Rent RTX 4080
Compare live on-demand and spot rental prices across 97+ cloud providers. Mid-tier Ada Lovelace. 16GB VRAM limits model size but strong FP32 at a lower price than 4090.
Choosing the right billing model for RTX 4080
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
- Cost-efficient serving
RTX 4080 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 4080 Rental Guide
An RTX 4080 rental makes sense for 7B-class models, image generation, and development tasks that have a confirmed 16GB memory footprint. It is usually more cost-efficient than high-end data-center hardware when capacity is not the bottleneck.
Use spot instances for interruptible fine-tuning, batch generation, and experiments that write frequent checkpoints. On-demand is preferable for interactive testing or small services that need immediate access, although consumer GPU availability can be inconsistent across providers.
Before choosing it, compare the total memory requirement with a 24GB RTX 4090 or a 48GB L40S; the 4080’s lower rental rate is irrelevant if offloading or quantization compromises the workload. Lack of ECC, NVLink, and data-center validation are the central limitations for enterprise deployment.
Frequently Asked Questions
How much does it cost to rent a RTX 4080?
RTX 4080 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 4080?
The cheapest RTX 4080 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 4080?
With 16GB of GDDR6X, the RTX 4080 can run LLM models up to approximately 8B parameters at FP16, 16B at INT8, or 32B at INT4/GGUF quantization. Common workloads include: Budget inference, Small model fine-tuning, Cost-efficient serving. Mid-tier Ada Lovelace. 16GB VRAM limits model size but strong FP32 at a lower price than 4090.
Should I use on-demand or spot pricing for RTX 4080?
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 4080 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 4080's 97.5 TFLOPS FP16 and 717 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 4080. For workloads that fit within 16GB and don't require FP8 precision, the RTX 4080 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 4080 and why does it matter?
The RTX 4080 has 717 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 4080 for Stable Diffusion or image generation?
Yes — the RTX 4080 is capable for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 4080's 48.7 TFLOPS FP32 throughput determines images-per-second. The 16GB 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.