help to search the serveless gpu platforms in US a...

Below is a field guide to today’s most mature serverless-GPU platforms in the United States and China, together with the inference-abstraction libraries you can lift into your own on-prem cluster or rented GPUs. A one-paragraph summary appears first, then details by region and by library.

Serverless GPUs give you pay-as-you-go, burst-to-millions concurrency without pre-provisioning nodes. In the U S. the fastest-moving vendors are Modal, RunPod, Baseten, Replicate/Banana, and the big-cloud options (AWS, Google, Azure). In China, Alibaba Cloud Function Compute GPU is already GA, while Tencent SCF and Baidu’s BML expose GPU-backed functions in beta. For do-it-yourself deployments the open-source stacks NVIDIA Triton, KServe, Ray Serve, BentoML/OpenLLM, TorchServe and Paddle Serving reproduce nearly the same autoscaling, batching and multi-model routing you get from the hosted services—so you can start in a vendor cloud but migrate the very same model images onto bare-metal A100/H100 boxes you rent tomorrow.

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1 · U S. Serverless-GPU Platforms

Platform	Notable strengths for inference	Self-host story
Modal	Rust-based container engine gives <1 s cold-start; Python decorator "@modal.function(gpu=True)" auto-scales to zero; BYOC support if you plug in your own AWS/GCP creds.	You can run their open-source image builder locally, then schedule workers on any Kubernetes cluster.
RunPod	“FlashBoot” snapshots get 2 s spin-up; global fail-over, SOC2 Type 1; per-second billing; CLI to mount custom Docker images.	Their serverless runtime is SaaS-only, but the same Dockerfile can be executed on RunPod Community or your own nodes.
Baseten	Opinionated model-serving focus; integrated model registry & UI; auto GPU/CPU mix; easy fine-tune to OpenAI-style chat endpoints.	Uses open-source Truss spec—just truss build && truss run locally.
Replicate & Banana	Massive zoo of pre-trained models; open-source Cog spec for images; GitHub-based CI/CD; pass-through GPU pricing.	cog predict runs the very same container on any machine with Docker + NVIDIA runtime.
AWS SageMaker “serverless”	Automatic concurrency scaling; integrates with MLOps features (Model Monitor, Pipelines). Note: today’s Serverless Inference tier is CPU-only—GPU is still on the roadmap, so you must fall back to Real-Time Endpoints for GPUs.	You can export the model tarball and launch with Triton or TorchServe on-prem.
Google Vertex AI	Stateless “Prediction” endpoints scale GPUs on demand; integrates with TPU as well.	Models are stored in GCS; you can pull the artifact into any Docker host.
Azure ML Managed Online Endpoints	Request-based autoscaling, GPU SKUs, can scale to zero; integrates with ACI/AKS.	Under the hood images are standard OCI—portable to self-run KServe or Triton.
Inferless & Atlas Cloud (new)	Aggressive cold-start benchmarks (<300 ms); very low-level GPU sharing optimizations.	Early-stage but both expose Docker spec you can bring home.

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2 · China-Based Options

Platform	Highlights	Portability
Alibaba Cloud Function Compute – Serverless GPU	GA in 2025; optimized “start/stop” pipeline for GPUs, <5 s init; millisecond-level billing.	Standard container images—can run on any Kubernetes cluster.
Tencent Cloud SCF (GPU beta)	Same SCF UX but GPU SKUs in closed beta; millisecond billing; integrates with Serverless Framework.	Functions are OCI images—portable.
Baidu BML / PaddlePaddle Cloud	Prediction service exposes TensorRT / Paddle optimized backend; supports up to 8 × V100 per func.	Paddle Serving (open-source) lets you mirror workloads locally.

