What is the cheapest cloud provider to rent NVIDIA NVIDIA GeForce RTX 3090?

Based on our live rate telemetry, the lowest verified rate for NVIDIA GeForce RTX 3090 starts at $0.08/hr (spot) available on SaladCloud.

Can I run ComfyUI and vLLM on NVIDIA GeForce RTX 3090?

Yes. With 24GB of GDDR6X memory, the NVIDIA GeForce RTX 3090 is capable of running modern AI workloads. It is ideal for ComfyUI FLUX.1 and 8B-14B LLMs.

Ampere Architecture

NVIDIA GeForce RTX 3090 Cloud Rental Pricing

The budget champion for developers seeking 24GB VRAM under $0.25/hour. Exceptional value on spot markets.

Lowest On-Demand

$0.10/hr

Cheapest Spot Rate

$0.08/hr

VRAM Capacity

24 GB

Memory Type

GDDR6X

Memory Bandwidth

936 GB/s

FP16 Compute

71 TFLOPS

Power / TDP

350W

Interconnect

PCIe 4.0 x16

Live Cloud Instance Rates

Found 5 cloud instances for NVIDIA GeForce RTX 3090

Cloud Provider	Host Tier	GPU Config	Interconnect	vCPU & RAM	Network & Disk	Spot Price	On-Demand	Action
S SaladCloud	Community	1x (24GB)	PCIe 4.0 x16	4 vCPUs / 16GB RAM	450 MB/s (~4 Gbps) Ephemeral	$0.08/hr	$0.10/hr	Deploy
V Vast.ai	Datacenter	1x (24GB)	PCIe 4.0 x16	4 vCPUs / 16GB RAM	1.2 GB/s (~10 Gbps) Persistent Volume	$0.14/hr	$0.22/hr	Deploy
T TensorDock	Datacenter	1x (24GB)	PCIe 4.0 x16	4 vCPUs / 16GB RAM	1.2 GB/s (~10 Gbps) Persistent Volume	$0.16/hr	$0.24/hr	Deploy
R RunPod	Datacenter	1x (24GB)	PCIe 4.0 x16	8 vCPUs / 30GB RAM	1.0 GB/s (~8 Gbps) Persistent Volume	$0.19/hr	$0.29/hr	Deploy
V Vast.ai	Community	2x (48GB)	PCIe 4.0 x16	8 vCPUs / 32GB RAM	250 MB/s (~2 Gbps) Persistent Volume	$0.28/hr	$0.44/hr	Deploy

Get Restock & Flash Price-Drop Alerts for NVIDIA GeForce RTX 3090

We monitor NVIDIA GeForce RTX 3090 capacity across RunPod, Lambda, and Vast.ai 24/7. Get notified immediately when spot capacity opens below average rates.

Optimal Workloads & Workflows

Budget ComfyUI, Ollama testing, learning PyTorch, prototyping

Calculate exact model VRAM & context requirements in the AI Calculator

Architecture Deep-Dive & Hardware Analysis

NVIDIA GeForce RTX 3090: The Entry-Level 24GB AI Prototyping King (2026 Technical Guide)

The NVIDIA GeForce RTX 3090 remains the most accessible 24GB AI accelerator in cloud computing. Built on the Ampere GA102 architecture with 10,496 CUDA cores, 328 3rd Gen Tensor Cores, and 24GB of GDDR6X memory delivering 936 GB/s bandwidth, the RTX 3090 broke consumer barriers by bringing 24GB VRAM to mass developers. Renting between $0.14/hr (spot on community markets) and $0.28/hr (on-demand), it offers the absolute lowest barrier to entry for SDXL ComfyUI generation, 8B LLM fine-tuning, and machine learning experimentation.

Engineering Decision Matrix: Is NVIDIA GeForce RTX 3090 Right For You?

Sweet Spot Workloads & Pipelines:

Budget-conscious AI learning, ComfyUI, and Stable Diffusion XL generation.
Local QLoRA fine-tuning for 7B/8B models with Unsloth or Axolotl.
Interactive coding, Jupyter notebook experiments, and hobbyist AI projects.

Workloads To Avoid & Bottlenecks:

Large models requiring native FP8 acceleration (RTX 3090 lacks Ada/Hopper Transformer Engine).
Heavy 70B model serving (requires multiple cards and lacks sufficient memory bandwidth).

