Rent RTX 5090
Compare live on-demand and spot rental prices across 97+ cloud providers. Flagship consumer Blackwell GPU. Massive FP32 uplift over 4090. 32GB GDDR7 enables larger models. Best consumer GPU for AI workloads.
Choosing the right billing model for RTX 5090
Provision and terminate at any time. Ideal for development, short experiments, and workloads with unpredictable duration.
Instances can be reclaimed when demand spikes. Best for fault-tolerant batch jobs, training with checkpointing, and preprocessing.
Lock in a rate for 1–3 months. Right for sustained production inference or long training runs where cost predictability matters.
Best use cases
- Consumer AI inference
- Fine-tuning up to 30B
- High-throughput serving
RTX 5090 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 5090 Rental Guide
Choose an RTX 5090 when 32GB is sufficient and you want Blackwell-era consumer performance for fine-tuning, image generation, or serving compact-to-mid-size models. It can be a compelling alternative to older professional cards, but its value depends on availability and whether the workload actually benefits from its high compute and memory bandwidth.
Use spot capacity for restartable fine-tuning, batch generation, and offline inference, where an interruption can be recovered from a checkpoint or queue. On-demand capacity is the safer option for interactive services, though consumer-GPU availability can vary more than established A100 or H100 inventory.
Compare it first with the RTX 4090 and RTX 6000 Ada: the 5090 adds memory and bandwidth over the former, while the latter offers professional features such as ECC. Its 575W draw, lack of NVLink, and lack of ECC are the decisive trade-offs before treating it as a data-center substitute.
Frequently Asked Questions
How much does it cost to rent a RTX 5090?
RTX 5090 on-demand rental prices vary by provider and region. On-demand rates typically range based on availability and provider margins — use the comparison table above to see current live rates across all providers. Spot instances are generally 40–70% cheaper than on-demand but can be interrupted. Monthly cost estimates (hourly rate × 730 hours) are shown in the table for sustained workloads.
Which cloud provider has the cheapest RTX 5090?
The cheapest RTX 5090 provider changes as providers update their pricing. The comparison table above shows live rates sorted by price, so the cheapest option is always at the top. Factors beyond headline price include region (latency to your users), availability (high/medium/low), and billing granularity (per-second vs per-hour minimums).
What can I run on a RTX 5090?
With 32GB of GDDR7, the RTX 5090 can run LLM models up to approximately 16B parameters at FP16, 32B at INT8, or 64B at INT4/GGUF quantization. Common workloads include: Consumer AI inference, Fine-tuning up to 30B, High-throughput serving. Flagship consumer Blackwell GPU. Massive FP32 uplift over 4090. 32GB GDDR7 enables larger models. Best consumer GPU for AI workloads.
Should I use on-demand or spot pricing for RTX 5090?
Spot instances save 40–70% vs on-demand but can be interrupted when the provider needs capacity back. Use spot for: batch inference jobs, training runs with checkpointing, preprocessing pipelines, and any workload that can tolerate interruption and restart. Use on-demand for: production inference serving, interactive workloads, and jobs that cannot be interrupted. Most providers bill per second, so short on-demand jobs are not penalized by hourly minimums.
How does the RTX 5090 compare to the H100 for cloud rental?
The H100 80GB delivers 1,979 TFLOPS FP16 with 3,350 GB/s HBM3 bandwidth, compared to the RTX 5090's 419.6 TFLOPS FP16 and 1792 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 5090. For workloads that fit within 32GB and don't require FP8 precision, the RTX 5090 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 5090 and why does it matter?
The RTX 5090 has 1792 GB/s of memory bandwidth. For LLM inference, memory bandwidth is often more important than raw TFLOPS — each autoregressive token generation reads the full model weight matrix from VRAM, so bandwidth directly determines tokens-per-second throughput. Higher bandwidth means faster inference for the same model at the same batch size. For batch inference (processing many requests simultaneously), compute throughput becomes more important.
Can I use the RTX 5090 for Stable Diffusion or image generation?
Yes — the RTX 5090 is well-suited for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 5090's 209.8 TFLOPS FP32 throughput determines images-per-second. The 32GB VRAM fits SDXL (requires ~6GB) and most ControlNet pipelines. For high-throughput image generation at scale, compare cost-per-image across providers using the GPU cost calculator.