Rent RTX 5080
Compare live on-demand and spot rental prices across 97+ cloud providers. Strong mid-range Blackwell. 16GB GDDR7 limits model size but excellent FP32 per dollar. Lower TDP than 5090.
Choosing the right billing model for RTX 5080
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 consumer inference
- Small model fine-tuning
- Cost-efficient serving
RTX 5080 — Specs & Benchmarks
Performance bars, compute tiers (FP32/FP16/BF16/FP8/INT8), memory specs, LLM model size guidance, and related GPU comparisons.
RTX 5080 Rental Guide
Rent an RTX 5080 for small-model serving, development, or image-generation work when 16GB is a proven fit. Its performance can lower latency or raise throughput versus older consumer cards, but there is little value in paying for it if the model must be aggressively offloaded or split because of the memory limit.
Spot instances are well suited to checkpointed fine-tunes, rendering queues, and batch inference, where their lower price offsets interruption risk. On-demand usage is better for short-lived interactive tests or production endpoints that need predictable access, subject to the provider’s consumer-card supply.
The key comparison is against a 24GB RTX 4090 or a 32GB RTX 5090: the 5080 may offer newer memory technology, but neither compute nor bandwidth compensates for an out-of-memory workload. No ECC and no NVLink also make it a poor fit for persistent enterprise services or pooled-memory designs.
Frequently Asked Questions
How much does it cost to rent a RTX 5080?
RTX 5080 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 5080?
The cheapest RTX 5080 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 5080?
With 16GB of GDDR7, the RTX 5080 can run LLM models up to approximately 8B parameters at FP16, 16B at INT8, or 32B at INT4/GGUF quantization. Common workloads include: Budget consumer inference, Small model fine-tuning, Cost-efficient serving. Strong mid-range Blackwell. 16GB GDDR7 limits model size but excellent FP32 per dollar. Lower TDP than 5090.
Should I use on-demand or spot pricing for RTX 5080?
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 5080 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 5080's 275.4 TFLOPS FP16 and 960 GB/s bandwidth. The H100 is significantly more expensive — typically $2.50–$5.00/hr vs lower rates for the RTX 5080. For workloads that fit within 16GB and don't require FP8 precision, the RTX 5080 often delivers better cost-per-token than the H100.
What is the memory bandwidth of the RTX 5080 and why does it matter?
The RTX 5080 has 960 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 5080 for Stable Diffusion or image generation?
Yes — the RTX 5080 is well-suited for Stable Diffusion and image generation workloads. Image generation is primarily FP32 and FP16 compute-bound, and the RTX 5080's 137.7 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.