The Llama 3.1 API for Production Inference

Call Llama 3.1 in three lines

OneInfer's endpoint is OpenAI-compatible, so the official OpenAI SDKs work unchanged — just point the base URL at OneInfer.

from openai import OpenAI

client = OpenAI(base_url="https://api.oneinfer.ai/v1", api_key="YOUR_ONEINFER_KEY")

resp = client.chat.completions.create(
    model="llama-3.1-70b",
    messages=[{"role": "user", "content": "Explain kernel fusion in one paragraph."}],
    stream=True,
)
for chunk in resp:
    print(chunk.choices[0].delta.content or "", end="")

Also available: llama-3.1-8b for lean, low-latency paths and llama-3.1-405b for frontier reasoning. Streaming, multi-turn, system prompts and function calling are supported — see the Chat Completions docs.

Which Llama 3.1 size should you use?

Why run Llama 3.1 on OneInfer

Kernel-optimized for the model, not just the framework

OneInfer forges Triton and CUDA kernels tuned to Llama 3.1's exact tensor shapes and hot paths, then benchmarks and ships the fastest — throughput you don't get from a generic serving stack.

Intelligent routing and failover

Short prompts take a lean, low-latency path; heavy reasoning is elevated automatically. If a provider slows or fails, traffic rebalances without you paging anyone at 3AM.

One API, every modality

The same key that serves Llama 3.1 also serves vision, audio and video models — and multi-model Chains stitch them together with under 1ms of overhead.

Related: All Model APIs · Comparisons · Best LLM inference API 2026 · Chat Completions API · Cost estimator