2025-12-26 11:22:00

Why did SRAM demand suddenly explode? Just look at this operation and you'll understand.

Not long ago, a leading AI chip manufacturer publicly disclosed its holdings in a certain tech giant, and shortly after, announced the acquisition of a chip innovation company. This luck or strength, a closer look reveals the answer.

What is this company's core advantage? Unlike traditional GPUs that rely on external high-bandwidth memory (HBM), their LPU processor adopts an on-chip integrated large-capacity static random-access memory (SRAM) design. This 230MB on-chip SRAM can provide up to 80TB/s of memory bandwidth—what does this number mean? It directly outperforms traditional GPU solutions in data processing speed.

How does it perform in practice? Their cloud services are famous for their astonishing inference speed. When running open-source large models like Mixtral and Llama 2, they can output about 500 tokens per second, which is not even comparable to traditional service response speeds. The pricing is also competitive, with costs calculated per million tokens, making it quite cost-effective.

Why is this so important now? Because the entire AI field is undergoing a critical shift— inference demand is about to fully surpass training demand. In this context, providing an efficient, low-cost, and truly scalable inference infrastructure through innovative architectures like LPU is what the market truly needs. A certain chip company's leader explicitly stated plans to integrate this low-latency processor into their AI factory architecture, aiming to serve broader AI inference and real-time workloads.

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MEVvictim

· 4h ago

When I saw the figure of 80TB/s, I knew who would win; the HBM gameplay is about to be crushed. With inference costs so competitive, I’m optimistic about this wave. Another story of "I bought in early," luck or skill—judge for yourself. SRAM integration is clever; it directly reduces the complexity compared to traditional solutions. I believe in 500 tokens per second, but I want to see how long this can actually run in a real production environment. That’s why I’ve been paying close attention to on-chip storage recently; I’ve sensed this shift coming early. The king of competition has come up with a new trick—let’s see how low the costs can go. The LPU architecture is up and running; GPU’s days might need to change. Price is a killer feature; it really depends on how effective the actual deployment is. I’ve heard the claim that inference will surpass training for years—could this really be happening now?

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AirdropDreamer

· 4h ago

80TB/s bandwidth? Now GPU manufacturers have to sit tight. SRAM is indeed an invisible track this time.

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rugpull_ptsd

· 4h ago

80TB/s this number is truly amazing, crushing traditional GPUs is no exaggeration --- So ultimately, it's about the inference part taking off, it should have been valued earlier --- 500 tokens/s? That speed is really crazy, finally someone is seriously working on inference --- Exactly this idea, on-chip SRAM directly eliminates the latency monster, efficiency skyrocketing --- The move to acquire is clever, LPU is the future way for inference, right? --- Cheaper cost? Now the training folks are panicking, inference is really about to turn around --- Wait, what does 80TB/s mean... it's faster than anything else --- Finally, someone has mastered inference thoroughly, that HBM setup should have been phased out long ago

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BearMarketBro

· 4h ago

80TB/s? Laughing to death. If this can really be achieved, HBM manufacturers will be crying. --- Inference supertraining, I finally understand this wave—it’s all about money. --- That’s pretty aggressive, directly integrating SRAM to bypass HBM bottlenecks. It’s about time to play like this. --- 500 tokens/s sounds impressive, but where are the real benchmark scores? --- This is the right path for AI chips. Bypassing external bottlenecks is the key to winning. --- Chip manufacturers understand the game: hold positions first, then acquire. Capital is so capricious. --- Taking the SRAM route was the right choice, but I’m worried the subsequent process costs won’t come down. --- The turning point where inference becomes mainstream has finally arrived. Whoever seizes it will win. --- Affordable price + fast speed—this era of benchmarking is about to change. --- Wait, does that mean the HBM orders are about to cool off?

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