What is Cerebras — Fastest LLM Inference for AI Agents?

Ultra-fast LLM inference at 2000+ tokens/second. Cerebras provides the fastest cloud inference for Llama and Qwen models with OpenAI-compatible API for instant AI responses.

Is Cerebras — Fastest LLM Inference for AI Agents free to use?

Yes. Cerebras — Fastest LLM Inference for AI Agents is freely available on TokRepo. Check the Source & Thanks section on the asset page for the specific open-source license.

How do I install Cerebras — Fastest LLM Inference for AI Agents?

Visit the asset page on TokRepo and click "Copy for agent" to get the installation instructions. Most assets can be installed with a single command.

Cerebras — Fastest LLM Inference for AI Agents

What is Cerebras Inference?

Cerebras provides the fastest cloud LLM inference available — 2000+ tokens per second for Llama 3.3 70B, roughly 10x faster than traditional GPU inference. Built on Cerebras' custom Wafer-Scale Engine (WSE) chips, it delivers near-instant responses. OpenAI-compatible API means you can swap in Cerebras as a drop-in replacement for any OpenAI-based application.

Answer-Ready: Cerebras is the fastest cloud LLM inference — 2000+ tokens/sec for Llama 70B (10x faster than GPU). Custom wafer-scale chips. OpenAI-compatible API for drop-in replacement. Supports Llama 3.3, Qwen 2.5, DeepSeek. Free tier available.

Best for: Applications needing ultra-low latency AI responses. Works with: Any OpenAI-compatible tool, Claude Code (via Bifrost), LangChain. Setup time: Under 2 minutes.

Speed Comparison

Provider	Llama 3.3 70B Speed	Relative
Cerebras	2,100 tok/s	10x
Groq	750 tok/s	3.5x
Together AI	400 tok/s	2x
AWS Bedrock	200 tok/s	1x
OpenAI (GPT-4o)	150 tok/s	0.7x

Supported Models

Model	Context	Speed
Llama 3.3 70B	8K	2,100 tok/s
Llama 3.1 8B	8K	4,500 tok/s
Qwen 2.5 32B	8K	2,800 tok/s
DeepSeek R1	8K	1,800 tok/s

Features

1. OpenAI-Compatible API

# Drop-in replacement — just change base_url
client = OpenAI(base_url="https://api.cerebras.ai/v1", api_key="...")

2. Streaming

stream = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content, end="")

3. Tool Calling

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
        },
    }],
)

Pricing

Tier	Requests	Price
Free	30 req/min	$0
Developer	Higher limits	Pay-as-you-go
Enterprise	Custom	Custom

FAQ

Q: Why is it so fast? A: Cerebras uses custom wafer-scale chips (WSE-3) — a single chip larger than a GPU that eliminates memory bandwidth bottlenecks.

Q: Can I use it with Claude Code? A: Not directly (Claude Code uses Claude). Use Bifrost CLI to route Haiku-tier requests to Cerebras for speed.

Q: How does quality compare? A: Same models, same quality. Cerebras runs the exact same Llama/Qwen weights — only inference speed differs.