Self-Hosted LLM Calculator
See which GPUs fit your model, cloud vs local costs, and when self-hosting breaks even vs the API.
Configuration
For cloud instance billing
API (GPT-4o mini)
$4.50
per month
Cloud GPU (spot)
$28.80
NVIDIA T4 16GB
Local GPU (electricity)
$10.08
+ $700.00 upfront
Cloud: Cloud GPU (spot) costs more than API at this volume
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Compatible GPUs for Llama 3.1 8B at Q4 (8+ GB VRAM)
| GPU | VRAM | Provider | On-Demand/mo↑ | Spot/mo | Purchase Price |
|---|---|---|---|---|---|
| NVIDIA T4 16GB | 16 | CHEAPESTGCP / AWS g4dn | $84.00 | $28.80 | — |
| NVIDIA A10G 24GB | 24 | AWS g5 instances | $180.00 | $72.00 | — |
| NVIDIA L4 24GB | 24 | GCP | $192.00 | $84.00 | — |
| NVIDIA L40S 48GB | 48 | Lambda Labs | $480.00 | $192.00 | — |
| NVIDIA A100 40GB | 40 | Lambda Labs | $504.00 | $216.00 | — |
| NVIDIA A100 80GB | 80 | AWS / GCP / Azure | $768.00 | $288.00 | — |
| NVIDIA H100 80GB | 80 | CoreWeave / Lambda | $1,080.00 | $480.00 | — |
| NVIDIA RTX 4090 24GB (local) | 24 | Local / On-prem | — | — | $1,699.00 |
| NVIDIA RTX 3090 24GB (local) | 24 | Local / On-prem | — | — | $700.00 |
| NVIDIA A6000 48GB (local) | 48 | Local / On-prem | — | — | $4,000.00 |
* Cloud prices are spot instance rates as of early 2026. Spot prices fluctuate. Local GPU prices are approximate US market prices. Electricity at $0.12/kWh.
Self-Hosted LLM — FAQ
Quantization reduces model weights from 32-bit or 16-bit floats to 4-bit or 8-bit integers, dramatically reducing VRAM requirements. Q4 quantization (4-bit) reduces VRAM by ~75% vs FP32 with minimal quality loss for most tasks. For production use, Q4_K_M is the recommended balance of size and quality.
A 70B model in Q4 quantization requires ~42GB VRAM (including KV cache). This fits on a single A100 80GB or requires two 24GB GPUs (like RTX 4090s) with tensor parallelism. A single RTX 3090/4090 can run 7B–13B models comfortably.
Cloud GPUs (AWS, Lambda Labs, RunPod) charge hourly and require no upfront investment. Local GPUs require $700–4,000+ upfront but have near-zero variable cost (just electricity ~$20–60/month). Local hardware breaks even in 3–18 months depending on utilization.
Popular options include: Ollama (easiest, local), llama.cpp (C++ runtime, very efficient), vLLM (production serving, GPU), TGI by Hugging Face (production), and LM Studio (GUI for local use). All support GGUF/quantized model formats.
For cloud GPU rental: self-hosting is often cheaper immediately if usage > ~300K tokens/day (varies by model and GPU). For local hardware: the break-even is typically 3–12 months depending on hardware cost and daily token volume.
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