LLM Hosting

Self-Host Your LLM on Managed GPUs

Deploy Llama, Mistral, or DeepSeek on dedicated H100/A100 GPUs. Zero per-token costs. Full data privacy. We manage the infrastructure — you own the model and the output.

Talk to an Engineer See GPU Hardware
OpenAI-compatible API endpoint India datacenter (DPDP) Zero per-token charges 5,000 INR free trial credits
Running production workloads for
Revolt MotorsPC JewellerRR KabelImpresarioIntentwiseLoomBhimaBGaussMitutoyo
~163K
Requests/mo to Beat API Cost (70B)
$3,400
Avg Monthly Savings vs OpenAI API
<15 ms
India DC Latency to Metro Cities
8 min
Median Incident Response Time
24/7
NOC Coverage — Bangalore-Based

What ZenoCloud Handles

You bring the model. We handle everything from bare metal to the API endpoint.

Hardware + CUDA Stack

GPU server racked and tested. Ubuntu 22.04 LTS, CUDA 12.4, cuDNN 9.0, NCCL 2.19 — installed and validated before handoff.

Runtime Configuration

vLLM, Ollama, TGI, or llama.cpp — configured for your model family and concurrency requirements. Production default is vLLM with PagedAttention.

OpenAI-Compatible API

HTTPS endpoint at your subdomain. Swap openai.api_base — drop-in replacement, no application code changes.

Prometheus + Grafana

GPU utilization, request latency (p50/p95/p99), queue depth, KV cache usage. AlertManager rules for Slack or PagerDuty.

Data Privacy by Design

Single-tenant bare metal. No inference logging by ZenoCloud. Weights encrypted at rest with LUKS. DPA signed — no training on your data.

Custom Model Support

HuggingFace Hub (public or private), S3-compatible buckets, or local .safetensors checkpoints. LoRA and PEFT adapters merged or applied at vLLM runtime.

GPU Sizing Guide for LLMs

Primary constraint is VRAM. Rule of thumb: model parameters × 2 bytes (FP16) = minimum VRAM. GPTQ/AWQ 4-bit quantization reduces that by approximately 4x.

L4
VRAM 24GB GDDR6
Models That Fit 7B FP16 (Mistral, Llama 8B), embedding models, Whisper
Per Month (Reserved) ₹30,000 ($360)
RTX 4090
VRAM 24GB GDDR6X
Models That Fit 7B FP16, Phi-4 14B (quantized), Mistral 7B
Per Month (Reserved) ₹58,000 ($700)
L40S
VRAM 48GB GDDR6
Models That Fit 13B FP16, Qwen 2.5 32B (quantized)
Per Month (Reserved) ₹75,000 ($900)
A100 80GB
VRAM 80GB HBM2e
Models That Fit Llama 3.1 70B FP16, Mixtral 8x22B, DeepSeek R1 32B
Per Month (Reserved) ₹1,50,000 ($1,800)
H100 SXM
VRAM 80GB HBM3
Models That Fit 70B FP16 at 2x A100 throughput, Llama 3.3 70B
Per Month (Reserved) ₹1,50,000 ($1,800)
H100 x4 NVLink
VRAM 320GB total
Models That Fit Llama 3.1 405B, DeepSeek V3 671B MoE
Per Month (Reserved) Custom

* Throughput measured at vLLM batch size 8 (7B) and batch size 4 (70B). Multi-GPU NVLink clusters available for 405B+ models — contact for scoping and pricing.

Self-Hosted LLM vs OpenAI / Anthropic API

The break-even is approximately 160,000 requests/month for a 70B model at average prompt/completion length. Above that, self-hosting is cheaper and gives you full data control.

