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NEW A100-ready GPU clusters now live →

Build your AI infrastructure
from GPU to models agents systems pipelines intelligence

Glixy Labs deploys production-grade GPU clusters, private LLMs, and cloud infrastructure for startups and enterprises — in 48 to 72 hours.

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99.9% uptime SLA 48–72 hr deployment India-focused support Cheaper than AWS
▦

A100 cluster live

8x GPUs · 640 GB VRAM

✦

LLM deployed

Llama-3 70B · RAG ready

glixy-cluster-01
32 GPUs 2.5 TB VRAM 87% util Mumbai · IN
A100
A100
4090
A100
3090
4090
A100
3090
4090
A100
3090
A100
4090
3090
A100
4090

GPU utilization

Throughput

2.4 PFLOPS
GPU CLUSTERS PRIVATE LLMS RAG SYSTEMS CLOUD DEPLOYMENT KUBERNETES NEURAL NETWORKS ON-PREMISE AI 99.9% UPTIME GPU CLUSTERS PRIVATE LLMS RAG SYSTEMS CLOUD DEPLOYMENT KUBERNETES NEURAL NETWORKS ON-PREMISE AI 99.9% UPTIME

Powered by industry-leading tech

DockerDocker KubernetesKubernetes LangChainLangChain LlamaIndexLlamaIndex Hugging FaceHugging Face TensorFlowTensorFlow PyTorchPyTorch NginxNginx DockerDocker KubernetesKubernetes LangChainLangChain LlamaIndexLlamaIndex Hugging FaceHugging Face TensorFlowTensorFlow PyTorchPyTorch NginxNginx
RedisRedis PineconePinecone WeaviateWeaviate PostgreSQLPostgreSQL SupabaseSupabase TerraformTerraform GitHub ActionsGitHub Actions RedisRedis PineconePinecone WeaviateWeaviate PostgreSQLPostgreSQL SupabaseSupabase TerraformTerraform GitHub ActionsGitHub Actions
What we do

End-to-end AI infrastructure, built for scale

Seven deeply integrated services — from raw GPU compute to production-ready private AI. Pick what you need, or let us architect the full stack.

▦

GPU Cluster Infrastructure

High-performance GPU clusters optimized for AI training, inference, and HPC workloads — without the bottlenecks.

RTX 3090 RTX 4090 A100-ready CUDA
Explore GPU clusters →
✦

LLM Development & Private AI

Production-ready Large Language Models tailored to your business data — secure, scalable, and fully owned.

LangChain LlamaIndex RAG Vector DB
$glixy llm deploy --model llama3-70b → provisioning A100 cluster... → loading weights · 140 GB → RAG index ready · 2.1M docs ✓deployed in 47m
Build private LLM →
☁

Cloud & Server Infrastructure

Production-grade deployment, containerization, and orchestration. From VPS to multi-region scaling.

Docker K8s CI/CD
Deploy to cloud →
◉

Networking & Cloud Services

Enterprise-grade DNS, load balancing, CDN, and secure firewall architecture.

Configure network →
⚙

ML & Neural Networks

Custom ML models — predictions, recommendations, classification, detection — built for your domain.

Train custom model →
⟳

DevOps & Automation

Automated CI/CD, infrastructure as code, real-time monitoring. Faster delivery, fewer surprises.

CODE
→
BUILD
→
DEPLOY
Automate pipeline →
⛨

Security & Private Deployment

On-premise LLMs, encrypted pipelines, access control. Your data never leaves your infrastructure.

Secure deployment → ⚡

Live Security Console

24/7 monitoring across encryption, access, audit, and threat layers — always on.

⛨
Encryption · AES-256
Access · SSO
Audit · Live
Threats · 0
Scanning… SECURE
[14:02:18] key rotated · vault-01
[14:02:09] SSO login · admin@glixy
[14:01:54] brute attempt blocked
View console →
GPU Cluster Infrastructure

Multi-GPU clusters that scale without bottlenecks

Distributed training, multi-node scaling, Kubernetes-based GPU scheduling, and high-speed NVMe storage — pre-configured with optimized CUDA environments.

  • Multi-GPU servers (RTX 3090 / 4090 / A100-ready architecture)
  • Distributed training setup with multi-node scaling
  • Kubernetes-based GPU scheduling and allocation
  • High-speed NVMe storage for fast data access
  • Optimized CUDA environments via NVIDIA CUDA
Learn more → Get a quote

cluster-mumbai-01

Running

A100 #1

94%

A100 #2

88%

4090 #1

62%

4090 #2

58%

3090 #1

31%

A100 #3

91%

3090 #2

22%

4090 #3

71%

Inside the rack

Tour our data center floor

Real GPU racks. Real fans. Real LEDs. Live now in Mumbai and Bangalore.

