LLM inference APIs
Serve quantized 7B–70B-class LLMs behind your own OpenAI-compatible endpoint. vLLM or TGI on GPU, llama.cpp / Ollama on big-CPU. Bill your customers by token.
AI VPS Hosting
AI workloads,
pick your shape.
High-RAM CPU for inference / RAG, or NVIDIA-class GPU for training, same VPS panel.
Independent cloud, since 2008. From $2.48/mo · root SSH in 60 seconds.
CPU from $2.48/mo · GPU plans on pricing · 14-day money-back
~ ssh root@ai-nyc-001
connected
root@ai-nyc-001:~# curl -fsSL https://ollama.com/install.sh | sh
Installing Ollama runtime... done
root@ai-nyc-001:~# ollama run llama3.1:8b-instruct-q4
pulling manifest · downloading 4.7 GB to NVMe
model ready · CPU inference starting
root@ai-nyc-001:~# curl localhost:11434/api/generate -d '...'
{"response":"Hello! How can I help you today?"}
root@ai-nyc-001:~# _
AI VPS at a glance
Cloudzy offers AI VPS hosting in two shapes, high-RAM CPU plans for quantized LLM inference, RAG, and pipelines, plus NVIDIA-class GPU plans for training and large-model serving. Plans run on AMD EPYC, NVMe storage, and 40 Gbps uplinks across 12 regions. CPU starts at $2.48 per month; provisioning takes 60 seconds; CUDA images are pre-baked on GPU plans. Cloudzy has operated independently since 2008, serves 122,000+ developers, and is rated 4.6 / 5 by 708+ reviewers on Trustpilot.
- CPU starts at
- $2.48 / month
- GPU types
- RTX · Pro
- Provisioning
- 60 seconds
- Regions
- 12 worldwide
- Uptime SLA
- 99.95%
- Money-back
- 14 days
Why AI builders pick Cloudzy
A cloud that ships AI.
Four reasons your AI workload belongs here.
AMD EPYC + NVMe
Latest EPYC for CPU inference, NVMe for fast model loads. Dedicated GPUs via PCI passthrough on GPU plans.
14-day money-back
Run your real inference latency test on Cloudzy. If it doesn't fit your SLO, refund inside 14 days.
99.95% uptime
Production AI APIs need a host that doesn't reboot during peak. Last-30-day SLA tracked publicly at status.cloudzy.com.
Engineers on chat
Stuck on CUDA versions, NCCL errors, or vLLM tuning? Engineers with AI workload experience, minutes not hours.
The AI stack
Bring whatever framework.
It runs.
PyTorch, TensorFlow, JAX, vLLM, TGI, Ollama, llama.cpp, sglang, all run cleanly. Pre-baked CUDA images on GPU plans skip the driver dance. CPU plans handle quantized inference and embedding workers cheaply.
Docker + nvidia-container-toolkit ready on GPU plans
PyTorch
CPU & GPU
TensorFlow
CPU & GPU
vLLM
GPU LLM serving
Ollama
CPU + GPU LLMs
Hugging Face
Transformers · Diffusers
pgvector
RAG vector store
Qdrant
Vector DB
LangChain
Agent framework
Use cases
What AI teams run on
Cloudzy.
RAG backends
Postgres + pgvector or Qdrant on a CPU VPS, optional GPU box for embedding/generation. NVMe means vector lookups stay snappy.
Agent runtimes
Long-running LangChain or LlamaIndex agents that hit OpenAI/Anthropic APIs and your own data. Static IP keeps tool-calling stable.
Image / video generation
Stable Diffusion, SDXL, ComfyUI, video models on RTX-class GPUs. NVMe lets you swap models in seconds, not minutes.
Fine-tuning & training
LoRA / QLoRA fine-tunes on RTX-class, full-parameter training on datacenter-class GPUs. Pre-baked CUDA, NCCL, PyTorch.
Embedding workers
Run a sentence-transformers worker on a 16–32 GB CPU VPS to embed millions of docs without paying per-call SaaS rates.
60s
Provisioning
40 Gbps
Uplink
NVMe-only
Storage
12
Regions
99.95%
Uptime SLA
14 days
Money-back
Global network
12 regions. Four continents.
Inference latency, solved.
Place your AI API close to your customers. Pair a CPU gateway in one region with a GPU box in another.
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CPU AI plans
Quantized LLMs · RAG · Embeddings. CPU is enough.
