Quick answer: If you run LLMs 8 or more hours per day, buying a local GPU pays for itself in roughly 5 to 10 months depending on the card. Below about 4 hours a day the payback stretches past two years, which is longer than most people keep a GPU. For occasional or burst usage, cloud GPUs from RunPod or Vast.ai are significantly cheaper.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→The real cost comparison
Most “cloud vs local” analyses miss hidden costs. Here is the full breakdown for running a 70B model in 2026.
Cloud GPU costs (per month, 2026 pricing)
| Provider | GPU | $/hr | 4 hrs/day | 8 hrs/day | 24/7 |
|---|---|---|---|---|---|
| RunPod | RTX 4090 | $0.39 | $47/mo | $94/mo | $281/mo |
| RunPod | A100 80GB | $1.64 | $197/mo | $394/mo | $1,181/mo |
| Vast.ai | RTX 4090 | $0.25 | $30/mo | $60/mo | $180/mo |
| Vast.ai | RTX 3090 | $0.15 | $18/mo | $36/mo | $108/mo |
| Vast.ai | A100 80GB | $1.10 | $132/mo | $264/mo | $792/mo |
Cloud pricing fluctuates. Vast.ai is a marketplace so prices vary by supply and demand. RunPod offers more consistent pricing and reliability. For a detailed comparison of both platforms — pricing tiers, reliability, and which to choose for LLM workloads — see RunPod vs Vast.ai for LLM.
Local GPU costs (total cost of ownership)
| GPU | Purchase | Electricity/mo* | Break-even vs Cloud |
|---|---|---|---|
| RTX 3090 (used) | ~$820 | ~$18 | ~23 months at 8 hrs/day |
| RTX 4090 | ~$2,200 | ~$23 | ~37 months at 8 hrs/day |
| RTX 5090 | ~$4,900 | ~$29 | ~36 months at 8 hrs/day |
Assumes $0.18/kWh (EIA US residential average, June 2026), full rated TDP and an 85%-efficient PSU. Break-even compares each card against renting that same card on RunPod — $0.22/hr for a 3090, $0.34 for a 4090, $0.69 for a 5090 — not against one shared rate.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
When cloud wins
Cloud GPUs make more sense when you:
- Run LLMs less than 2 hours per day — the per-hour cost stays well below the amortized cost of hardware
- Need burst access to A100/H100 — running 70B+ models at full precision requires hardware that costs $10,000+ to buy
- Want zero maintenance — no driver updates, no PSU upgrades, no thermal management
- Are experimenting — trying different model sizes before committing to a hardware purchase
For a hobbyist running Llama 3 70B a few times per week, a Vast.ai RTX 3090 at $0.15/hr costs under $10/month. Buying the card makes no sense at that usage level. Once you’ve decided to rent, our best cloud GPU for LLM guide maps each model size to the exact GPU tier worth paying for.
Try RunPod Cloud GPU→ Try Vast.ai Cloud GPU→When local wins
Buying your own GPU wins when you:
- Run models 8+ hours daily — the break-even point is roughly 5-10 months depending on the card
- Value privacy — your data never leaves your machine
- Run models 24/7 — local is 3-5x cheaper than cloud for always-on inference servers (if that is your plan, our best GPU for an LLM server guide covers the throughput and reliability picks)
- Want zero latency — no network overhead, no cold starts
- Plan to use the GPU for 2+ years — the long tail of ownership is nearly free after break-even
A used RTX 3090 at $820 running 8 hours daily breaks even against Vast.ai pricing in roughly 5 months. Everything after that is pure savings.
The hybrid approach
Many power users in 2026 run a hybrid setup:
- Local RTX 4090 or RTX 5090 for daily 7B-34B model inference
- Cloud A100/H100 for occasional 70B+ runs or fine-tuning jobs
This gives you the best of both worlds — fast, private, cheap daily inference with on-demand access to datacenter GPUs when you need them.
Best local GPUs for the money
| Budget | GPU | Best For |
|---|---|---|
| ~$425 | RTX 4060 Ti 16GB | 7B-13B daily driver |
| ~$820 | RTX 3090 (used) | 34B quantized, best value |
| ~$2,200 | RTX 4090 | 34B comfortable, some 70B |
| ~$4,900 | RTX 5090 | 34B-70B quantized |
For more on choosing the right local GPU, see our best budget GPU for local LLM guide and VRAM requirements guide.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 3090 on Amazon→Buy on Shopee SG→Which option should you choose?
- Using LLMs less than 2 hours per day? Stick with cloud. A Vast.ai RTX 3090 at $0.15/hr costs under $10/month at that usage level. Buying hardware makes no financial sense.
- Using LLMs 4-8 hours daily for work? Buy a local GPU. A used RTX 3090 at $820 breaks even versus cloud in about 5 months, then runs nearly free for years.
- Need 70B+ models occasionally but run 7B-13B daily? Go hybrid. Buy a local RTX 4090 for daily use and spin up a cloud A100 for the occasional large model run.
- Privacy is non-negotiable? Buy local. No cloud provider can guarantee your prompts and data stay private, regardless of their terms of service.
Common mistakes to avoid
- Comparing only GPU rental cost to GPU purchase price. Total cost of ownership includes electricity, PSU upgrades, and cooling. A 350W GPU adds $15-20/month in electricity alone.
- Keeping cloud instances running when idle. Forgetting to shut down a RunPod instance overnight can double your monthly bill. Set auto-stop timers or use serverless endpoints.
- Buying a local GPU for occasional weekend experimentation. If you use LLMs under 10 hours per week, it takes over 2 years to break even on a $900 GPU. Cloud is cheaper for hobbyist usage.
Our recommendation
Start with cloud, buy when you hit a usage threshold. Track your cloud GPU spending for a month. If it exceeds $50-80/month consistently, a local GPU will pay for itself within a year — our cloud GPU TCO vs self-hosted LLM analysis models the exact break-even curves for different usage patterns. If you are already running models daily and know what size you need, skip the cloud trial and buy the hardware — the math is clear.
The cheapest GPU is the one that matches your actual usage pattern, not the one with the lowest sticker price.