Cheap LLM Inference Hosting India 2026 — From $0.21/hr GPU
Run LLM inference with vLLM, Ollama, llama.cpp on AIC GPU cloud
Why AIC Cloud GPU for LLM Inference?
- ✓A100 80GB at $0.31/hour with INR billing — among cheapest globally
- ✓H100 80GB at $1.99/hour for production-scale inference
- ✓RTX 4090 at $0.21/hour for small model inference / dev
- ✓Pre-installed CUDA + PyTorch + TensorFlow + vLLM containers
- ✓Hourly billing — pay only for inference time
Quick Start — LLM Inference on AIC Cloud GPU
- 1Provision AIC Cloud GPU instance at /cloud-gpu (A100 80GB or H100)
- 2SSH in — CUDA + PyTorch pre-installed
- 3Install vLLM: `pip install vllm`
- 4Download model: `huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct`
- 5Serve with vLLM: `python -m vllm.entrypoints.api_server --model meta-llama/Meta-Llama-3-8B-Instruct`
Features
Frequently Asked Questions — LLM Inference
Which GPU should I use for LLM inference?
Llama-3 8B / Mistral 7B: RTX 4090 ($0.21/hr) or A100 40GB. Llama-3 70B: A100 80GB ($0.31/hr) or 2× A100 40GB. Llama-3 405B / Mixtral: 4× A100 80GB or H100. For low-latency production inference at scale, H100 80GB ($1.99/hr) is the premium choice.
What's the throughput of vLLM on A100 80GB?
For Llama-3 8B on A100 80GB with vLLM, expect ~150-300 tokens/second per request, with batch processing reaching 2,000-3,000+ tokens/second across multiple concurrent requests. Real numbers depend on prompt length, output length, and quantization (FP16 vs INT8 vs INT4).
Should I use vLLM, Ollama, or llama.cpp?
vLLM: best for production HTTP inference at scale (highest throughput). Ollama: simplest setup, good for development and small workloads. llama.cpp: best for CPU/quantized inference, runs without CUDA. TGI (Hugging Face): excellent for production with proper batching. For most production LLM hosting, vLLM is the strongest default.
How does AIC Cloud GPU pricing compare to AWS / GCP?
AWS p4d (8× A100): $32.77/hour. GCP a2-highgpu-8g (8× A100): ~$29/hour. AIC Cloud A100 80GB: $0.31/hour. AWS is roughly 100× more expensive per GPU than AIC Cloud. For pure GPU compute (no AWS ecosystem needed), AIC Cloud is dramatically cheaper.
Can I host a custom fine-tuned model?
Yes — upload your model weights to the GPU instance via scp/rsync or download from Hugging Face Hub. vLLM, Ollama, and llama.cpp all support custom models (PyTorch, GGUF, AWQ, GPTQ formats). For very large models (70B+), ensure your GPU has enough VRAM (need 2× A100 80GB for 70B FP16, single A100 80GB for 4-bit quantized).
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Ready to deploy LLM Inference on AIC Cloud GPU?
A100 80GB instance from $0.31/hr (~₹28/hr) · Hourly billing · INR via UPI
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