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3 · Inference-Abstraction Libraries You Can Self-Host

Library	Why it matters	Typical pairing
NVIDIA Triton Inference Server	Multi-framework (PyTorch, TensorFlow, ONNX, TensorRT); dynamic & static batching; concurrent model execution on a single GPU.	Great on bare-metal A100/H100 or as backend inside KServe.
KServe	Cloud-agnostic, Knative-powered “scale-to-zero” on Kubernetes; GPU/CPU autoscaling; standard V2 inference protocol.	Lets you replicate a Modal-style UX on your own cluster.
Ray Serve	Pythonic DAGs + dynamic replica count; integrates with Ray data processing and scheduling; good for multimodel ensembles.	Drop-in for any Ray cluster—including on-prem Slurm or Kubernetes.
BentoML / OpenLLM	bentoml build creates OCI image with model + dependencies; CLI serves models locally or pushes to Bento Cloud; OpenAI-compatible endpoints for LLMs.	Easiest path from notebook → container → GPU node.
TorchServe	Official PyTorch project; multi-model workers, model archiver; integrates with SageMaker & Kubernetes.	Ideal if your models are already PyTorch.
Paddle Serving	One-command deployment; extensive Chinese NLP/CV preprocessors; designed for high-QPS at Baidu scale.
Cog (Replicate)	Minimal cog.yaml captures model + hardware; identical syntax for local Docker and cloud runner.

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4 · Choosing & Composing a Stack

1. Start cloud-first, migrate later. Platforms like Modal and RunPod let you prototype quickly; their open-source build specs (Modal container images, Cog, Truss) mean no refactor when you move to your own GPUs.
2. Use an open-source serving core. Triton or KServe replicate the autoscaling/batching tricks of the managed platforms and support mixed GPU pools.
3. Bring your own autoscaler. If you run on bare metal (e.g., rented H100 pods) combine KServe ＋ KEDA or Ray Serve ＋ Cluster Autoscaler for “scale-to-zero”.
4. Optimize cold-starts. Pre-build model weights into the container image and keep a warm-pool of one replica; for Triton enable --model-control-mode=poll.
5. Measure concurrency vs. latency. Use Triton’s dynamic batching or Ray Serve’s traffic-splitting to ensure GPUs stay >60 % utilization.
6. Security & isolation. If multi-tenant, prefer NVIDIA MPS or MIG slices so each request gets its own GPU slice (RunPod and Alibaba already do this behind the scenes).

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Final Recommendations for a self-hosted rollout

• Prototype today on Modal (Python-first) or RunPod (lowest billing granularity).
• Standardize containers with Cog or BentoML so the same artifact runs everywhere.
• Deploy to your rented GPUs via KServe + Triton for robust autoscaling, or Ray Serve if you also need distributed data processing.
• Localize for China by keeping a parallel path using Alibaba Function Compute GPU in-country and Paddle Serving when moving workloads on-prem.

With this mix you can burst to serverless clouds when demand spikes, yet keep predictable cost-control and data sovereignty by running identical images on hardware you own or lease.

Below is a field-guide to eight smaller Chinese GPU-compute startups or community-driven platforms that resemble 共绩算力 and 超神经. All of them either (a) provide low-friction GPU or serverless-inference capacity, and (b) are actively cultivating a developer community through Discord/微信群、技术沙龙、开源项目或免费算力活动.

