Mayuresh

Posted on Nov 3

The best AI inference for your project. Blazing fast responses.

#ai #machinelearning #rag #programming

The AI world is evolving fast. While most companies deal with slow response times and performance issues, Cerebras is delivering incredible speed that's transforming what we can do with large language models.

Why Speed Actually Matters in AI

Speed isn't just a cool feature anymore—it's essential for business success. According to Cerebras customer stories, their infrastructure delivers over 2,000 tokens per second. That's more than 30 times faster than ChatGPT or Claude!

This isn't just about getting faster answers. It's about building entirely new types of AI applications that weren't possible before.

Here's what one customer from GSK said:

"With Cerebras' inference speed, GSK is developing innovative AI applications, such as intelligent research agents, that will fundamentally improve the productivity of our researchers and drug discovery process."

What Makes Cerebras Special?

Cerebras built something truly revolutionary—a wafer-scale engine designed specifically for AI workloads. While traditional GPU clusters struggle with communication delays and memory limits, Cerebras' architecture provides:

Unmatched throughput: Process thousands of tokens per second
Ultra-low latency: Real-time interactions that feel natural
Massive context windows: Handle complex, multi-step reasoning tasks
Energy efficiency: Do more while using less power

What This Means for Developers

You can now build applications that:

Process entire codebases in seconds instead of minutes
Analyze genomic data in real-time for medical decisions
Power enterprise search that feels instant
Enable conversational AI that never interrupts your flow

Getting Started with Cerebras

The good news? Cerebras makes their enterprise-grade infrastructure surprisingly easy to use for developers. Let's look at how you can integrate Cerebras-powered Llama models into your apps.

Note: These examples show common patterns. Check the Cerebras documentation for exact implementation details.

JavaScript/Node.js Example

Perfect for web apps and real-time interfaces:

// cerebras-llama-js.js
import { CerebrasClient } from '@cerebras/sdk';

// Initialize the client with your API key
const client = new CerebrasClient({
  apiKey: process.env.CEREBRAS_API_KEY,
  model: 'llama-3-70b',
});

async function generateResponse(prompt) {
  try {
    // Cerebras delivers results at lightning speed
    const startTime = Date.now();

    const response = await client.generate({
      prompt: prompt,
      maxTokens: 512,
      temperature: 0.7,
      topP: 0.9,
    });

    const endTime = Date.now();
    const duration = endTime - startTime;
    const tokensPerSec = response.usage.output_tokens / (duration / 1000);

    console.log(`Response generated in ${duration}ms`);
    console.log(`Tokens per second: ${tokensPerSec.toFixed(2)}`);

    return response.text;
  } catch (error) {
    console.error('Error generating response:', error);
    throw error;
  }
}

// Example usage
const prompt = "Explain how Cerebras' wafer-scale architecture improves AI performance:";
generateResponse(prompt).then(console.log);

Rust Example

For performance-critical applications:

// cerebras_llama.rs
use cerebras_sdk::{Client, GenerateRequest, Model};
use std::time::Instant;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the Cerebras client
    let client = Client::new(
        &std::env::var("CEREBRAS_API_KEY")?,
        Model::Llama3_70b,
    );

    let prompt = "Write a function that calculates Fibonacci numbers efficiently in Rust:";

    // Measure the incredible speed
    let start = Instant::now();

    let request = GenerateRequest::new()
        .prompt(prompt)
        .max_tokens(256)
        .temperature(0.3)
        .top_p(0.95);

    let response = client.generate(request).await?;

    let duration = start.elapsed();
    let tokens_per_second = response.output_tokens as f64 / duration.as_secs_f64();

    println!("Response generated in {:.2?}", duration);
    println!("Tokens per second: {:.2}", tokens_per_second);
    println!("Result:\n{}", response.text);

    Ok(())
}

Go Example

For cloud-native applications and microservices:

// cerebras_llama.go
package main

import (
    "context"
    "fmt"
    "log"
    "os"
    "time"

    "github.com/cerebras/go-sdk/client"
    "github.com/cerebras/go-sdk/models"
)

func main() {
    // Initialize Cerebras client
    apiKey := os.Getenv("CEREBRAS_API_KEY")
    if apiKey == "" {
        log.Fatal("CEREBRAS_API_KEY environment variable is required")
    }

    cerebrasClient, err := client.NewClient(apiKey)
    if err != nil {
        log.Fatal("Failed to create client:", err)
    }

    // Define the prompt
    prompt := `Analyze this Go code for potential performance optimizations:

package main

import "fmt"

func main() {
    sum := 0
    for i := 0; i < 1000000; i++ {
        sum += i
    }
    fmt.Println(sum)
}`

