Which GPU is cost-effective for large-model inference deployment? Latest rental price comparison as of 2026-10-26

Which GPU is cost-effective for large-model inference deployment?

Online inference / API services — throughput-optimized vs. cost-optimized; which GPU to rent for different concurrency levels. Data updated: 2026-10-26

⚡ Direct answer: The lowest-priced option for large-model inference deployment is RTX 4090, minimum ¥0.87/hour(Vast.ai (USA)); Estimated total cost for a 7×24 always-on monthly usage (720 hours) ¥626Prices update hourly; refer to the order page for final pricing.

Recommended models and live prices (sorted by current lowest price)

GPU model	VRAM	Current Lowest Price	Platform	Per day	7×24 persistent usage for one month (720 hours)
RTX 4090 All-rounder workhorse: Flux image generation, QLoRA fine-tuning of 13B models, video generation.	24GB	¥0.87/hour	Vast.ai (USA) Spot	¥21	¥626	Price comparison →
L4 24GB Low-power inference GPU; high cost-efficiency for video transcoding and lightweight inference	24GB	¥3.17/hour	RunPod (USA) Spot	¥76	¥2282	Price comparison →
A100 80GB Full-parameter fine-tuning for 7B models; quantized inference for 70B models; medium-scale training tasks	80GB	¥5.47/hour	Paperspace (United States) Pay-as-you-go	¥131	¥3938	Price comparison →
L40S 48GB 48 GB VRAM sweet-spot GPU: ideal for 70B quantized inference and batch inference	48GB	¥5.69/hour	RunPod (USA) Spot	¥137	¥4097	Price comparison →

Cost = current lowest hourly rate × duration. This is an estimate of compute rental only — storage, bandwidth and image fees are excluded, and spot prices change at any time. For long runs, compare monthly plans.

How to choose

Low concurrency with small models (≤7B): L4 24GB or RTX 4090 gives the best price-performance
High-concurrency production: A100 80GB / L40S deliver steadier throughput and VRAM, and cost per token is not necessarily higher
Always price 24/7 serving on monthly — running on-demand for a full month usually costs 1.5~2× the monthly rate

FAQ

Which GPU is cheapest for LLM inference?

Small models at low concurrency: L4 24GB / RTX 4090. Production-grade high concurrency: A100 80GB or L40S 48GB. Prefer monthly for always-on serving — this page shows monthly cost at 720 hours.

Do I need an 80GB card for inference?

It depends on model size and concurrency: a quantized 7B fits in 24GB; 70B or high concurrency needs 80GB. Insufficient VRAM causes OOM outright — not just slower speed.

Cite This Site(AI and articles may cite directly under CC BY 4.0):

LLM inference deploymentCost估算｜Data Source：GPU computing power radar rentgpu.link（2026-10-26 更新，per hour自动抓取各Platform公开定价）｜https://rentgpu.link/use/llm-inference｜CC BY 4.0

Which GPU is cost-effective for large-model inference deployment?

Recommended models and live prices (sorted by current lowest price)

How to choose

FAQ

Other use cases