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 trial node before you commit
Running production workloads for
Revolt MotorsPC JewellerRR KabelImpresarioIntentwiseLoomBhimaBGaussMitutoyo
What LLM hosting means
LLM hosting
LLM hosting is the deployment of a large language model, Llama, Mistral, or DeepSeek, on GPU infrastructure a customer controls rather than calling a third-party API. ZenoCloud provisions dedicated H100 or A100 GPUs in India, manages the CUDA and vLLM stack, and bills a fixed monthly cost with zero per-token charges.
Self-hosted LLM
A self-hosted LLM runs on infrastructure the customer or its provider controls, keeping model weights and inference data off a third-party API. ZenoCloud manages the underlying GPU and serving stack while the customer retains the model and its output, with an India datacenter supporting DPDP-compliant data handling.
150–300K
Req/mo Break-Even vs API (Model-Dependent)
0
Per-Token Charges
<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.

L40S
VRAM 48GB GDDR6
Models That Fit 7B–13B FP16 (Mistral, Llama 8B), Qwen 2.5 32B (quantized)
Per Node / Month ₹55,000$599 ≈ ₹75/hr effective≈ ₹75/hr effective
RTX PRO 6000
VRAM 96GB GDDR7
Models That Fit 70B 4-bit (AWQ/GPTQ), 32B FP16, image generation
Per Node / Month ₹1,10,000$1,249 ≈ ₹151/hr effective≈ ₹151/hr effective
A100 80GB
VRAM 80GB HBM2e
Models That Fit Llama 3.1 70B, Mixtral, DeepSeek R1 32B
Per Node / Month ₹97,000$1,099 ≈ ₹133/hr effective≈ ₹133/hr effective
H100 80GB
VRAM 80GB HBM3
Models That Fit 70B at 2x A100 throughput, Llama 3.3 70B
Per Node / Month ₹1,80,000$2,099 ≈ ₹247/hr effective≈ ₹247/hr effective
H200 141GB
VRAM 141GB HBM3e
Models That Fit 70B FP16 single-GPU headroom, long-context serving
Per Node / Month ₹2,50,000$2,799 ≈ ₹342/hr effective≈ ₹342/hr effective
H200 / B200 NVLink
VRAM Multi-GPU
Models That Fit Llama 3.1 405B, DeepSeek V3 671B MoE
Per Node / Month Custom

* Monthly commitment, 1-month minimum — no hourly product; effective ₹/hr (monthly ÷ 730) shown for comparison only. Managed ops add-on: ₹15,000 ($179) per node/month. More configurations available on request. Multi-GPU NVLink clusters for 405B+ models on custom pricing.

Self-Hosted LLM vs OpenAI / Anthropic API

The break-even is roughly 150,000–300,000 requests/month depending on model and traffic shape. 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 (₹55K–₹2.5L/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–13B model (Mistral 7B, Llama 3.1 8B) on an L40S node costs ₹55,000/month ($599). A 70B model (Llama 3.1 70B) on A100 80GB costs ₹97,000/month ($1,099), or ₹1,80,000/month ($2,099) on H100 for higher throughput. Prices are per node per month, 1-month minimum; the managed ops add-on is ₹15,000 ($179) per node. Break-even versus OpenAI API is roughly 150,000–300,000 requests/month depending on model and traffic shape.
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 $1,099/month fixed — unlimited tokens, full data control, zero rate limits. Break-even is roughly 150,000–300,000 requests/month depending on model and traffic shape; savings grow linearly above it.
Can I self-host Llama 3 on GPU in India?
Yes. We deploy Llama 3.1 8B on L40S, Llama 3.1 70B on A100 80GB or H100, and Llama 3.1 405B on H200 or B200 NVLink clusters at our Mumbai location. Lead time is 2–3 business days for single GPU, 5–7 days for multi-GPU 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 80GB. Mixtral 8x22B requires H100 or H200. DeepSeek R1 (7B, 32B) runs on A100 80GB. DeepSeek V3 (671B MoE) requires an H200 or B200 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. P1 alerts get an engineer response in under 15 minutes. Scale-tier clients get a dedicated on-call engineer with direct phone access.
Can I evaluate a node before the monthly term begins?
Yes. We provision a trial node so you can validate on the exact hardware before the monthly term begins. We use the trial to validate your model loads correctly and benchmark throughput at your expected concurrency. Nearly every client evaluates on a trial node first; request one and our engineering team sets it up.
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 can provision a trial node before you commit.

Talk to an Engineer Request a Quote
+1 714 242 5683 · +91 99991 08033 · support@zenocloud.io

Related AI Services

Other products in the ZenoCloud AI / GPU pillar.

AI Inference Hosting

Vision, speech, embeddings — production inference at scale

AI Model Training

Fine-tuning on A100 / H100 clusters

ML Infrastructure

Storage, scheduling, MLOps integration

GPU Hosting Catalog

L40S to B200-class — specs and pricing

H100 GPU Servers

NVIDIA H100 80GB SXM — availability and specs

Security & DPDP Compliance

Data handling policies and compliance posture