • 93 implied HN points • 06 Jan 25
🕹 Technology AI hardware Cloud Computing Machine Learning Embedded Systems Data processing
  1. Qualcomm's Cloud AI 100 PCIe card is now available for the wider embedded market, making it easier to use for edge AI applications. This means businesses can run AI locally without relying heavily on cloud services.
  2. There are different models of the Cloud AI 100, offering various compute powers and memory capacities to suit different business needs. This flexibility helps businesses select the right fit based on how much AI processing they require.
  3. Qualcomm is keen to support partnerships with OEMs to build appliances that use their AI technology, but they are not actively marketing it widely. Interested users are encouraged to reach out directly for collaboration opportunities.

What’s the future of AI hardware?

Product Identity • 138 implied HN points • 17 Jun 24
🕹 Technology AI hardware Product Design Tech Trends Gadgets Consumer Electronics Innovation
  1. AI hardware is still finding its identity and purpose. It's not yet clear how AI will truly enhance our devices.
  2. New gadgets often create high expectations but can lead to disappointment. Companies may hype products that aren't fully developed.
  3. Innovation in hardware often combines old ideas with new technology. It might be better to improve existing devices than to create entirely new ones.

Inside Packaging And Module Technology With Saras Micro

Semiecosystem • 19 implied HN points • 24 Jun 24
🕹 Technology Semiconductor industry AI hardware
  1. The semiconductor industry is entering a new growth cycle driven by the rise of AI tools and applications, with the next wave of growth expected to come from AI hardware.
  2. To overcome challenges in traditional chip scaling, the industry is adopting chiplet-based architectures and heterogeneously integrated packaging approaches for continued performance scaling.
  3. Advanced packaging technologies play a crucial role in supporting high-performance compute devices for AI systems, with companies like Saras exploring innovative solutions like embedded capacitive module technology for improved power delivery.
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What hardware do I need to create a LLM?

Shrek's Substack • 0 implied HN points • 18 Apr 23
🕹 Technology AI hardware Machine Learning Data science Computer Science Software Engineering
  1. Training large language models (LLMs) needs powerful hardware, often multiple A100 GPUs with 40GiB of VRAM each. Running them is cheaper than training.
  2. Different data types like FP16 and TF32 are crucial for handling model memory. New types help manage larger numbers while saving memory.
  3. For smaller models, single hardware can work, but bigger models need a lot of VRAM or multiple systems. There's a difference between training and running models efficiently.