Last month I uploaded a blog post on Cerebras, I explored the incredible speed of Cerebras’ wafer-scale engine in my post "The best AI inference for your project. Blazing fast responses.". The AI hardware landscape has evolved dramatically since then. In this updated deep-dive, we pit the reigning throughput champion, Cerebras, against the latency king, Groq, to help you choose the right engine for your project.

The quest for faster AI inference is more than a hardware race—it’s about enabling real-time applications that were previously impossible. Whether you’re building a voice agent that can’t lag or a bulk data processor that needs to handle millions of tokens, the underlying hardware defines your limits.

Two architectures have risen to the top of the speed conversation: Groq’s LPU (Language Processing Unit) and Cerebras’ CS-3 wafer-scale engine. Both claim to leave traditional GPUs in the dust, but they take fundamentally different paths. Let’s break down which one might be the fastest AI inference provider for your needs.

Architectural Showdown: Two Paths to Speed

At their core, both Groq and Cerebras are designed to smash the memory bandwidth bottleneck that slows down LLM inference on GPUs. How they achieve this is where they diverge.

🚀 Groq’s LPU: The Determinist

Groq’s approach is built on deterministic, static execution. Its LPU architecture uses hundreds of megabytes of on-chip SRAM as primary weight storage (not a cache) to minimize latency. A compiler pre-computes the entire execution graph down to the clock cycle, eliminating runtime coordination delays and enabling precise tensor parallelism. This makes Groq exceptionally good at delivering consistent, low-latency responses.

🏔️ Cerebras’ CS-3: The Monolith

Cerebras takes a "big chip" approach. Its wafer-scale engine is a single, massive chip designed to hold entire large models in its on-chip SRAM, boasting a staggering ~21 PB/s of memory bandwidth. This eliminates the need to shuttle weights between chips or memory, which is a major bottleneck for other systems. The architecture is optimized for massive, steady-state throughput.

Performance Benchmarks: Latency vs. Throughput

Raw numbers tell a compelling story. Independent benchmarks and provider data highlight a clear dichotomy: Groq excels at latency, while Cerebras dominates raw throughput.

Model & Metric Groq (LPU) Cerebras (CS-3) Notes Llama 2 70B (Tokens/sec) ~241 tokens/sec Not directly compared Groq’s low-latency strength Llama 3.1 8B (Tokens/sec) Not benchmarked Up to 1,800 tokens/sec Cerebras’ throughput dominance Llama 3.1 70B (Tokens/sec) Not benchmarked ~450 tokens/sec oss-gpt-120B (Tokens/sec) ~493 tokens/sec ~3,000 tokens/sec Cerebras claims a ~6x advantage End-to-End Response Time Faster for first token Up to 5x faster for full completion Groq better for interactive chats

The Verdict: If your primary metric is time-to-first-token for real-time interactions (e.g., voice bots), Groq is often the winner. If you need to process massive volumes of text (e.g., summarizing thousands of documents) as fast as possible, Cerebras provides unmatched throughput.

Beyond Speed: Accuracy, Ecosystem, and Cost

Speed isn’t everything. Accuracy, cost, and how you integrate the platform are crucial for production systems.

· Accuracy & Precision: Cerebras supports native 16-bit precision, while Groq often uses 8-bit quantization for speed, employing its "TruePoint" numerics to minimize accuracy loss. For tasks sensitive to precision, this is a key differentiator. · Pricing Model: Both offer competitive and complex pricing. Cerebras claims a price-performance advantage of up to 6x over Groq for throughput-heavy workloads. However, for lower-volume, latency-sensitive tasks, Groq’s pricing can be very attractive. · Ecosystem & Ease of Use: Both shine here. They offer OpenAI-compatible APIs, making integration a matter of changing an endpoint and API key. Both are available on cloud platforms and support on-prem deployment.

When to Choose Which: A Practical Guide

Your project’s requirements should dictate your choice.

Choose Groq if you are building:

· Real-time conversational AI (chatbots, voice agents) · Interactive coding assistants where developer flow is critical · Latency-sensitive API backends where user waiting is not an option

Choose Cerebras if you are building:

· Large-scale batch processing (document summarization, data labeling) · Scientific simulation & research requiring maximum throughput · Enterprise copilots that analyze vast internal datasets · Training and inference workloads (Cerebras supports both)

Getting Started: Code Examples

Integrating either platform is straightforward. Here’s a quick look.

Using Groq with Node.js

import Groq from 'groq-sdk';

const client = new Groq({ apiKey: process.env.GROQ_API_KEY });

const chatCompletion = await client.chat.completions.create({
messages: [{ role: 'user', content: 'Explain quantum computing simply.' }],
model: 'llama3-70b-8192', // Example model
});
console.log(chatCompletion.choices[0]?.message?.content);

Source: Groq TypeScript SDK

Using Cerebras with Node.js

import { CerebrasClient } from '@cerebras/sdk';

const client = new CerebrasClient({
apiKey: process.env.CEREBRAS_API_KEY,
model: 'llama-3-70b',
});

const response = await client.generate({
prompt: "Explain how wafer-scale engines work.",
maxTokens: 512,
});
console.log(response.text);

Adapted from previous Cerebras post

Final Thoughts

The "fastest" AI inference provider isn’t a single answer—it’s a question of what kind of speed you need.

· Groq is the "Latency King." Its deterministic LPU architecture is engineered for lightning-fast, consistent responses, making it ideal for real-time, interactive applications where every millisecond of wait time degrades the user experience. · Cerebras is the "Throughput Titan." Its wafer-scale CS-3 engine is a monster for bulk processing, capable of crushing massive inference jobs by keeping entire models on a single chip. It’s the tool for when you need to process a mountain of data, not just a single query quickly.

For many teams, the optimal strategy might even be inference arbitrage—using a router like LiteLLM to send latency-sensitive traffic to Groq and throughput-heavy batch jobs to Cerebras, optimizing for both performance and cost.

What’s your experience? Are you team Groq for latency or team Cerebras for throughput? Share your benchmarks and use cases in the comments below!