▦ GLIXY-RACK-04 · MUMBAI

A100 ×8 node-01
A100 ×8 node-02
RTX 4090 ×4 node-03
Storage nvme-pool
Switch 100GbE
RTX 3090 ×4 node-04
Control k8s-master
UPS 10kVA

⚡ live telemetry · refreshing

312 GPUs.
22 TB VRAM. 2.4 PFLOPs.

Hover any rack unit to inspect. Every fan, LED, and load bar reflects a real metric streaming from our Mumbai + Bangalore data centers.

Total throughput
2.4 PFLOPS
Avg utilization
87%
Active jobs
142
Mean GPU temp
68°C

▢ DATA CENTER FLOOR · 4 CABINETS LIVE

CAB-01
CAB-02
CAB-03
CAB-04
LLM Development & Private AI

Build your own private LLM on your own data

From architecture design to deployment — fine-tuned LLMs, RAG systems, vector databases, and an API layer for seamless integration. All on your infrastructure.

  • Custom LLM pipelines (fine-tuning / prompt optimization)
  • Retrieval-Augmented Generation (RAG) systems
  • Vector database integration for semantic search
  • Private AI deployment — on-premise or cloud
  • API layer for integration into apps and workflows
LangChain LlamaIndex Pinecone Weaviate Hugging Face
Learn more → Get a quote 
# Glixy private LLM — RAG pipeline
from glixy import PrivateLLM, RAGStore

llm = PrivateLLM(
  model="llama3-70b",
  deployment="on-prem",
  gpu="a100-cluster-01",
)

store = RAGStore(
  vector_db="weaviate",
  embeddings="bge-large",
)

store.ingest("./company-docs")

response = llm.query(
  "What's our Q3 revenue?",
  context=store,
)
# → "$284,920 (↑ 12.4% YoY)"
LLM architecture

How your private LLM processes a query

From raw text to embeddings to attention layers to a grounded answer — all on your GPUs, in milliseconds.

Input · query
"What's our Q3 revenue?"
Embedding · 4096-d
Transformer · 80 layers
multi-head attn32 heads
Output · response
"$284,920 — up 12.4% YoY."

Parameters

70 B

Context window

128 K

Tokens/sec

142

Time-to-first-token

87 ms

0+

GPU clusters deployed

0hr

Avg deployment time

0%

Uptime SLA target

0%

Cheaper than AWS

Global infrastructure

Deployed where your users are

Mumbai, Bangalore, Singapore, Frankfurt, NYC. Anycast routing puts your AI milliseconds from any user — without paying hyperscaler markup.

Network status · live

All systems
Mumbai · IN32 ms
Bangalore · IN28 ms
Singapore · SG64 ms
Frankfurt · DE118 ms
NYC · US186 ms
Why Glixy Labs

AI + GPU + Cloud — one platform, one team

We're not a reseller. We architect, deploy, and run the entire stack — from bare-metal GPUs to RAG pipelines to production endpoints.

More affordable than AWS

Up to 60% lower compute costs without sacrificing performance or reliability.

48–72 hour deployment

From kickoff to running cluster in days, not months. We move at startup speed.

AI + GPU + Cloud

One team, one platform. End-to-end ownership of every layer of your AI stack.

Private & secure

On-premise LLM options. Encrypted pipelines. Your data stays in your control.

India-focused support

Local team, local time zones, local data residency. Full support in IST.

Scales with you

Start with one GPU. Scale to a 32-node cluster. We grow with your workload.

Highlights

What makes us different

⚡

48hr deploy

Production cluster in 2 days

💰

60% savings

vs equivalent AWS pricing

🔒

On-premise

Air-gap deployment ready

🇮🇳

India support

Local team, IST hours

Docker
Kubernetes
LangChain
LlamaIndex
Hugging Face
TensorFlow
PyTorch
Nginx
Redis
Pinecone
Weaviate
PostgreSQL
Supabase
Terraform
GitHub Actions
Flip to explore

The full stack, in one place

Hover any card to see what's inside.

▦

GPU compute

Multi-GPU servers with NVLink and InfiniBand fabric.

Hover →
▦

312 GPUs online

RTX 3090, 4090, A100. Combined 22 TB VRAM. 87% avg utilization across 47 active clusters.

2.4 PF

✦

Private LLMs

Llama-3, Mistral, Qwen — fine-tuned on your data, deployed on your hardware.

Hover →
✦

1,243 models

Production LLMs serving 14k+ QPS. 92% mean accuracy on customer-defined evals. Zero training data leakage.

14k QPS

☁

Cloud + K8s

Docker, Kubernetes, ArgoCD — production-ready orchestration.

Hover →
☁

99.9% uptime

5 regions, multi-AZ, auto-scaling. 4.2M deployments shipped without a single SLA breach in 2025.

5 regions

⚙

Custom ML

Predictions, recommendations, classification — trained on your domain.

Hover →
⚙

147 models live

Fraud, churn, demand, recsys. Avg AUC 0.91. Mean inference latency 87ms p95.

