NVIDIA GeForce RTX 4090 Cloud Pricing & Rental Rates
The RTX 4090 delivers 82.6 TFLOPS FP16 at the lowest spot pricing. Ideal for QLoRA fine-tuning, Stable Diffusion, and development workloads where PCIe bandwidth is sufficient.
Target Workload: Budget fine-tuning, QLoRA, Stable Diffusion, & local dev
Live Cloud Pricing for NVIDIA GeForce RTX 4090
Spot and reserved rates refreshed from provider APIs. Click a provider to deploy.
| Provider | GPU & VRAM | Interconnect | Spot Rate | On-Demand | Monthly | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Community | PCIe 4.0 (64 GB/s) | $0.34 / hr | $0.85 / hr | $208 / mo | Instant | |||
Bare Metal | PCIe 4.0 (64 GB/s) | $0.69 / hr | $1.73 / hr | $422 / mo | Instant | |||
Cloud | PCIe 4.0 (64 GB/s) | $0.74 / hr | $1.85 / hr | $453 / mo | Instant | |||
Dedicated | PCIe 4.0 (64 GB/s) | $0.89 / hr | $2.23 / hr | $545 / mo | Instant |
Models That Fit NVIDIA GeForce RTX 4090 (24GB GDDR6X)
Deterministic VRAM calculation (FP8) via entity graph. Only editorial and enriched models shown.
| MODEL | PARAMS | CONTEXT | FP8 VRAM | FIT? | CALCULATOR |
|---|---|---|---|---|---|
| Llama 3.1 8B Instruct | 8.03B | 128K | 10.8 GB | ✓ fits | Pre-filled → |
| Llama 3.2 3B Instruct | 3.2B | 128K | 4.3 GB | ✓ fits | Pre-filled → |
| Llama 3.2 1B Instruct | 1B | 128K | 1.3 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 14B | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 7B | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 14B Instruct | 14.7B | 128K | 19.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 7B Instruct | 7.61B | 128K | 10.2 GB | ✓ fits | Pre-filled → |
| Mistral NeMo 12B | 12B | 128K | 16.1 GB | ✓ fits | Pre-filled → |
| Gemma 2 9B | 9.24B | 8K | 12.2 GB | ✓ fits | Pre-filled → |
| Gemma 3 12B | 12B | 128K | 16.1 GB | ✓ fits | Pre-filled → |
| FLUX.1 [schnell] | 12B | 128K | 16.1 GB | ✓ fits | Pre-filled → |
| MiMo V2.5 (Free) | 7B | 128K | 9.4 GB | ✓ fits | Pre-filled → |
Inference & Serving Capacity
Practical model feasibility, max batch sizes, and KV-cache retention limits for NVIDIA GeForce RTX 4090.
Llama 3.3 70B
OOM24 GB far too small, QLoRA requires CPU offload
DeepSeek 671B
OOMImpossible on single GPU
Qwen 2.5 32B
OOM13B practical max, 30B requires aggressive quant
vLLM Throughput (FP8)
~28 tok/s (vLLM, Llama 8B INT4, batch=1)
Estimated tokens/second, single GPU, Llama-class model
Max Context Window (Llama 70B)
8k tokens (INT4 QLoRA) — 70B requires multi-GPU or offloading
Maximum context length before KV-cache eviction
Hardware Bottleneck Analysis
Whether NVIDIA GeForce RTX 4090 is compute-bound (TFLOPS) or memory-bandwidth bound (GB/s) across workloads.
Bottleneck Classification
Extremely memory-bandwidth bound — 1 TB/s vs 82.6 TFLOPS FP16
Recommended Quantization
GGUF / AWQ / GPTQ — INT4 mandatory for 13B+
Best Cluster Topology
PCIe Single Node — 2 GPU max for training
Deep Analysis
RTX 4090 PCIe Gen4 Lane Saturation & QLoRA Analysis: The RTX 4090 connects via PCIe 4.0 x16 (64 GB/s bidirectional) — a critical bottleneck for multi-GPU workloads. Unlike SXM GPUs with NVLink, the 4090 lacks P2P NVLink: 2x RTX 4090 training requires PCIe round-trips through the CPU, yielding only 1.6x speedup (not 2x). For QLoRA fine-tuning, this is acceptable: the 24 GB GDDR6X fits Llama 70B INT4 (~14 GB) with 10 GB remaining for adapter weights and optimizer states in CPU RAM. The 1.0 TB/s memory bandwidth supports ~28 tok/s at INT4 — sufficient for development iteration. Host stability risks: consumer-grade GPUs on cloud spot markets may have varying thermal conditions, driver versions, and PCIe slot configurations. We recommend validating GPU health via nvidia-smi before deploying production workloads. The sweet spot: QLoRA fine-tuning of 13B-30B models where the 24 GB VRAM is adequate and the $0.34-$0.69/hr spot pricing beats datacenter GPUs by 5-10x.
Architecture & Die Breakdown
Architecture
Ada Lovelace AD102 — 5nm TSMC
TDP
450W
Memory Subsystem
24GB GDDR6X at 1.0 TB/s bandwidth. GDDR6/X provides cost-effective bandwidth for workloads that don't require HBM-level throughput.
Interconnect
PCIe 4.0 (64 GB/s). Standard PCIe bus. Suitable for single-GPU workloads or multi-GPU training with gradient accumulation.
Precision Performance
| Precision | TFLOPS | Use Case |
|---|---|---|
| FP8 | 165 | Training & inference with mixed-precision |
| FP16 | 82.6 | Full-precision training, fine-tuning, evaluation |
Break-Even ROI Calculator
Monthly hours where reserved pricing beats spot for NVIDIA GeForce RTX 4090. Above the break-even point, reserve commits save money.
Spheron
RunPod
Lambda Labs
Break-even at 120 hours/month: if you run NVIDIA GeForce RTX 4090 more than 120 hours per month, reserved pricing on all three providers saves money. At 720 hours/month (24/7), reserved saves $92+/mo vs on-demand.
Related GPUs
Frequently Asked Questions
How much does it cost to rent an NVIDIA GeForce RTX 4090 per hour?▾
What is the monthly reserved pricing for NVIDIA GeForce RTX 4090?▾
Can an NVIDIA GeForce RTX 4090 run 70B parameter LLMs?▾
Is spot pricing reliable for distributed NVIDIA GeForce RTX 4090 training?▾
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