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

Online inference / API services: Throughput-first or cost-first? Which GPU to rent for different concurrency levels · Data updated: Oct 31, 2026

⚡ 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 auto-update every 45 minutes; final pricing reflects the checkout page.

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	¥2.09/hour	Jarvislabs (India) Spot	¥50	¥1505	Price comparison →
L40S 48GB 48 GB VRAM sweet-spot GPU: ideal for 70B quantized inference and batch inference	48GB	¥4.54/hour	Cloudrift (United States) Pay-as-you-go	¥109	¥3269	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 →

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 Compute Radar rentgpu.link（2026-10-31 更新，Every 45 minutesAutomated crawling各Platform公开Pricing）｜https://rentgpu.link/use/llm-inference｜CC BY 4.0

Other use cases

Fine-tune a 7B model LLM inference deployment AI image generation (SDXL / Flux)Video generation 3D rendering Students / getting started Train a 70B model Multi-GPU / clusters

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