Rent an RTX 4070 when you want Ada efficiency on a budget. 12 GB GDDR6X at a 200 W envelope — runs SDXL 1024² with ControlNet, Llama-3 8B FP16 inference at ~80 tok/s, DreamBooth on SD 1.5, ComfyUI graphs with adapter stacks. Spun up in under 90 seconds, billed per-minute, paid in BTC, USDT/USDC or CLORE. Cooler, quieter and newer than Ampere at a friendly hourly rate.
4070 lands in the sweet spot for hobbyists — Ada cores, 12 GB VRAM, low power draw, hardware AV1 encode. Ideal for SDXL, ComfyUI, and 7B-class inference where energy bills matter.
12 GB GDDR6X with modern Ada cores at a fraction of the power draw of a 3090. Runs SDXL 1024² + one ControlNet, 8B FP16 chat at ~80 tok/s, and SD 1.5 DreamBooth without breaking a sweat. The energy-conscious pick for daily AI development work.
SD 1.5, SDXL, ComfyUI workflows. Blender Cycles with OptiX delivers solid 1080p–4K renders at hobbyist-friendly cost.
vLLM and TGI containers run 7B–13B FP16 models with comfortable batch sizes. The cheapest path to production-grade open-source inference.
When you want modern Ada silicon without the 4080's power budget. 12 GB GDDR6X is plenty for SDXL 1024² and 7B FP16 — step up to 4070 Ti or 4080 for batch-2.
// prices are spot-market lows · refreshed every 60 s
Every server is priced by its host. These are the live floors across the marketplace — you'll see hundreds of variants once you're in.
No sales call. No quota request. No three-week procurement. The first four commands are all you need.
Filter the marketplace by RTX 4070, country, GPU count, reliability score, network speed.
Choose a Docker image — PyTorch, vLLM, ComfyUI, Blender — or paste your own.
You get a public endpoint, an SSH key, and Jupyter on port 8888 in under 90 s.
Per-minute billing rounds to the second. Stop the instance and the meter stops with it.
Yes — 12 GB GDDR6X fits SDXL 1024² batch-1 and 7B Llama FP16 inference comfortably. Tighter than a 3090 but cheaper, modern Ada cores, and lower power (200 W vs 350 W). Step up to 4070 Ti or 4080 for batch-2 SDXL or 13B.
Consumer cards on CLORE.AI cover most hobby and indie workflows: Stable Diffusion 1.5 and SDXL, ComfyUI/Automatic1111, Flux.1, LoRA and QLoRA fine-tuning of 7B-13B LLMs, Whisper transcription, video transcoding, Blender Cycles, and game-server hosting. Anything that fits in 8-32 GB VRAM and runs in Docker runs here. You get full root SSH plus a Jupyter template if you want one.
Cold-start lands in roughly 60-90 seconds for a typical Docker image: server allocation, container pull, GPU passthrough, SSH up. Pre-cached templates (PyTorch, ComfyUI, vLLM, Ollama) are faster because the image is already on the host. Once running you pay per minute, so a 10-minute experiment costs ten minutes of rental, not an hour.
On-demand is a fixed per-hour price the host sets; the rental cannot be revoked while you have funds. Spot is auction-style: you bid, the highest bidder runs, and a higher bidder can preempt you. Spot is typically 30-50% cheaper. CLORE.AI charges 2.5% on spot and 10% on on-demand, split 50/50 with the host.
Spot prices on CLORE.AI usually beat RunPod community pricing because there is no centralized markup; you rent directly from the host with a 2.5% spot fee. Vast.ai is the closest comparison, and on consumer cards CLORE.AI is generally within a few cents per hour. Hold CLORE in your wallet for Proof of Holding and you stack up to 50% off the marketplace fee.
Yes. Point at any registry - Docker Hub, GHCR, Quay, your private registry - then set env vars, port forwards, and your SSH public key in the rent dialog. Templates on the platform are just preset configs; nothing is locked down. You get full root inside the container with GPU passthrough.
12 GB Ada at a 200 W envelope — the efficient pick for SDXL, 8B inference, and ControlNet-heavy ComfyUI graphs.
12 GB fits SDXL + a single ControlNet adapter; for stacked adapters move to 4070 Ti or 4080.
Read the guide →Ada cores edge out Ampere on transformer inference per watt — 200 W TDP keeps host costs low.
Read the guide →Subject DreamBooth runs comfortably on 12 GB; SDXL DreamBooth wants a 16 GB card or tight memory tricks.
Read the guide →Side-by-side specs across the consumer tier. Click any row to see that GPU.
Step-by-step guides verified on CLORE.AI hardware. Pick a workload, copy the docker image, ship in minutes.
Per-minute payouts in BTC, USDT, USDC or CLORE. No listing fee, no contracts, withdraw any time.
Hosts around the world are accepting workloads right now. Sign up, top up your wallet, and the next hour is yours.