Rent RTX 3090
Compare live on-demand and spot rental prices across 97+ cloud providers. Previous-gen consumer GPU. Cheapest option for 24GB VRAM workloads. Limited BF16 support.
Choosing the right billing model for RTX 3090
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
- Budget inference
- Small model fine-tuning
- Experimentation
RTX 3090 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 3090 Rental Guide
Choose an RTX 3090 when 24GB is the minimum workable capacity and a lower rental rate matters more than Ada or Blackwell efficiency. It remains practical for development, quantized LLM inference, and small fine-tunes, particularly where moving up to a professional GPU would not improve the memory fit.
Spot pricing is a natural match for this older consumer card: use it for resumable experiments, offline generation, and batch work with checkpoints. Use on-demand only when an interactive workflow needs a known-duration machine and the provider can supply stable capacity.
Compare it closely with the RTX 4090, which delivers much more compute, and with an A10G or L40S when ECC or data-center operation is required. The 3090’s 24GB is valuable, but no ECC, limited BF16 support, and no NVLink are material constraints rather than minor specification differences.
Frequently Asked Questions
How much does it cost to rent a RTX 3090?
RTX 3090 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 3090?
The cheapest RTX 3090 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 3090?
With 24GB of GDDR6X, the RTX 3090 can run LLM models up to approximately 12B parameters at FP16, 24B at INT8, or 48B at INT4/GGUF quantization. Common workloads include: Budget inference, Small model fine-tuning, Experimentation. Previous-gen consumer GPU. Cheapest option for 24GB VRAM workloads. Limited BF16 support.
Should I use on-demand or spot pricing for RTX 3090?
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 3090 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 3090's 71 TFLOPS FP16 and 936 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 3090. For workloads that fit within 24GB and don't require FP8 precision, the RTX 3090 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 3090 and why does it matter?
The RTX 3090 has 936 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 3090 for Stable Diffusion or image generation?
Yes — the RTX 3090 is capable for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 3090's 35.6 TFLOPS FP32 throughput determines images-per-second. The 24GB 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.