Blazing Speed
Custom CUDA kernels & quantization deliver answers before you can blink.
Run Llama 4 Maverick responses in 1.7× the speed of vanilla inference – no infra hassle.
from openai import OpenAI
client = OpenAI(
base_url="https://api.llamaspeed.com/v1/fast",
api_key=YOUR_API_KEY,
)
completion = client.chat.completions.create(
model="fastllama4",
messages=[
{"role": "user", "content": "Who are you?"}
]
)
import asyncio
from openai import AsyncOpenAI
client = AsyncOpenAI(
base_url="https://api.llamaspeed.com/v1/fast",
api_key=YOUR_API_KEY,
)
async def main():
stream = await client.chat.completions.create(
model="fastllama4",
messages=[
{"role": "user", "content": "What is the meaning of life?"}
],
stream=True
)
async for chunk in stream:
if chunk.choices[0].delta.content is not None:
print(chunk)
We benchmarked Llama Speed's API across other frontier model providers on reproducing the ShareGPT dataset and measured throughput in tokens / second for each subsequent token after the first token was generated.
Custom CUDA kernels & quantization deliver answers before you can blink.
One HTTPS endpoint, auto‑scaling. Focus on product, not GPUs.
Token‑level SSE lets your users read as the model thinks.