A GPU is like a truck: its value is the massive payload (parallel data processing), not the driver (control logic). It excels at going straight for a long time. A CPU is like a motorcycle: it's mostly driver, designed for agility and complex steering through obstacle courses (branching instructions).
A fundamental constraint today is that the model architecture used for training must be the same as the one used for inference. Future breakthroughs could come from lifting this constraint. This would allow for specialized models: one optimized for compute-intensive training and another for memory-intensive serving.
The key metric for AI chips (GPUs/TPUs) is achieving a high percentage of theoretical peak performance (e.g., 70-80%). This concept, known as "mechanical sympathy," is largely absent in the CPU world, where software performance is so inefficient that measuring against peak is considered nonsensical.
The AI supply chain is crunched not just by obvious components like TSMC wafers and HBM memory. A significant, often overlooked bottleneck is rack manufacturing—including high-speed cables, connectors, and even sheet metal—which are "sneaky hard" due to extreme power, heat, and signal integrity demands.
While software development champions agile methods, chip design is necessarily a "waterfall" process. The massive, irreversible cost of fabrication means the architecture must be finalized before implementation (writing Verilog). This elevates the importance of the initial, pre-code architecture and simulation phase.
Google's TPUv1 was a minimal viable product built in a year by a skeleton crew. This lean approach is now impossible for new AI chips because the market has matured, and the "table stakes" for features, performance, and reliability are much higher, requiring a more complete initial product.
Startups can make big bets on emerging workloads, like LLMs before they were proven. This is a product risk. In contrast, incumbents like Google or NVIDIA must ensure their next chip serves a wide range of existing customers, forcing them to be more conservative and avoid disruptive product bets.
Despite its near-monopoly on leading-edge chips, TSMC maintains its dominance partly by not charging exorbitant prices. This conservative, long-term strategy makes it economically unattractive for new competitors to enter the market, thus protecting TSMC's position more effectively than maximizing short-term profit would.
Unlike competitors, MatX's ML team conducts fundamental research, training LLMs to validate novel hardware choices. This allows them to safely "cut corners" on industry standards, such as using less precise rounding methods. This deep co-design of model and hardware creates a uniquely efficient product.
NVIDIA's CUDA software ecosystem is a powerful moat in markets with many developers (like gaming). However, its advantage shrinks when selling to frontier AI labs. These labs buy $10B compute clusters and find it economical to hire teams to write custom software for new hardware, reducing their dependency on CUDA.
Modern AI models are moving towards extremely low-precision arithmetic (e.g., 4-bit numbers) because it's more efficient. The trade-off is analogous to image processing: you get a better result with more pixels (more computations) and fewer colors (less precision) than the other way around.
Existing AI chips force a trade-off: high-throughput HBM memory (NVIDIA, Google) has high latency, while low-latency SRAM memory (Grok) has poor throughput. MatX's architecture combines both, putting model weights in fast SRAM and inference data in high-capacity HBM to achieve both low latency and high throughput.