Key features
- Serverless GPU: APAC T4/A10G/A100 with per-second billing; zero idle cost
- HuggingFace: APAC direct deployment from HuggingFace model hub
- Custom models: APAC PyTorch/ONNX/TensorRT models via Python class interface
- Auto-scaling: APAC zero-to-N GPU workers based on request concurrency
- Sub-30s cold start: APAC fast worker activation for low-latency resumption
- Private models: APAC proprietary model weights stored in Inferless-managed storage
Best for
- APAC AI product teams deploying LLMs, diffusion models, or custom ML inference APIs at variable or intermittent traffic volumes — particularly APAC SaaS teams adding AI inference features to products where steady-state request volume is moderate and reserved GPU instance costs would be economically unjustifiable.
Limitations to know
- ! APAC cold start latency (sub-30s) may be too slow for interactive real-time UX requirements
- ! APAC data residency: US-primary infrastructure — review for APAC data sovereignty compliance
- ! Less mature than Baseten for APAC teams requiring TensorRT optimization and advanced serving tuning
About Inferless
Inferless is a serverless GPU inference platform providing APAC AI product teams with a managed deployment layer for converting HuggingFace, custom PyTorch, ONNX, and TensorRT models into production-grade auto-scaling inference APIs — combining per-second GPU billing, zero-infrastructure management, and sub-30-second cold starts into a deployment experience optimized for inference workloads that vary in volume. APAC teams serving LLMs, diffusion models, speech models, and custom ML inference at variable traffic patterns use Inferless as their inference API layer.
Inferless' deployment model packages APAC models through a Python class interface — teams define a `load()` method for model initialization and an `infer()` method for request handling, and Inferless manages GPU provisioning, containerization, scaling, and API exposure. APAC engineering teams familiar with Python model serving frameworks adapt existing inference code to Inferless' interface within hours, deploying models that auto-scale from zero to multiple concurrent GPU workers based on request volume.
Inferless' GPU selection covers APAC inference workload requirements — T4 (16GB) for smaller models and cost-sensitive batch workloads, A10G (24GB) for mid-size LLMs and image generation, A100 (80GB) for large language models requiring full-precision inference. APAC teams fine-tuning and deploying 7B–13B parameter LLMs for domain-specific applications (legal document processing, APAC language customer service) use Inferless' A10G workers for the VRAM headroom their models require at moderate request volumes.
Inferless' per-second GPU billing charges APAC teams only for active inference time — requests trigger GPU worker activation, compute runs during inference, and billing stops when the inference completes. APAC applications with intermittent AI inference demand (document processing triggered by uploads, AI features in low-traffic SaaS products) use Inferless to avoid paying for reserved GPU capacity that would sit idle between requests. APAC teams estimating 500–5,000 inference requests per day often find serverless GPU billing more cost-effective than reserved instance pricing.
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