Log in Rent RTX 4090
RTX 4090 · live marketplace — from $0.31 / hr

Rent an RTX 4090
by the minute.
Not the month.

Rent an RTX 4090 — the most-rented consumer card on the network since 2023. 24 GB GDDR6X at 1,008 GB/s, 16,384 Ada cores. Production Flux batch-4, 8B FP16 vLLM serving at 2,200 tok/s, 34B QLoRA, single-card Hunyuan video, dual-card 70B INT4. Spun up in under 90 seconds, billed per-minute, paid in BTC, USDT/USDC or CLORE. The reference consumer GPU for ComfyUI commercial work.

Per-minute billing SSH + Docker + Jupyter Spot & on-demand 34 regions
SHELL ~/projects/finetune
# install the official Python SDK + CLI $ pip install clore-ai $ clore search --gpu "RTX 4090" --max-price 2.0 312 servers · cheapest spot $0.31/hr · cheapest on-demand $0.49/hr $ clore deploy 51847 --image cloreai/ubuntu22.04-cuda12 --type on-demand --currency bitcoin order #82144 created · waiting for boot… running · ssh ready $ clore ssh 82144
GPU
RTX 4090 ×1
VRAM
24 GB
Rate
$0.49/hr
Status
Running
$0.31/hr
Starting on-demand price
24GB
GDDR6X VRAM per card
34
Regions with 4090 hosts
<90s
Cold-start to ready
workloads

The default GPU
for serious work.

The RTX 4090 has been Clore's most-rented card since launch. It handles 13B–34B LLM fine-tunes, SDXL pipelines, and 4K renders without breaking a sweat.

Most-rented consumer card on the network

Battle-tested inventory across 34 regions, 312+ live listings on a typical day, spot floor near $0.31/hr. The de facto reference for ComfyUI commercial pipelines, 34B QLoRA, and dual-card 70B INT4. Whatever you want to run, somebody has already published the recipe for it on a 4090.

Llama-3 8B SFT ~14k tok/s

Diffusion & rendering

SDXL, Flux.1 dev, ComfyUI workflows at production scale. Blender Cycles with OptiX turning out 4K frames at ~58 s/frame.

SDXL 1024² batch-4 4.2 it/s

Inference at scale

vLLM and TGI containers run Llama-3 8B FP16, or 70B INT4 across 2× 4090. Real throughput, real cost-per-token.

Llama-3 8B FP16 92 tok/s/user
why 4090

Best price-to-performance
on the marketplace.

The 4090 is the sweet spot — datacenter-class throughput at consumer pricing. Specs from Nvidia's reference sheet; pricing is the lowest live rate right now.

RTX 4090 RTX 5090 A100 80GB H100 80GB
Architecture Ada Lovelace Blackwell Ampere Hopper
CUDA cores 16,384 21,760 6,912 14,592
VRAM 24 GB GDDR6X 32 GB GDDR7 80 GB HBM2e 80 GB HBM3
Memory bandwidth 1,008 GB/s 1,792 GB/s 1,935 GB/s 3,350 GB/s
FP16 / BF16 (dense) ~165 TFLOPS ~210 TFLOPS 312 TFLOPS 756 TFLOPS
From / hr (on-demand) $0.31 $0.39 $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

$0.31 / hr
≈ 0.0000028 BTC · 207 CLORE
  • Lowest possible rate
  • Per-minute billing
  • Can be interrupted by on-demand renter
  • Best for batch training, rendering
Browse spot 4090s
MOST RENTED

On-demand

$0.49 / hr
≈ 0.0000045 BTC · 327 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 4090.

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 4090, 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 4090"
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.

Can I run 70B models on a 4090?

Yes — Llama-3 70B INT4 fits across two 4090s with tensor parallelism via vLLM or ExLlamaV2. For single-card 70B you'll want an H100 or H200. 13B and 34B fit comfortably on one 4090.

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 4090.

24 GB GDDR6X and 1 TB/s bandwidth — the canonical consumer card for Flux production, 34B QLoRA, and 70B INT4.

Flux.1 dev 1024² batch 4
ComfyUI + fp8 dev + Flash Attn 2
~4.2 it/s @ 1024² batch 4

Production-ready Flux pipeline — 4090 is the de facto reference card for ComfyUI commercial work in 2026.

Read the guide →
vLLM Llama-3 8B FP16 serving
vLLM 0.6+ + chunked prefill
~2,200 tok/s aggregated, 32 concurrent

Serves a small product/feature with one card; horizontal scale by adding cards behind a load balancer.

Read the guide →
Hunyuan-Video 5-second clip
ComfyUI + fp8 + sequence parallelism
~7 min per 5 s @ 720p

24 GB is the floor for Hunyuan; 1× 4090 generates short clips, multi-card scales linearly.

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
16 GB GDDR6X
9,728
~195
716
$0.27
~4.5
~95
RTX 4090 / this page
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 4090.

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.
Language Models
vLLM serving
High-throughput LLM serving with PagedAttention.
Training
LLM fine-tuning
LoRA / QLoRA fine-tuning workflow.
Video Generation
Hunyuan Video
Tencent's open video generation model.
Image Generation
ComfyUI on CLORE.AI
Node-based pipeline for SDXL, Flux, and SD3.
Training
Kohya SS LoRA training
The standard SDXL LoRA training pipeline.
Comparisons
llm serving comparison
See all guides →
other gpus

Compare with similar cards.

RTX 3090
24 GB · from $0.18/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.