0.91 AUC

⟳

DevOps

CI/CD, IaC, monitoring, on-call — your platform team in a box.

Hover →
⟳

847 pipelines

Mean deploy time 4m 12s. 99.4% pipeline success rate. Auto-rollback on health failures.

4m 12s

⛨

Security

SOC 2, GDPR, HIPAA — encrypted at every layer.

Hover →
⛨

SOC 2 Type II

Annual audit. AES-256 at rest, TLS 1.3 in transit. Customer-managed keys via HSM.

SOC 2

How it connects

One platform, every layer

From bare-metal GPUs to your API endpoint — wired together, monitored end-to-end.

▦ GPU cluster
⛵ Kubernetes
✦ Glixy core
📚 Vector DB
🔌 API gateway
How we work

From kickoff to production in 4 steps

A clear, accountable process that ships infrastructure in days — not quarters.

1

Discovery & architecture

30-min call to understand your workload, scale targets, and budget. We deliver a written architecture spec within 24 hours.

DAY 1
2

Provisioning & setup

GPU servers, networking, storage, and Kubernetes orchestration provisioned. CUDA environments tuned to your model.

DAY 2
3

Deployment & integration

Models loaded, RAG indexes built, APIs exposed. Integration testing with your existing apps and workflows.

DAY 2–3
4

Handover & monitoring

Dashboards, runbooks, and 24/7 monitoring live. Ongoing support, scaling, and optimization on retainer.

DAY 3+
Customer stories

Trusted by AI teams across India

★★★★★

"Glixy Labs deployed our 8-GPU A100 cluster in 52 hours. Same setup quoted 6 weeks elsewhere. Our LLM training is 3x cheaper than AWS."

RP

Rahul Patel

CTO · AI startup, Bangalore

★★★★★

"On-premise Llama-3 with RAG over 2M internal docs. Compliance team finally said yes. Glixy handled the entire stack end-to-end."

PS

Priya Sharma

VP Engineering · Fintech

★★★★★

"From bare metal to production endpoint in 3 days. The Kubernetes setup, monitoring dashboards, and runbooks are first-class."

AK

Arjun Krishnan

Head of ML · E-commerce

Pricing

Pricing that scales with you

Transparent monthly plans, custom enterprise pricing, and India-friendly billing.

Starter

₹49k/month

Single-GPU node, perfect for early-stage AI projects.

  • 1× RTX 3090 / 4090 GPU
  • 64 GB RAM · 2 TB NVMe
  • Docker + monitoring
  • Email support (24h)
  • No SLA
Start build
Most popular

Growth

₹1.99L/month

Multi-GPU cluster for serious AI training and inference.

  • 4× A100 / 4090 GPUs
  • 256 GB RAM · 8 TB NVMe
  • Kubernetes + auto-scaling
  • RAG pipeline + vector DB
  • 99.9% uptime SLA
  • Priority support (4h)
Start build

Enterprise

Custom

Dedicated cluster + on-premise + private LLM stack.

  • 8–32× A100 GPUs
  • On-premise deployment
  • Private LLM (Llama-3 70B+)
  • Custom SLA + dedicated DevOps
  • SSO · audit logs · compliance
  • 24/7 white-glove support
Contact sales

Need a different config? See full pricing →

FAQ

Common questions

How is Glixy Labs cheaper than AWS?

We operate our own GPU racks in Indian data centers and pass the savings on. No reseller markup, no egress fees, and India-billable currency means up to 60% lower TCO compared to equivalent AWS instances.

What's the realistic deployment timeline?

For most workloads we target 48–72 hours from contract signing to a running cluster. Custom on-premise builds with hardware procurement may take 2–4 weeks. We give a firm timeline in writing after the initial discovery call.

Can my data and LLM stay fully on-premise?

Yes. Our private deployment option installs the entire stack — GPUs, models, RAG indexes, monitoring — on hardware you own. Data never leaves your network. We handle setup, ongoing patches, and support remotely or on-site.

Which GPUs and models do you support?

RTX 3090, RTX 4090, A100, and H100-ready architectures. Open-source models including Llama-3, Mistral, Qwen, Mixtral, plus custom fine-tunes. CUDA, PyTorch, TensorFlow, JAX — all pre-configured and tuned.

Do you handle ongoing monitoring and scaling?

Yes — every plan includes Grafana dashboards, alerting, and a runbook. Growth and Enterprise tiers add proactive scaling, on-call DevOps, and quarterly architecture reviews.

Can I migrate from AWS / GCP / Azure?

Absolutely. We do AWS-to-Glixy migrations every month — including S3 → object storage, EKS → managed K8s, and SageMaker → custom training pipelines. Most migrations complete within a sprint.

Build your AI infrastructure today

From GPU clusters to private LLMs — get a quote and a written architecture in 24 hours.

🚀 Start Build → 📞 Book a Call