Rent RTX 4070
Compare live on-demand and spot rental prices across 97+ cloud providers. Entry Ada Lovelace. Lowest cost per hour for Ada architecture. 12GB VRAM, low TDP.
Choosing the right billing model for RTX 4070
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
- Ultra-budget inference
- Experimentation
- Small models
RTX 4070 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 4070 Rental Guide
Renting the RTX 4070 makes sense when your workload requires 12GB of GDDR6X memory and 58.2 TFLOPS of FP16 compute. The most common use cases are Ultra-budget inference, Experimentation, Small models. Before committing to a rental, verify that your model and batch size fit within 12GB — a 70B parameter model requires approximately 140GB at FP16, which would require two RTX 4070 instances with tensor parallelism.
Spot instances for the RTX 4070 typically save 30–60% vs on-demand. Given its 12GB VRAM, it's a strong candidate for spot-priced fine-tuning and batch inference jobs where interruption recovery is straightforward. On-demand instances give you full control with no commitment — ideal for development, short experiments, and workloads with unpredictable duration. Most providers bill per second or per minute, so short jobs are not penalized by hourly minimums.
Reserved pricing (1–3 month commitments) makes sense if you have a predictable, sustained workload. For development, experimentation, or variable-volume inference, on-demand remains the most flexible choice. When comparing providers, look beyond the headline hourly rate: check region availability (latency matters for interactive inference), spot interruption frequency, and whether the provider offers per-second billing. Use the GPU cost calculator to model total cost across different billing models and utilization rates before choosing a provider.
Frequently Asked Questions
How much does it cost to rent a RTX 4070?
RTX 4070 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 4070?
The cheapest RTX 4070 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 4070?
With 12GB of GDDR6X, the RTX 4070 can run LLM models up to approximately 6B parameters at FP16, 12B at INT8, or 24B at INT4/GGUF quantization. Common workloads include: Ultra-budget inference, Experimentation, Small models. Entry Ada Lovelace. Lowest cost per hour for Ada architecture. 12GB VRAM, low TDP.
Should I use on-demand or spot pricing for RTX 4070?
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 4070 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 4070's 58.2 TFLOPS FP16 and 504 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 4070. For workloads that fit within 12GB and don't require FP8 precision, the RTX 4070 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 4070 and why does it matter?
The RTX 4070 has 504 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 4070 for Stable Diffusion or image generation?
Yes — the RTX 4070 is capable for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 4070's 29.1 TFLOPS FP32 throughput determines images-per-second. The 12GB 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.