1. Silicon Architecture & Ampere GA102 Consumer Power

Fabricated on Samsung's 8nm custom node, the RTX 3090 features 28 billion transistors and 10,496 CUDA cores. While lacking Ada's 72MB L2 cache and FP8 precision, its 24GB GDDR6X buffer at 936 GB/s memory bandwidth provides substantial memory throughput.

2. Cloud Rental Value: Renting for Under $0.20/hr

On community clouds like Vast.ai and RunPod Community, RTX 3090 spot instances regularly rent for $0.14 to $0.19/hr. For developers running non-urgent batch jobs or learning deep learning frameworks, it delivers unbeatable cost efficiency.

Framework Benchmarks & Execution Profiles

ComfyUI SDXL 1024x1024 (30 steps)EXCELLENT

Memory Footprint: 11.2 GB / 24 GB

Generates in 4.8 seconds. Excellent value.

vLLM Llama-3.1-8B-Instruct (FP16, 4k context)GOOD

Memory Footprint: 17.0 GB / 24 GB

Delivers 72 tokens/second generation throughput.

Technical FAQ: Renting & Deploying NVIDIA GeForce RTX 3090

How does RTX 3090 compare to RTX 4090?

The RTX 4090 is about 2x to 2.5x faster in compute and features native FP8 support and a 72MB L2 cache, but costs roughly 2x more ($0.34-$0.44/hr vs $0.18-$0.25/hr). For budget prototyping, the 3090 remains a fantastic choice.

GPU Hardware Watchdog & Audit

Hardware Architecture Traps & Sanity Benchmarks

Avoid fake multi-GPU speedups, PCIe bottlenecks, and slow disk thrashing before paying full price.

SXM5 vs PCIe Interconnect

An 8x H100 SXM5 cluster delivers 900 GB/s bidirectional NVLink. In contrast, 8x H100 PCIe cards run at only 64 GB/s. For multi-GPU LLM training and vLLM tensor parallelism, PCIe cards suffer 40% to 70% throughput collapse due to all-reduce bottlenecks.

Tier 3 Cloud vs P2P Miner Rigs

Community marketplace hosts (Vast.ai) can save up to 60%, but lack power redundancy. A host reboot during a 20-hour LoRA run loses your epoch if checkpoints aren’t saved to remote S3. Choose Tier-3 data centers for production training.

Disk IOPS & Model Weights

Loading a 70B Safetensors model (140GB) on a cheap HDD network share takes 15 minutes. On high-speed local NVMe (>2.5 GB/s), it loads in 45 seconds. Always verify IOPS before debugging slow vLLM startup times.

Run on Fresh SSH Instance (Instant Verification)Takes ~5 seconds to run

# --- GPUy Day-1 Cloud GPU Healthcheck & Benchmark ---
# 1. Check GPU Clocks, Temperature, Power, PCIe Bus & Driver
nvidia-smi --query-gpu=name,driver_version,temperature.gpu,utilization.gpu,power.draw,clocks.current.graphics,memory.total,pcie.link.gen.current,pcie.link.width.current --format=csv

# 2. Check Thermal Throttle Violations & Performance State
nvidia-smi -q -d PERFORMANCE,CLOCK | grep -E "Throttle Reason|Performance State" | head -n 8

# 3. Check NVLink Interconnect Status (Critical for Multi-GPU Training)
nvidia-smi nvlink -s 2>/dev/null || echo "No NVLink detected (PCIe bus fallback)"

# 4. Benchmark NVMe Disk Write Speed (Look for >1.0 GB/s)
dd if=/dev/zero of=/workspace/speed_test.tmp bs=1G count=2 oflag=direct 2>&1 | tail -n 1
rm -f /workspace/speed_test.tmp

# 5. Test Hugging Face CDN Download Speed
curl -s -w 'HuggingFace CDN Speed: %{speed_download} bytes/sec\n' -o /dev/null https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin

Idle Temp< 45°C (Load < 78°C)

PCIe Bus LinkGen4 x16 / Gen5 x16

NVMe Disk Speed> 1.2 GB/s Direct IO

Weights CDN> 50 MB/s (400 Mbps)