AutoDL

AutoDL GPU pricing overview

AutoDL is a marketplace GPU cloud provider headquartered in China. AutoDL is a Chinese GPU cloud marketplace offering affordable H800, A100, and RTX GPU instances for AI researchers and developers in China. The platform provides on-demand GPU rental with competitive CNY pricing, making it one of the most accessible GPU cloud options for Chinese academic and research teams. Prices are denominated in CNY; USD equivalents are approximate. A practical choice for Chinese AI teams that need affordable GPU access for model training, fine-tuning, and inference within Chinese infrastructure. Billing is On-demand with a minimum commitment of None. Available regions include CN. GPU instances can be provisioned on-demand, making AutoDL suitable for both short-duration experiments and sustained production workloads.

AutoDL vs other GPU providers

AutoDL competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Affordable GPU pricing for Chinese market; Wide GPU catalog including H800 and A100; Popular with Chinese AI research community. Use the side-by-side comparison tool above to see AutoDL pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 AutoDL listings alongside 91+ providers in a single sortable view.

Best use cases for AutoDL

AutoDL is best suited for: Chinese AI researchers and students, Cost-sensitive GPU workloads in China, Teams needing GPU access within Chinese infrastructure. Support tiers range from Community → Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on AutoDL, covering H800 80GB, RTX PRO 6000 96GB, A800 80GB, RTX 5090. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

AutoDL billing model and cost structure

AutoDL uses On-demand pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on AutoDL

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How AutoDL pricing data is collected

Prices shown are sourced from AutoDL's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in CNY per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.