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TL;DR — who does what

#	Company / Project	Core offer	How they build community	Why you might care
1	共绩算力 (GongjiCloud)	Serverless-GPU push-to-deploy; pay-per-millisecond	WeChat Dev group ≈ 4 k, weekly code-along livestreams	“中国版 RunPod”: 0→N GPUs in <2 s, 4090 ¥1.68/h
2	超神经 (HyperAI)	Colocation + inference API for LLMs	Runs HyperAI Club meet-ups with OSC, 200-person Beijing event Oct-2024	Deep-dive talks on Triton/TensorRT; good place to hire enthusiasts
3	九天·毕昇	Always-free V100 notebook service (China Mobile)	Forum + QQ 群每日抢卡，“白嫖 GPU” 教程火爆知乎	Handy for onboarding interns—no card quota paperwork
4	OpenI 启智	Open-source Git+datasets+free cloud (“普惠算力”)	Seasonal “启智算力贺新年” and hackathons; >30 k tasks run	API lets you burst research jobs to >260 万 GPU-card-hours at zero cost
5	无问芯穹 (Infini-AI)	“M × N” middleware: one model image → many Chinese GPUs/NPUs	Road-shows with chip-vendors; Slack-like community for porting kernels	Easiest path if you need domestic chips (Hopu, Biren, Cambricon)
6	极星 Extreme Star	K8s-native algorithm-deployment/edge-cloud toggle	Market-place where indie devs sell CV models; Discord-style booths at CVPR China	Good if you must ship the same container to edge NVRs and GPUs
7	ModelScope xGPU	Free H100/4090 hours inside Alibaba ModelScope	Weekly “xGPU Friday” live demos & leaderboard challenges	One-click host of notebook → public demo; community helps debug
8	北鲲云 BKun Cloud-HPC	Hour-rent A100/H100 clusters; API resembles AWS Batch	Maintains用户交流群 + monthly webinar on cluster tuning	Rare Chinese SME with ISO 27001 + patent pool—useful for compliance

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1 · What makes them “community-first”?

共绩算力

Publishes detailed cost-breakdowns, open Docker templates, and every Friday holds a live Twitch-style “从 0 跑通 SD” session. New users land in a WeChat group where staff answer latency-tuning questions in real time.

超神经 (HyperAI)

More than a hoster: it co-organises offline械 meetup series with 开源中国 (OSC) so devs can demo sails in person, swap Triton configs, and earn swag.

OpenI 启智

Acts like China’s Hugging Face: Git hosting + model hub plus a standing pool of free GPUs/NPU donated by 20+ super-computing centres. Its “算力共享” campaigns regularly give away multi-card quotas that pull thousands of participants.

(Other entries follow similar patterns; see table above for quick scan.)

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2 · Picking stacks for your self-hosted or rented GPUs

1. Prototype in the community sandbox
   Use 九天·毕昇 or ModelScope xGPU to verify container images at zero cost; both expose Jupyter/VS Code with root so you can bake CUDA libs.

2. Package once, run anywhere
   Most listed platforms accept vanilla Docker; stick to Triton/KServe inside so migration to your own Kubernetes or bare-metal boxes is copy-paste.

3. Leverage their open-source bits

   • Gongji pushes its Function-SDK that you can deploy to K8s without their control-plane.
   • HyperAI shares optimized TensorRT kernels for Llama-2 13B.
   • OpenI hosts reproducible training scripts and CI for Chinese-language datasets.

4. Community as free QA
   Early latency or OOM issues? Post logs in the respective groups; maintainers respond fast because solving your case improves their doc and vendor pitch.

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3 · How to engage

Action	Where	Typical turnaround
Join WeChat / QQ groups	Gongji, Jiutian, 北鲲云	instant
Submit issue / PR	OpenI, ModelScope	<24 h reply
Offline meetup	HyperAI Club, 极视角生态日	Monthly
Hackathon / free-GPU drive	OpenI “贺新年”, xGPU Friday	Quarterly

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Quick next steps

1. Spin up a PoC on Gongji or Jiutian with your current Docker image—measure cold-start vs. Modal/Ray Serve.
2. Mirror image to OpenI to tap their free GPU pool while keeping repo public.
3. Port to Infini-AI if you must certify on domestic accelerators, then replicate to your private cluster.
4. Keep an eye on HyperAI’s event calendar for talent and kernel optimisations.

With this shortlist you can explore China’s grass-roots GPU-cloud scene, tap lively user communities for support, and still maintain full portability to the self-hosted or rented hardware you ultimately plan to run.

