Purpose‑built for production AI

High‑Performance Inference Server for LLMs

Deploy GPT‑class and multi‑modal models with speed — ultra‑low latency, high throughput, and long context windows powered by advanced KVCache management and intelligent scheduling.

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Overview

Inferneo is a next‑generation inference server designed to serve large language models with unmatched speed, scalability, and efficiency. It surpasses generic model servers by focusing relentlessly on end‑user latency and predictable throughput.

Through advanced KVCache optimization and a latency‑aware scheduler, Inferneo sustains extended context windows without sacrificing responsiveness. The architecture is engineered for long‑sequence inference, high GPU utilization, and distributed deployment via tensor parallelism and model sharding.

Built for Scale

Numbers that matter when you go to production.

req/min per GPU

sec first‑token target

token context capable

Key Capabilities

⚡ Ultra‑low latency serving

Throughput without compromise

Smart batching and prioritization maximize GPU utilization while keeping tail latency low for realtime experiences.

🧠 KVCache optimization

Long context, efficient memory

Compaction and reuse strategies reduce memory pressure and unlock extended contexts for complex tasks.

🧩 Parallelism & sharding

Scale across devices

First‑class support for tensor parallelism and model sharding to serve massive models across multi‑GPU clusters.

Lightweight, production‑ready runtime with minimal overhead
Observability hooks for tracing, metrics, and health
Safe rollout with canaries and versioned deployments
Supports GPT‑class and multi‑modal models

Quick Start

Spin up an endpoint in minutes. Choose your preferred interface and start serving.

curl -X POST https://api.inferneo.ai/v1/generate \
  -H "Authorization: Bearer $INFERNEO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-class-7b",
    "prompt": "Say hello to the world in one sentence.",
    "max_tokens": 64
  }'

import requests

url = "https://api.inferneo.ai/v1/generate"
headers = {"Authorization": f"Bearer {INFERNEO_API_KEY}", "Content-Type": "application/json"}
payload = {
  "model": "gpt-class-7b",
  "prompt": "Say hello to the world in one sentence.",
  "max_tokens": 64,
}
print(requests.post(url, headers=headers, json=payload, timeout=30).json())

import fetch from "node-fetch";

const res = await fetch("https://api.inferneo.ai/v1/generate", {
  method: "POST",
  headers: { "Authorization": `Bearer ${INFERNEO_API_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ model: "gpt-class-7b", prompt: "Say hello to the world in one sentence.", max_tokens: 64 })
});
console.log(await res.json());

Architecture

Modular components streamline deployment in cloud or on‑prem environments.

Scheduler

Smart batching, prioritization, and preemption to meet strict latency SLOs.

Execution Engine

Optimized kernels with attention to GPU residency and cache locality.

Memory Manager

KVCache compaction and spill strategies tuned for long contexts.

Engineered for Results

Toggle metrics to see how the engine prioritizes user‑perceived performance.

Inferneo

vLLM

Triton

Use Cases

Realtime assistants

Interactive chat, customer support, and copilots with sub‑second TTFB.

Knowledge search

Long‑context retrieval‑augmented generation for enterprise knowledge bases.

Content automation

High‑throughput batch generation for localization, summarization, and transcreation.

FAQ

How is Inferneo different from generic model servers?+

Inferneo focuses on end‑user latency with an intelligent scheduler, KVCache efficiency, and first‑class support for long context — all while keeping the system lightweight and production‑ready.

Does it support distributed deployment?+

Yes. It is engineered for tensor parallelism and model sharding and can scale horizontally across multi‑GPU clusters.

Can I bring my own models?+

Absolutely. Bring any compatible transformer model; adapters make it straightforward to integrate custom architectures.

Get in Touch

Interested in trying Inferneo or collaborating? Reach out and we’ll follow up quickly.

hello@inferneo.ai GitHub