    // Measure performance
    start := time.Now()

    request := models.GenerateRequest{
        Prompt:      prompt,
        MaxTokens:   300,
        Temperature: 0.2,
        Model:       "llama-3-70b",
    }

    response, err := cerebrasClient.Generate(context.Background(), request)
    if err != nil {
        log.Fatal("Generation failed:", err)
    }

    duration := time.Since(start)
    tokensPerSecond := float64(len(response.Tokens)) / duration.Seconds()

    fmt.Printf("Response generated in %v\n", duration)
    fmt.Printf("Tokens per second: %.2f\n", tokensPerSecond)
    fmt.Printf("Analysis:\n%s\n", response.Text)
}

PHP Example

For web applications and enterprise integration:

<?php
// cerebras_llama.php
require 'vendor/autoload.php';

use Cerebras\Client;
use Cerebras\GenerateRequest;

// Initialize the Cerebras client
$client = new Client($_ENV['CEREBRAS_API_KEY'] ?? getenv('CEREBRAS_API_KEY'));

$prompt = "Generate a secure PHP login system with password hashing and session management:";

// Time the response
$startTime = microtime(true);

try {
    $request = new GenerateRequest([
        'prompt' => $prompt,
        'model' => 'llama-3-70b',
        'max_tokens' => 400,
        'temperature' => 0.5,
        'top_p' => 0.9,
    ]);

    $response = $client->generate($request);

    $endTime = microtime(true);
    $duration = $endTime - $startTime;
    $tokensPerSecond = count($response->tokens) / $duration;

    echo "Response generated in " . number_format($duration, 4) . " seconds\n";
    echo "Tokens per second: " . number_format($tokensPerSecond, 2) . "\n";
    echo "Generated code:\n" . $response->text . "\n";

} catch (Exception $e) {
    echo "Error: " . $e->getMessage() . "\n";
}
?>

What Makes Cerebras Different?

The speed difference isn't just about better hardware—it's about completely rethinking AI infrastructure.

While competitors use clusters of GPUs connected by slow interconnects, Cerebras built a single chip the size of a wafer with:

4 trillion transistors working together
No communication bottlenecks between processing elements
Memory bandwidth that eliminates data transfer delays
Software stack optimized specifically for LLM workloads

This architecture enables real-world performance that's simply unmatched. One customer noted:

"We have a cancer-drug response prediction model that's running many hundreds of times faster on that chip (Cerebras) than it runs on a conventional GPU… We are doing in a few months what would normally take a drug development process years…"

Real-World Use Cases

1. Real-time Code Assistance

Developers using tools powered by Cerebras can stay "in flow" because the AI responds at the speed of thought. No more waiting and losing your train of thought.

2. Enterprise Search

Companies like Notion use Cerebras for instant, intelligent search across massive document collections—making information retrieval feel like magic.

3. Healthcare Diagnostics

Medical researchers can analyze genomic data in real-time, potentially saving lives by drastically reducing the time to find the right treatment.

4. Financial Analysis

Process market data, news, and reports simultaneously to make trading decisions in milliseconds instead of minutes.

How to Get Started Today

Cerebras is making this revolutionary technology accessible to all developers:

Sign up at Cerebras.ai
Get your API key from the developer dashboard
Install the SDK for your preferred language
Start building applications that were previously impossible

# Install SDKs
npm install @cerebras/sdk          # JavaScript/Node.js
cargo add cerebras_sdk             # Rust
go get github.com/cerebras/go-sdk  # Go
composer require cerebras/php-sdk  # PHP

The Future is Fast

Cerebras isn't just making AI faster—they're redefining what's possible. When inference happens at human speed, entirely new interaction patterns emerge.

Applications can now:

Maintain complex context across multiple interactions
Handle real-time multi-modal data
Perform deep reasoning without frustrating delays

As one developer said:

"Everything happens so fast that developers stay in flow, iterating at the speed of thought."

Whether you're building developer tools, healthcare applications, or enterprise software, Cerebras provides the foundation to build products that others simply cannot match.

The Bottom Line

The question isn't whether you need this speed—it's what will you build when latency is no longer your constraint?

Ready to experience the speed difference? Visit Cerebras.ai to get started today.

I am using cerebras for my MoneySense AI and
Tagnovate for RAG and text generation.
Have you used Cerebras or other high-performance AI infrastructure? Share your experiences in the comments below! 👇

Tags: #ai #machinelearning #cerebras #llm #performance #rust #javascript #go #php #webdev #datascience