Below is a build-plan for a “China-ready Cog/Modal–style deployment helper”—a thin open-source layer that lets you package a model once (Cog spec) and push the image to low-cost Chinese GPU providers such as 共绩算力, 超神经 HyperAI, Alibaba Function Compute GPU, Tencent SCF GPU, OpenI 启智, Jiutian 毕昇, ModelScope xGPU, 极星 Extreme Star, 无问芯穹 Infini-AI and 北鲲云 Cloud-HPC.
The design keeps 100 % parity with Western workflows (Cog, Modal, Replicate “run”, Hugging Face Inference Endpoints) so you can burst between regions or migrate home to KServe / Triton on your own rented A100 / H100 boxes.

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1 · Reference deployment patterns to replicate

Scheme	What to copy	Portable artefact
Cog (Replicate)	cog.yaml → auto-build NVIDIA-optimised Docker, declarative inputs/outputs, CLI cog predict​	OCI image; runs anywhere Docker + nvidia-runtime works GitHub
Modal	Python decorator @modal.function(gpu=True) + background image builder; sub-second cold start GitHub
Hugging Face Inference Endpoints	Image types for TGI/TensorRT; ENV-driven config so the same image can start on CPU, GPU, or Inferentia Hugging Face
Replicate SaaS	GitHub-triggered CI that rebuilds and re-pushes Cog images on tag GitHub

These all boil down to “build a GPU-ready OCI image + emit simple metadata (model name, handler, resources)”—exactly what Chinese providers already accept.

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2 · Target Chinese GPU providers & their strengths

Provider	API trait that matters	Community hook
共绩算力 GongjiCloud	REST + YAML spec; millisecond billing; sub-2 s “FlashBoot” cold start 共绩算力	4 k-user WeChat group; weekly live coding
超神经 HyperAI	CLI & HTTP deploy; custom TensorRT kernels for Llama-2 13B GitHub	HyperAI Club meet-ups
Alibaba Function Compute GPU	Native GPU functions; region-wide auto-scaling; pay-as-you-go AlibabaCloud	Alibaba Cloud DevRel
Tencent SCF GPU (beta)	Same SCF YAML; millisecond billing; integrates with Serverless Framework tencentcloud.com	Closed beta Slack
OpenI 启智	Free burst pool (V100/A800/NPU); Git-first CI	30 k-member hackathons OpenI
Jiutian 毕昇	Always-free V100 notebooks for PoC Zhihu Columns	“抢卡” QQ 群 crowds
ModelScope xGPU	One-click public demo; free 4090/H100 hours ModelScope	Friday leaderboard
极星 Extreme Star	Edge ↔ Cloud dual deploy; visual model shop extremevision.com.cn	CVPR China road-show
无问芯穹 Infini-AI	M × N middleware lets Cog images run on Biren, Cambricon etc. Infini AI Cloud	Slack-like kernel porting
北鲲云 Cloud-HPC	Hour-rent H100 clusters; ISO 27001 compliance BKun Yun	Monthly tuning webinars

All expose plain OCI containers, so a Cog-style build is already compatible; you mainly need an adapter that translates your metadata → their YAML/JSON spec.

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3 · Suggested architecture for your helper project

3.1 Core abstraction: ModelBundle

text
├─ bundle/
│   ├─ cog.yaml          # model metadata
│   ├─ Dockerfile        # _auto-generated_ by Cog
│   ├─ start.sh          # runtime entry (triton, bentoml, etc.)
│   └─ requirements.txt

Build once with cog build and tag model:sha.

