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Cerebras runs Llama models on its wafer-scale chips and delivers inference speeds well beyond anything available from GPU-based providers — frequently over 1000 tokens per second. It’s the right pick when latency is the dominant constraint: real-time customer support, voice agents, autocomplete-style UIs, and high-throughput agent swarms.

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Installation

pip install -U swarms

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Environment Setup

export CEREBRAS_API_KEY="..."
Get an API key at cloud.cerebras.ai.

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Quick Start

Every Cerebras model uses the cerebras/ prefix: 
from swarms import Agent

agent = Agent(
    agent_name="Cerebras-Agent",
    model_name="cerebras/llama-3.3-70b",
    max_loops=1,
)

print(agent.run("Summarize the architectural innovations behind wafer-scale compute in three paragraphs."))

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Model Names

Modelmodel_nameBest for
Llama 3.3 70B"cerebras/llama-3.3-70b"Default — frontier open model at peak speed
Llama 3.1 70B"cerebras/llama3-70b-instruct"Llama 3.1 70B instruction-tuned
Llama 3.1 8B"cerebras/llama3.1-8b"Smaller, even faster

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Speed-Critical Use Cases

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Voice Agent Loop

Cerebras’s speed is what makes real-time voice agents feel natural — the model can respond in tens of milliseconds: 
from swarms import Agent

voice_agent = Agent(
    agent_name="Voice-Assistant",
    model_name="cerebras/llama-3.3-70b",
    system_prompt="You are a friendly voice assistant. Keep responses under 2 sentences.",
    streaming_on=True,
    max_loops=1,
)

# Plug into your TTS / STT pipeline
voice_agent.run("What's a good weeknight dinner I can make in 20 minutes?")

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High-Volume Classification

When you need to process thousands of items per minute: 
from swarms import Agent

classifier = Agent(
    agent_name="Cerebras-Classifier",
    model_name="cerebras/llama3.1-8b",
    system_prompt="Classify each input as one of: support, sales, billing, other. Reply with the label only.",
    max_loops=1,
)

for ticket in tickets:
    label = classifier.run(ticket)
    route(ticket, label)

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Streaming

Streaming on Cerebras feels essentially instant: 
from swarms import Agent

agent = Agent(
    agent_name="Streaming-Cerebras",
    model_name="cerebras/llama-3.3-70b",
    streaming_on=True,
    max_loops=1,
)

agent.run("Write a 200-word explanation of how transformer attention works.")

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Massive Parallel Agent Swarms

Cerebras’s speed compounds in multi-agent setups — 20 agents in parallel can still finish in a couple seconds: 
from swarms import Agent, ConcurrentWorkflow

agents = [
    Agent(
        agent_name=f"Reviewer-{i}",
        model_name="cerebras/llama-3.3-70b",
        system_prompt=f"You are reviewer #{i}. Give a one-paragraph critique.",
        max_loops=1,
    )
    for i in range(20)
]

workflow = ConcurrentWorkflow(agents=agents)
reviews = workflow.run("Draft proposal: build an in-house vector database instead of using Pinecone.")

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Tool Use

Cerebras’s Llama models support function calling: 
from swarms import Agent

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"{city}: 21°C, partly cloudy"

agent = Agent(
    agent_name="Cerebras-Assistant",
    model_name="cerebras/llama-3.3-70b",
    tools=[get_weather],
    dynamic_temperature_enabled=True,
    max_loops=3,
)

print(agent.run("What's the weather in Tokyo right now?"))

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Production Defaults

from swarms import Agent

agent = Agent(
    agent_name="Production-Cerebras",
    model_name="cerebras/llama-3.3-70b",
    max_loops=1,
    persistent_memory=True,
    context_compression=True,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)

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Next Steps