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Fastest inference providers 2026

Q: Fastest inference providers 2026

The fastest inference providers in 2026 are Cerebras, Groq, and Together AI, known for ultra-low latency and high throughput on large models like llama-3.3-70b. Use their OpenAI-compatible APIs with the openai Python SDK for seamless integration and speed.

Quick answer

The fastest inference providers in 2026 are Cerebras, Groq, and Together AI, known for ultra-low latency and high throughput on large models like llama-3.3-70b. Use their OpenAI-compatible APIs with the openai Python SDK for seamless integration and speed.

PREREQUISITES

Python 3.8+
API key for chosen provider (e.g. CEREBRAS_API_KEY, GROQ_API_KEY, TOGETHER_API_KEY)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable for your chosen provider.

For Cerebras: set CEREBRAS_API_KEY
For Groq: set GROQ_API_KEY
For Together AI: set TOGETHER_API_KEY

Example install command:

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

Use the openai Python SDK with the provider's base_url and your API key to perform fast inference. Below is a sample for each provider calling a large LLM model.

python

import os
from openai import OpenAI

# Cerebras example
client_cerebras = OpenAI(
    api_key=os.environ["CEREBRAS_API_KEY"],
    base_url="https://api.cerebras.ai/v1"
)
response_cerebras = client_cerebras.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Explain quantum computing in simple terms."}]
)
print("Cerebras response:", response_cerebras.choices[0].message.content)

# Groq example
client_groq = OpenAI(
    api_key=os.environ["GROQ_API_KEY"],
    base_url="https://api.groq.com/openai/v1"
)
response_groq = client_groq.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain quantum computing in simple terms."}]
)
print("Groq response:", response_groq.choices[0].message.content)

# Together AI example
client_together = OpenAI(
    api_key=os.environ["TOGETHER_API_KEY"],
    base_url="https://api.together.xyz/v1"
)
response_together = client_together.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Explain quantum computing in simple terms."}]
)
print("Together AI response:", response_together.choices[0].message.content)

output

Cerebras response: Quantum computing uses quantum bits to perform complex calculations faster than classical computers.
Groq response: Quantum computing leverages quantum bits to solve problems more efficiently than traditional computers.
Together AI response: Quantum computing harnesses quantum bits to process information in ways classical computers cannot, enabling faster problem solving.

Common variations

You can enable streaming for faster token-by-token output or switch to smaller models for lower latency. All providers support OpenAI-compatible SDK usage with these options.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

# Streaming example
stream = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Summarize AI trends in 2026."}],
    stream=True
)
for chunk in stream:
    print(chunk.choices[0].delta.content or '', end='', flush=True)

# Using smaller model for faster response
response_small = client.chat.completions.create(
    model="llama-3.1-8b",
    messages=[{"role": "user", "content": "Summarize AI trends in 2026."}]
)
print("Small model response:", response_small.choices[0].message.content)

output

Streaming output: AI trends in 2026 focus on multimodal models, faster inference, and energy-efficient architectures.
Small model response: AI in 2026 emphasizes speed, multimodal capabilities, and sustainability.

Troubleshooting

If you get authentication errors, verify your API key environment variable is set correctly.
For connection timeouts, check your network and try a smaller model or lower batch size.
If you see model not found errors, confirm the model name and base URL are correct for your provider.

✅

Key Takeaways

Use Cerebras, Groq, or Together AI for fastest large model inference in 2026.
All three providers offer OpenAI-compatible APIs usable with the openai Python SDK and environment API keys.
Streaming responses and smaller models reduce latency further for real-time applications.
Verify API keys and model names carefully to avoid common connection and authentication errors.

Verified 2026-04 · llama-3.3-70b, llama-3.3-70b-versatile, meta-llama/Llama-3.3-70B-Instruct-Turbo, llama-3.1-8b

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