Why Groq Feels Like Cheating

I recently built a multi-agent pipeline using LangGraph. I compared Groq to standard LLM providers. The difference felt massive.

Other providers feel like a normal API call. You send a request and wait for the text. Groq feels like cheating. A 70B model returned a full response before I finished reading my own prompt.

Most people assume Groq has better GPUs. That is wrong. Groq does not use GPUs at all. They built a new chip called the LPU, or Language Processing Unit.

GPUs were made for graphics and training models. They work well when you process massive batches of data. But they struggle with real-time inference.

The problem is the "memory wall." In a GPU, model weights live in memory separate from the compute cores. The chip spends too much time waiting for data to arrive.

Groq solved this by putting memory directly on the chip. They use SRAM instead of HBM. This creates a 10x gap in bandwidth. It also makes data access 20x faster when you factor in latency.

There is another reason for the speed: determinism.

GPUs use dynamic scheduling. The chip decides what to do while it runs. This creates tiny delays. Groq uses a software-first approach. Their compiler calculates every single operation and instruction ahead of time. The chip follows a pre-set schedule. It does not have to think about what to do next.

The results speak for themselves: • Llama 2 70B runs at 300 tokens per second on Groq. • An Nvidia H100 runs it at 30–40 tokens per second. • Llama 3 8B hits over 1,300 tokens per second on Groq.

Groq is also more efficient. It uses less total energy per token because it finishes the work so much faster.

This design has tradeoffs. SRAM is expensive and takes up a lot of physical space. One chip cannot hold a giant model. You need hundreds of LPUs working together to serve large models. This makes the hardware more expensive than GPUs.

Groq is not trying to train models. They focus on running existing models like Llama or Mixtral as fast as possible.

The industry is moving toward using both. GPUs handle the heavy training and initial processing. LPUs handle the fast, real-time conversation.

Nvidia optimized for total computation. Groq optimized to ensure compute never waits for data. For real-time AI agents, the second goal is what matters.

Source: https://dev.to/priyanshu79/why-groq-feels-like-cheating-29hm

Optional learning community: https://t.me/GyaanSetuAi