Log in Rent RTX 4080
RTX 4080 · live marketplace — from $0.18 / hr

Rent an RTX 4080
by the minute.
Not the month.

Rent an RTX 4080 for production diffusion and 7B/13B serving. 16 GB GDDR6X with 9,728 Ada cores — the cheapest card that runs SDXL batch-4 at ~6.5 it/s, serves Llama-3 8B FP16 via vLLM, and finishes 8B QLoRA fine-tunes in 2–3 hours of spot rental. Spun up in under 90 seconds, billed per-minute, paid in BTC, USDT/USDC or CLORE. The 16 GB production tier.

Per-minute billing SSH + Docker + Jupyter Spot & on-demand 42 regions
SHELL ~/projects/finetune
# install the official Python SDK + CLI $ pip install clore-ai $ clore search --gpu "RTX 4080" --max-price 2.0 486 servers · cheapest spot $0.18/hr · cheapest on-demand $0.27/hr $ clore deploy 38201 --image cloreai/ubuntu22.04-cuda12 --type on-demand --currency bitcoin order #82144 created · waiting for boot… running · ssh ready $ clore ssh 82144
GPU
RTX 4080 ×1
VRAM
24 GB
Rate
$0.27/hr
Status
Running
$0.18/hr
Starting on-demand price
24GB
GDDR6X VRAM per card
42
Regions with 4080 hosts
<90s
Cold-start to ready
workloads

The budget workhorse,
still going strong.

Three years in, the 4080 is still one of the most rented cards on the network. NVLink-capable, 16 GB, and cheap — ideal for hobbyists, students, and side projects.

16 GB Ada — real serving stack territory

16 GB is the floor where vLLM with 16-request KV cache fits cleanly for 8B FP16 serving — the spec where hobby diffusion turns into real production batch work. ~70% of 4090 throughput at ~55% of the rental price, and FP8 inference paths supported.

Llama-3 8B QLoRA ~7.8k tok/s

Diffusion & rendering

SD 1.5, SDXL, ComfyUI workflows. Blender Cycles with OptiX delivers solid 1080p–4K renders at hobbyist-friendly cost.

SDXL 1024² batch-2 1.8 it/s

Inference at scale

vLLM and TGI containers run 7B–13B FP16 models with comfortable batch sizes. The cheapest path to production-grade open-source inference.

Mistral-7B FP16 64 tok/s/user
why 4080

Same VRAM as the 4090,
at half the price.

Older silicon, but 16 GB is 16 GB. For workloads that fit, the 4080 is the cheapest path to a real GPU. Specs from Nvidia's reference sheet.

RTX 4080 RTX 4090 A100 80GB H100 80GB
Architecture Ada Lovelace Ada Lovelace Ada Lovelace Hopper
CUDA cores 9,728 16,384 6,912 14,592
VRAM 16 GB GDDR6X 16 GB GDDR6X 80 GB HBM2e 80 GB HBM3
Memory bandwidth 716 GB/s 1,008 GB/s 1,935 GB/s 3,350 GB/s
FP16 / BF16 (dense) ~71 TFLOPS ~165 TFLOPS 312 TFLOPS 756 TFLOPS
From / hr (on-demand) $0.18 $0.31 $1.20 $2.40

// prices are spot-market lows · refreshed every 60 s

pricing

Two ways to rent.
Pay only for the minutes you use.

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.

Spot

$0.18 / hr
≈ 0.0000025 BTC · 180 CLORE
  • Lowest possible rate
  • Per-minute billing
  • Can be interrupted by on-demand renter
  • Best for batch training, rendering
Browse spot 4080s
MOST RENTED

On-demand

$0.27 / hr
≈ 0.0000038 BTC · 280 CLORE
  • Guaranteed availability
  • No preemption, ever
  • Per-minute billing
  • Best for inference, dev work, demos
Rent on-demand
Pay with
Bitcoin on-chain
CLORE native token
USDT / USDC ERC-20 · BEP-20
workflow

Four steps to a running 4080.

No sales call. No quota request. No three-week procurement. The first four commands are all you need.

01 / FILTER

Pick your card

Filter the marketplace by RTX 4080, country, GPU count, reliability score, network speed.

02 / RENT

Click rent

Choose a Docker image — PyTorch, vLLM, ComfyUI, Blender — or paste your own.

