How to Run LLMs in the Cloud Cheaply (2026)
Last updated: 2026-06-16 — prices change frequently, click through to confirm.
The cheapest way to run Llama, Mistral, Qwen and other open-source LLMs on rented GPU cloud — without paying for more than you need.
How to Run LLMs in the Cloud Cheaply (2026)
Running large language models locally requires serious GPU hardware. If you don’t have an RTX 4090 or better sitting in your PC, cloud GPU rental is the cheapest path to running 7B–70B models without buying hardware.
This guide covers the cheapest setups for inference and experimentation — not training, which needs longer runs and more VRAM.
What VRAM Do You Actually Need?
Model size determines minimum VRAM. These are rough numbers for 4-bit quantized models (Q4_K_M via llama.cpp or Ollama):
| Model Size | Min VRAM | Recommended |
|---|---|---|
| 7B | 6 GB | 8 GB |
| 13B | 10 GB | 12 GB |
| 30B | 20 GB | 24 GB |
| 70B | 40 GB | 48 GB+ |
| 70B (full precision) | 140 GB | Multi-GPU |
For most use cases — Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B — a single 24GB GPU is plenty.
Cheapest Options by Use Case
Just testing / short sessions
Vast.ai community GPUs — RTX 3090 (24GB) bids from ~$0.15–0.25/hr. Run Ollama, pull a model, test for an hour, terminate. Total cost: under $0.50.
Regular inference / API endpoint
RunPod community cloud — RTX 4090 (24GB) from ~$0.35–0.50/hr. More stable than Vast.ai, good for running an inference server for a few hours a day.
70B models
RunPod A100 80GB — ~$2.49/hr. Can run Llama 3.1 70B at Q4 comfortably. For occasional use, spin up, run your batch, terminate.
Vast.ai A100 — often cheaper than RunPod but less consistent availability.
Recommended Stack
The cheapest working setup for cloud LLM inference:
- Rent a RunPod or Vast.ai pod with enough VRAM for your model
- Install Ollama — single command, handles model downloads and serving
- Pull your model —
ollama pull llama3.1:8bor similar - Expose port 11434 — access the API from anywhere
- Terminate when done — don’t leave it running idle
Total cost for a 2-hour Llama 3.1 70B session on an A100: ~$5.
Alternatives to Cloud GPU
Before renting, check these free or near-free options:
- Google Colab free tier — T4 GPU, limited hours, good for Llama 7B
- Groq — free API tier for Llama and Mistral, fast inference
- Together.ai / Fireworks — cheap per-token API pricing for 70B+ models
- Your own hardware — if you have an RTX 3080 or better, Ollama runs fine locally
Cloud GPU rental makes sense when you need: sustained throughput, a specific model not available via API, or full control over the inference environment.
Live GPU Pricing for LLM Workloads
- Cheapest RTX 4090 cloud rental — best for 7B–30B models
- Cheapest A100 80GB rental — best for 70B models
- RunPod vs Vast.ai — which is cheaper?
Pricing data updated daily. Last pull: {{ .Params.lastUpdated }}