When using state-of-the-art Large Language Models (LLMs), you might have wondered: why does the AI respond like a web page loading on a 1990s dial-up connection, spitting out words one by one?

Despite being powered by massive data centers and the world’s most advanced digital processors, LLMs are fundamentally constrained by a hidden bottleneck. The reason your AI generates text so slowly is not a lack of computing power, but a severe limitation in memory bandwidth.

The Sequential Nature of LLM Inference To understand the delay, we have to look at how LLMs generate language. LLM inference is a strictly sequential process. The prediction of the “next word” entirely depends on the previous word, meaning this process cannot be parallelized.

Each individual word (or token) generated by the large model requires the entire model to be processed. All of the model’s parameters must be pulled from memory and put into the calculation. If you want the AI to generate 100 words, all of the model’s parameters need to be calculated 100 separate times.

The Staggering Math of the Memory Trap Let’s look at the numbers using the popular Llama3.1-70B model as an example.

• The model has 70 billion parameters.
• At 16-bit precision, each parameter requires 2 bytes of storage, meaning the entire model requires 140GB of memory.
• Because a standard GPU only has a few hundred megabytes of on-chip SRAM memory, the model cannot be stored directly on the chip.

Therefore, every single time a single token is generated, that entire 140GB model must be fully transferred from the off-chip memory to the calculation core. If you want the AI to generate just 10 tokens per second, that requires 1.4 Terabytes per second (TB/s) of memory bandwidth (10 x 140GB).

While a high-end Nvidia H100 GPU has about 3.3 TB/s of memory bandwidth to support this relatively slow reasoning, achieving truly “instant” reasoning at 1000 tokens per second would require 140 TB/s of bandwidth. This astronomical requirement far exceeds the capabilities of any GPU server or system in existence.

Why Brute Force Fails You might think the solution is simply to string multiple GPUs together to build a more powerful system. Unfortunately, this is a misconception. Adding more processors will only increase the throughput of the system (allowing it to handle more queries from different users simultaneously), but it will not speed up the response time of a single query. No matter how many GPUs you add, the entire model must still be sent across the data bus to generate every single word.

Break the Bandwidth Trap with TetraMem As long as the memory and the processor are physically separated, AI will remain trapped by the memory bandwidth limit. To achieve real-time, instantaneous AI inference, the industry must eliminate the data commute entirely.

TetraMem is pioneering the ultimate solution to the memory bandwidth trap with its breakthrough multi-level RRAM analog in-memory computing (IMC) technology. By computing directly where the data is stored using a proprietary One-Transistor-One-Resistor (1T1R) crossbar architecture, TetraMem completely bypasses the need to constantly shuffle massive models back and forth between memory and compute cores.

Instead of fighting limited bandwidth, TetraMem utilizes the physical laws of nature—Ohm’s Law and Kirchhoff’s Current Law—to execute massive matrix multiplications in parallel, achieving unprecedented speed and targeting over 300 TOPS/W of energy efficiency.

Stop letting legacy digital architectures choke your AI’s performance. Discover how TetraMem’s analog in-memory computing solutions can eliminate your bandwidth bottlenecks today.