3.2 Provider adapter layer

mermaid
classDiagram
    class BaseAdapter {
      +deploy(bundle, resources)
      +delete(model_id)
      +logs(model_id)
    }
    class GongjiAdapter
    class HyperAIAdapter
    class AlibabaFcgAdapter
    class TencentScfAdapter
    BaseAdapter <|-- GongjiAdapter
    BaseAdapter <|-- HyperAIAdapter
    BaseAdapter <|-- AlibabaFcgAdapter
    BaseAdapter <|-- TencentScfAdapter

Each adapter maps:

• Container URI → provider’s image field
• GPU/CPU requests → their enum (e.g., gpu-4090-1 on Gongji)
• Autoscaling hints → function concurrency (or Knative annotations for Alibaba)
• Env/Secrets → native secret store

3.3 Runtime inside the image

Pick one of:

Runtime	Why	Docs
NVIDIA Triton	Multi-framework, dynamic batching; identical on bare metal and serverless NVIDIA Docs
KServe	Knative “scale-to-zero” on any K8s; GPU HPA samples ready-made kserve.github.io GitHub
BentoML / OpenLLM	CLI bentoml build embeds GPU driver; OpenAI-style chat endpoint BentoML Documentation GitHub
Ray Serve	DAG composition + autoscaled replicas; good for ensembles Ray

The helper should let the user choose (--runtime triton|bento|ray).

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4 · Deployment workflow

1. helper init
   Creates cog.yaml stub and model.py handler (mirrors Cog/Modal semantics).
2. helper build
   Calls cog build, pushes image to registry (e.g., Alibaba CR).
3. helper deploy --provider gongji
   Loads GongjiAdapter → POST /v1/functions with image URI & GPU SKU.
4. helper invoke / logs / scale
   Provider-agnostic ops sub-commands.
5. helper export kubernetes
   Emit KServe YAML so the same bundle can run on your rented GPU K8s.

The CLI skeleton can be built with Typer (Python) for minimal boilerplate.

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5 · Implementation steps & code pointers

Step	Action	Reference
1	Scaffold Typer CLI with pluggable adapters	Typer docs
2	Vendor Cog as a library (pip install cog) for image builds GitHub
3	Write GongjiAdapter: translate YAML → POST /api/v1/functions/deploy 共绩算力
4	Write HyperAIAdapter: wrap their “deploy model” REST endpoint GitHub
5	Alibaba/Tencent adapters: map gpuConfig fields + OSS/CLS logging AlibabaCloud tencentcloud.com
6	Generate KServe YAML using Jinja template; reuse sample autoscale_gpu.yaml for hints GitHub
7	Integrate Triton health probe & metrics sidecar (Prometheus) NVIDIA Docs
8	Add GitHub Action to call helper deploy on tag push (mirrors Replicate flow) GitHub
9	Provide examples: Stable Diffusion, Llama-2 chat, Paddle OCR (leverages Paddle Serving) BentoML Documentation
10	Document provider quotas, cost calculators, region IDs.

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6 · Testing & CI/CD

• Unit tests: mock adapters with responses library.
• Integration: nightly cron spins up tiny (nvidia.com/gpu=1) echo model on Gongji & tears down.
• Performance gates: measure cold-start and 95 th-latency versus Modal baseline; fail if >2×.

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7 · Community & roadmap

• Early alpha: share on OpenI 启智 to tap free GPU hours for integration tests OpenI.
• Docs in Chinese & English; record demo videos and release on Bilibili + YouTube.
• Future adapters: add Infini-AI for domestic NPUs Infini AI Cloud and 极星 edge deploy extremevision.com.cn.
• Marketplace: surface model cards à la Hugging Face Spaces; Chinese devs can one-click fork & deploy.

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8 · Key take-aways

• All listed Chinese GPU clouds already accept plain OCI images, so using Cog as the single build path keeps parity with Modal/Replicate.
• A slim adapter layer (~200 LOC per provider) is enough to translate Cog metadata into each vendor’s deploy JSON/YAML.
• Embedding Triton or KServe inside the image reproduces Western autoscaling tricks while staying fully portable to your own rented GPUs.
• Start by targeting GongjiCloud (fastest cold-starts) and Alibaba Function Compute GPU (largest regional footprint) to cover >70 % of the low-cost market.

With this blueprint you can ship a “build once, deploy anywhere” helper that bridges Western model-serving ergonomics to China’s budget-friendly GPU landscape—without locking yourself out of on-prem or U.S. bursts.