$ clore rent --gpu "RTX 4080"
03 / CONNECT

SSH or Jupyter

You get a public endpoint, an SSH key, and Jupyter on port 8888 in under 90 s.

04 / STOP

Stop anytime

Per-minute billing rounds to the second. Stop the instance and the meter stops with it.

faq

Questions hosts and renters ask.

When should I pick a 4080 over a 4090?

Pick the 4080 when 16 GB is enough — SDXL batch-2, 7B fine-tuning, 13B INT8 inference. ~70% of 4090 throughput at ~55% of the rental price. Step up to 4090 for 24 GB and 70B INT4 work.

What can I actually run on a consumer GPU on CLORE?

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.

How fast does a rented server actually boot?

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.

Spot vs on-demand - what's the difference?

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.

Is CLORE.AI cheaper than RunPod or Vast.ai?

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.

Can I bring my own Docker image and SSH key?

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.

workload spotlight

Real numbers on the RTX 4080.

16 GB Ada — the production pick for SDXL/Flux at scale, 7B fine-tunes, and 13B INT8 inference.

vLLM serving Llama-3 8B FP16
vLLM + continuous batching
~1,400 tok/s aggregated, p50 35 ms

16 GB fits 8B FP16 plus 16-request KV cache — the cheapest card to run a real serving stack.

Read the guide →
SDXL batch-4 production
ComfyUI + xformers + fp16
~6.5 it/s @ 1024² batch 4

Batch-4 generation pipeline for client work — 16 GB clears all VAE/CLIP/UNet caches simultaneously.

Read the guide →
Llama-3 8B QLoRA fine-tune
PEFT + 4-bit + Flash Attn 2
~3,100 tokens/s, ~12 GB peak

8B fine-tunes complete in 2–3 hours of 4080 spot rental — fits 8K context with gradient checkpointing.

Read the guide →
consumer comparison

Consumer-tier comparison.

Side-by-side specs across the consumer tier. Click any row to see that GPU.

GPU
VRAM
CUDA cores
FP16 TFLOPS (tensor, dense)
Mem BW (GB/s)
Spot $/hr
SDXL 1024² it/s
Llama-3 8B tok/s
RTX 3070
8 GB GDDR6
5,888
~80
448
$0.10
~1.4
~50
RTX 3080
10 GB GDDR6X
8,704
~119
760
$0.14
~2.0
~85
RTX 3090
24 GB GDDR6X
10,496
~142
936
$0.18
~3.0
~110
RTX 4070
12 GB GDDR6X
5,888
~117
504
$0.16
~2.5
~60
RTX 4080 / this page
16 GB GDDR6X
9,728
~195
716
$0.27
~4.5
~95
RTX 4090 tier focus
24 GB GDDR6X
16,384
~165
1,008
$0.31
~7.5
~125
RTX 5080
16 GB GDDR7
10,752
~225
960
$0.28
~5.5
~115
RTX 5090
32 GB GDDR7
21,760
~419
1,792
$0.39
~10.0
~180
RTX 4070 Ti
12 GB GDDR6X
7,680
~160
504
$0.20
~3.2
~75
workload guides

Run these on your rented RTX 4080.

Step-by-step guides verified on CLORE.AI hardware. Pick a workload, copy the docker image, ship in minutes.

Image Generation
Flux.1 on CLORE.AI
Run Black Forest Labs' Flux for state-of-the-art image gen.
Image Generation
ComfyUI on CLORE.AI
Node-based pipeline for SDXL, Flux, and SD3.
Language Models
vLLM serving
High-throughput LLM serving with PagedAttention.
Training
LLM fine-tuning
LoRA / QLoRA fine-tuning workflow.
Video Generation
Stable Video Diffusion
Stability's image-to-video model.
Image Processing
ControlNet advanced
Pose, depth, edge guidance for SDXL.
Comparisons
image gen ui comparison
See all guides →
other gpus

Compare with similar cards.

RTX 3090
24 GB · from $0.18/hr
Rent →
RTX 4090
24 GB · from $0.31/hr
Rent →
RTX 5090
32 GB · from $0.39/hr
Rent →

Your training run
is 90 seconds away.

Hosts around the world are accepting workloads right now. Sign up, top up your wallet, and the next hour is yours.