Llama 3 / 4 Model Cloud Hosting India 2026 — From $0.21/hr

Host Llama 3 / 3.1 / 3.3 models for inference or fine-tuning

Deploy Llama Model GPU from $0.31/hr (~₹28/hr) →Recommended: A100 80GB (A100 80GB or H100)

Why AIC Cloud GPU for Llama Model?

Quick Start — Llama Model on AIC Cloud GPU

  1. 1Provision AIC Cloud A100 80GB at /cloud-gpu ($0.31/hr)
  2. 2Install vLLM: `pip install vllm`
  3. 3Download Llama 3 from Hugging Face: `huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct`
  4. 4Serve via vLLM: `vllm serve meta-llama/Meta-Llama-3-8B-Instruct`
  5. 5Query via OpenAI-compatible API endpoint

Features

Llama 3 (8B, 70B), Llama 3.1 (8B, 70B, 405B), Llama 3.3 (70B)
vLLM, Ollama, llama.cpp, TGI all supported
4-bit / 8-bit quantization via bitsandbytes or AWQ
Fine-tuning with LoRA / QLoRA
Multi-GPU support for Llama 405B
INR billing via UPI / Razorpay

Frequently Asked Questions — Llama Model

Which GPU do I need for Llama 3?

Llama 3 8B: RTX 4090 (24 GB) or A100 40GB — runs at full FP16. Llama 3 70B: A100 80GB (4-bit quantization) or 2× A100 40GB FP16. Llama 3.1 405B: 4× A100 80GB or 2× H100 80GB. For production inference, H100 80GB ($1.99/hr) provides best throughput.

Can I fine-tune Llama on AIC Cloud?

Yes — LoRA fine-tuning of Llama 3 8B fits on a single A100 80GB. Full fine-tuning of 70B requires multi-GPU setup (4× A100 minimum). Use Hugging Face Transformers, axolotl, or unsloth for fine-tuning workflows.

How fast can I serve Llama 3 on AIC Cloud A100?

Llama 3 8B on A100 80GB via vLLM: ~200-400 tokens/second single request, 2,000-3,500 tokens/second batched. Llama 3 70B (4-bit quantized): ~50-80 tokens/second single request, 500-800 tokens/second batched.

Where do I get Llama model weights?

Hugging Face Hub (huggingface.co/meta-llama) — accept the Meta license, then download via `huggingface-cli`. Llama 3 weights are gated but free for commercial use (with restrictions for >700M monthly active users).

Is Llama better than Mistral or Qwen?

Depends on use case. Llama 3.3 70B: strong general reasoning, English-first. Mistral 7B / 8x7B: efficient, multilingual. Qwen 2.5: best for code + Chinese/Asian languages. For most English-language tasks, Llama 3 is the safest default. Always benchmark on your specific use case.

Related

LLM Inference

Run LLM inference with vLLM, Ollama, llama.cpp on AIC GPU cloud

Mistral Model

Host Mistral 7B, Mixtral, Codestral models for inference

Qwen Model

Host Qwen 2.5 / 3 models — strong code + multilingual

Ready to deploy Llama Model on AIC Cloud GPU?

A100 80GB from $0.31/hr (~₹28/hr) · Hourly billing · INR via UPI

Get Started →

Chat with us

We reply within minutes