Log in Rent RTX 3080
RTX 3080 · live marketplace — from $0.18 / hr

Rent an RTX 3080
by the minute.
Not the month.

Rent an RTX 3080 when 8 GB is too tight but 24 GB is overkill. 10 GB GDDR6X at 760 GB/s is the cheapest card that runs SDXL natively at 1024², Llama-3 8B FP16 inference, and 7B QLoRA fine-tunes. Spun up in under 90 seconds, billed per-minute, paid in BTC, USDT/USDC or CLORE. The graduation card from SD 1.5 hobbyism into real Stable Diffusion XL production.

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 3080" --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 3080 ×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 3080 hosts
<90s
Cold-start to ready
workloads

The budget workhorse,
still going strong.

Three years in, the 3080 is still one of the most rented cards on the network. NVLink-capable, 10 GB, and cheap — ideal for hobbyists, students, and side projects.

Cheapest native SDXL 1024 card

10 GB GDDR6X at 760 GB/s is exactly the spec where SDXL stops needing tiled VAE workarounds and starts running natively at 1024² batch-1. Spot floor sits around $0.14/hr — the budget-conscious upgrade path from 3070-class hobbyist work into real diffusion pipelines.

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 3080

Same VRAM as the 4090,
at half the price.

Older silicon, but 10 GB is 10 GB. For workloads that fit, the 3080 is the cheapest path to a real GPU. Specs from Nvidia's reference sheet.

RTX 3080 RTX 4090 A100 80GB H100 80GB
Architecture Ampere Ada Lovelace Ampere Hopper
CUDA cores 8,704 16,384 6,912 14,592
VRAM 10 GB GDDR6X 10 GB GDDR6X 80 GB HBM2e 80 GB HBM3
Memory bandwidth 760 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
≈ 0.0000013 BTC · 93.3 CLORE
  • Lowest possible rate
  • Per-minute billing
  • Can be interrupted by on-demand renter
  • Best for batch training, rendering
Browse spot 3080s
MOST RENTED

On-demand

$0.27 / hr
≈ 0.0000024 BTC · 173 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 3080.

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 3080, 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 3080"
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 a 3080 run SDXL at full 1024² resolution?

Yes — 10 GB GDDR6X is enough for SDXL at 1024² batch-1, and with tiled VAE you can push to batch-2. For batch-4 production pipelines, step up to a 3090 or 4080 with 16+ GB.

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 3080.

10 GB GDDR6X and 760 GB/s bandwidth make the 3080 the entry point for full-resolution SDXL and 7B fine-tuning.

SDXL 1024² production
Automatic1111 + xformers + fp16
~3.1 it/s @ 1024² batch 1

Tiled VAE pushes to batch 2 — the 3080 is the cheapest card that runs SDXL natively at full res.

Read the guide →
Llama-3 8B FP16 inference
llama.cpp server, batch 1, 8K context
~70 tok/s

FP16 8B weights fit with KV cache headroom; switch to INT8 for 13B with offload.

Read the guide →
XTTS v2 voice cloning
Coqui XTTS + fp16
~0.18 RTF (5.5× realtime)

Real-time voice cloning pipeline with 6-second reference samples — production-grade for podcast tooling.

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 / this page
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 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 3080.

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

Image Generation
A1111 WebUI on CLORE.AI
The classic SD WebUI with extensions and LoRA.
Image Generation
ComfyUI on CLORE.AI
Node-based pipeline for SDXL, Flux, and SD3.
Language Models
llama.cpp server
GGUF quantized inference with HTTP/OpenAI-compatible API.
Language Models
Ollama on CLORE.AI
One-command LLM inference for Llama, Mistral, Phi.
Image Processing
ControlNet advanced
Pose, depth, edge guidance for SDXL.
Audio Voice
XTTS voice cloning
Coqui XTTS for TTS and voice cloning.
Comparisons
llm serving comparison
See all guides →
other gpus

Compare with similar cards.

RTX 3070
8 GB · from $0.1/hr
Rent →
RTX 3090
24 GB · from $0.18/hr
Rent →
RTX 4080
16 GB · from $0.27/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.