OpenAI / Anthropic API
ZenoCloud Self-Hosted LLM
Monthly cost structure
Variable (per token)
Fixed (₹30K–₹2L/mo)
Data residency in India
DPDP Act 2023 compliant
Zero rate limits
Run any open-source model
PHI / PII stays on your servers
Fine-tuned model deployment
GPT-4 / Claude Opus capability
Zero setup time
Cost-effective below 50K req/mo
Calculate Your Break-Even
FAQ

Frequently Asked Questions

How much does it cost to self-host an LLM?
A 7B model (Mistral 7B, Llama 3.1 8B) on RTX 4090 or L4 GPU costs ₹30,000–₹58,000/month ($360–$700). A 70B model (Llama 3.1 70B) on A100 80GB costs ₹1,50,000/month ($1,800). These are all-in managed prices including hardware, power, bandwidth, OS, runtime, and 24/7 ops. Break-even versus OpenAI API occurs at approximately 150,000–300,000 requests/month.
What is LLM hosting vs OpenAI API cost at scale?
OpenAI API charges per token: roughly $0.011 per average request (500 input + 200 output tokens). At 200,000 requests/month that is $2,200/month with no data residency guarantees and rate limits. Self-hosted Llama 3.1 70B on A100 80GB costs $1,800/month fixed — unlimited tokens, full data control, zero rate limits. Savings grow linearly with volume above break-even.
Can I self-host Llama 3 on GPU in India?
Yes. We deploy Llama 3.1 8B on RTX 4090 or L4, Llama 3.1 70B on A100 80GB, and Llama 3.1 405B on H100 NVLink clusters in our Mumbai datacenter. Lead time is 2–3 business days for single GPU, 5–7 days for H100 clusters. All deployments satisfy DPDP Act 2023 data localization requirements.
Does ZenoCloud support Mistral, DeepSeek, and Qwen hosting?
Yes. Mistral 7B and Mixtral 8x7B run on A100 40GB or 80GB. Mixtral 8x22B requires H100 80GB. DeepSeek R1 (7B, 32B) runs on A100 40GB. DeepSeek V3 (671B MoE) requires an H100 NVLink cluster. Qwen 2.5 32B runs on L40S quantized. We handle model download, runtime configuration, and API endpoint setup.
What monitoring do I get with managed LLM hosting?
Every deployment includes a Grafana dashboard showing GPU utilization, request latency (p50/p95/p99), queue depth, error rate, and KV cache usage. A Prometheus /metrics endpoint lets you scrape into your own observability stack. AlertManager rules send Slack or PagerDuty alerts when p95 latency exceeds configured thresholds. Our Bangalore NOC monitors vLLM health 24/7.
Can I use my own fine-tuned model or HuggingFace private repo?
Yes. Provide a HuggingFace Hub repo URL (public or private with read token), an S3-compatible bucket URL, or a local .safetensors checkpoint. We upload the model to your NVMe storage, encrypted at rest with LUKS. LoRA and PEFT adapters are merged into the base model or applied at vLLM runtime. Custom model setups add approximately one business day to provisioning.
What happens if my vLLM instance crashes at 2 AM?
Our NOC team in Bangalore monitors all deployments around the clock. systemd restarts vLLM within 10 seconds on process crash. If the restart fails (OOM, CUDA error requiring hardware intervention), the NOC investigates and scales resources. Median time from alert to resolution is 8 minutes. Scale-tier clients get a dedicated on-call engineer with direct phone access.
Is there a free trial before I commit?
Yes — 5,000 INR in free GPU credits, no credit card required. Credits cover roughly 100 hours on an L4 or about 22 hours on an A100. We use the trial period to validate your model loads correctly and benchmark throughput at your expected concurrency. Talk to an engineer to activate your trial and get your model running.
Talk to an engineer, not a chatbot

Get Your LLM Running in 5 Business Days

Tell us your model, compliance requirements, and concurrency target. We scope the deployment, confirm lead time, and get you an OpenAI-compatible endpoint with 5,000 INR in free trial credits.

Talk to an Engineer Claim ₹5,000 GPU Credit
+1 714 242 5683 · +91 99991 08033 · support@zenocloud.io

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Security & DPDP Compliance

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