Many AI workloads are CPU-bound. Hourly billing · 50% off all plans · GPU plans listed separately on /pricing.
Most popular
4 GB DDR5
Quantized 7B inference · CPU
$14.47 /mo
$28.95/mo −50%
Deploy now
14-day money-back
- 2 vCPU @ EPYC
- 120 GB NVMe
- 5 TB · 40 Gbps
- Ollama / vLLM CPU
- Root SSH · KVM
12 GB DDR5
RAG backend · vector DB · embeddings
$34.98 /mo
$69.95/mo −50%
Deploy now
14-day money-back
- 4 vCPU @ EPYC
- 300 GB NVMe
- 8 TB · 40 Gbps
- Ollama / vLLM CPU
- Root SSH · KVM
16 GB DDR5
Mid-size CPU inference · API gateway
$49.98 /mo
$99.95/mo −50%
Deploy now
14-day money-back
- 8 vCPU @ EPYC
- 350 GB NVMe
- 10 TB · 40 Gbps
- Ollama / vLLM CPU
- Root SSH · KVM
Most popular
24 GB DDR5
Big-RAM CPU · agents · pipelines
$69.97 /mo
$139.95/mo −50%
Deploy now
14-day money-back
- 8 vCPU @ EPYC
- 450 GB NVMe
- 12 TB · 40 Gbps
- Ollama / vLLM CPU
- Root SSH · KVM
FAQ. AI VPS
Common questions, straight answers.
What is an AI VPS?
An AI VPS is a Linux cloud server sized and configured for AI workloads, high RAM and EPYC cores for CPU inference and RAG, or NVIDIA-class GPUs for training and large-model serving. You SSH in, install your stack, and run. Same VPS, different shapes for different jobs.
Do I need a GPU, or will CPU work?
Depends on the model. Quantized 7B-class LLMs (int4 / int8 via llama.cpp or Ollama) run usefully on a 16–32 GB CPU plan. Embedding models, vector databases (Qdrant, Weaviate, pgvector), and RAG pipelines are mostly CPU-bound. For training, larger model serving, or anything throughput-heavy, you want a GPU plan.
Can I run an inference API behind a load balancer?
Yes. Run vLLM, TGI, or your own FastAPI service on a GPU box, put a small CPU VPS in front as the API gateway and rate limiter. Both share a private network in the same region. 40 Gbps means the gateway is never the bottleneck.
Can I host a RAG backend?
Yes, and it's one of the most common shapes. A 16–32 GB CPU VPS runs Postgres + pgvector or Qdrant cheaply, you call out to a GPU VPS or hosted LLM for generation. NVMe makes vector queries snappy, EPYC handles the embedding compute when you batch.
Which AI frameworks are supported?
All of them. PyTorch, TensorFlow, JAX, ONNX, llama.cpp, Ollama, vLLM, TGI, sglang, MLX (on the appropriate hardware), Hugging Face Transformers, install via conda, pip, or Docker. Pre-baked CUDA images on the GPU plans, full root on every plan.
Are the GPUs shared?
No. GPU plans use PCI passthrough, the GPU you book is dedicated to your VM, full memory and full clocks. CUDA, NVENC, NCCL all behave the same as on a bare-metal box. RTX-class for cost-effective inference, datacenter-class for high-end training.
How much VRAM do I need?
8 GB for SDXL or 7B-class LLMs at int4. 24 GB for 13B at fp16 or 70B at int4. 40+ GB for fp16 70B and full-precision training. Match the GPU plan to your model size, quantization changes the math, so test before committing to a tier.
Is there a money-back guarantee?
Yes, 14 days from purchase, full refund, no questions asked. Run your real inference latency test, your real RAG benchmark, and decide if Cloudzy fits before you commit to a year.
How fast is provisioning?
Once payment is confirmed, your AI VPS is live in 60 seconds. CPU or GPU. Pre-baked CUDA images on GPU plans mean `nvidia-smi` returns within seconds. CPU plans ship with Ubuntu LTS or Debian, install your AI stack via conda or pip in a few minutes.
Can I use this in production?
Yes. 99.95% uptime SLA, hourly billing, no commitments, dedicated IPs, and the option to scale RAM/vCPU/storage live without rebuild. Many of our customers run AI inference and RAG APIs in production from Cloudzy.
Ready when you are.
AI VPS in 60 seconds.
Pick the shape your workload needs. CPU for inference / RAG; GPU for training. Same panel.
No credit card required · 14-day money-back guarantee · Cancel anytime