AI Infrastructure · Made in India

The Inference Stackfor Serious AI Teams

Zyora Labs builds GPU-native infrastructure — faster cold starts, smarter compute routing, and fine-tuned models — so you spend less time managing infrastructure and more time building AI.

Powered by World-Class Infrastructure

DigitalOcean
AMD
AWS
Azure
Redis
Google Cloud
NVIDIA
DigitalOcean
AMD
AWS
Azure
Redis
Google Cloud
NVIDIA
DigitalOcean
AMD
AWS
Azure
Redis
Google Cloud
NVIDIA
DigitalOcean
AMD
AWS
Azure
Redis
Google Cloud
NVIDIA
The Problem

The AI stack is a DevOps nightmare

Most teams spend more time fighting infrastructure than building products. We've been there. We built the tools we wished existed.

01

Cold starts kill production LLMs

Standard inference engines take 3-8 seconds to boot. Users leave before the first token.

02💸

GPU access is fragmented and opaque

Cloud GPUs are expensive, unavailable on demand, and locked to single providers.

03🔧

Fine-tuning is a month-long DevOps project

Getting a domain-specific model takes weeks of Kubernetes wrangling, not hours of data work.

Industry benchmarks, 2025

6.2s

Average LLM cold start, standard vLLM deployment

$4.80

Cost per GPU-hour, major cloud providers

18 days

Average time to first fine-tuned model in production

Products

One Stack. Every Layer.

01/07

ZSE

Zyora Server Engine

InferenceGPU-NativeOpen Source

Ultra memory-efficient LLM inference engine with instant cold starts. Pre-quantize models to .zse format — load 11× faster. Runs 32B models on 24GB consumer GPUs.

9.1s cold start on 7B
58.7 tok/s throughput
AMD + NVIDIA support
OpenAI-compatible API
.zse quantized format
Learn More
zse-terminal
$ pip install zllm-zse
$ zse quantize Qwen/Qwen2.5-7B -o ./model.zse
✓ Quantized to INT4 — 5.57 GB (.zse format)
$ zse serve ./model.zse --port 8000
✓ Cold start: 9.1s | VRAM: 5.9 GB
✓ OpenAI-compatible API ready on :8000
Throughput58.7 tok/s
VRAM5.9 GB
HardwareAMD · NVIDIA
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Solutions

Built for Every Builder

Whether you're running an engineering lab, a startup, or a hospital's records system — the Zyora stack fits.

Engineering Institutions

Deploy Bytebuddy or ZYORA-EDU-32B locally. Run AI workshops without cloud costs spiraling. Fully offline-capable.

On-PremiseOffline-FirstZSEzLLM
Campus-wide LLM deployment
Student-facing AI coding assistant
Offline-first, no cloud dependency
Lab management integrations
Get started
0

Cloud Cost

terminal
$ zse serve zyora-edu-32b --offline
✓ Model loaded (1.2 GB VRAM)
✓ 200 students connected
System operationalp99 <42ms
Why Zyora Labs

Infrastructure That Scales

Everything you need to build, deploy, and scale AI — without the complexity.

58.7tok/s

Blazing Fast

Optimized inference pipelines on 7B models. Sub-10s cold starts with pre-quantized .zse format.

99.99%uptime

Production Ready

Battle-tested infrastructure across 6 PoPs. Zero-config TLS, DDoS protection, WAF built-in.

6PoPs

Global Edge

Mumbai, Bangalore, Hyderabad, New York, Frankfurt, Singapore. Sub-15ms latency in India.

AMD + NVIDIA

Hardware Agnostic

Run on consumer RTX 4090 or datacenter H100. Same .zse format, same API, any hardware.

Frequently Asked

Tech Stack

RuntimePython + CUDA
APIOpenAI-compatible
Format.zse (pre-quantized)
LicenseApache 2.0
Infra6 global PoPs
BillingINR native
Open Source Core

ZSE and zLLM are Apache 2.0 licensed. Inspect, modify, and self-host without restrictions. No vendor lock-in, ever.

Get Started

Ready to Deploy Intelligence?

Whether you are evaluating AI for your enterprise, scaling production workloads, or joining our research mission — there is a path forward.

Talk to Sales

Get a custom demo and pricing for your organization. Our team will map the right Zyora model to your use case.

Contact Us

Read the Docs

Explore our API reference, integration guides, fine-tuning tutorials, and model benchmarks.

View Documentation

Join the Team

We are hiring researchers, engineers, and product leaders who want to shape the future of AI.

Open Positions