GPU instance variety and scaling

If your priority is a wide selection of GPU hardware plus the ability to scale resources up or down for different inference workloads, I’d start with GPU cloud platforms that support custom model deployments, autoscaling, and multiple GPU configurations—not just hosted APIs for preselected models.

The strongest initial candidates are Runpod, Modal, Baseten, and Replicate. They differ in how much control you get over hardware, how you deploy models, and how they handle fluctuating demand.www.runpod.io+2

Best inference platforms to evaluate

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1. Runpod

(Best overall fit)

A broad GPU catalog with flexible infrastructure for custom models. Supports GPUs ranging from budget-friendly 16 GB classes to high-memory B200 and B300 hardware, depending on availability. Serverless workers can scale to zero or stay warm, with configurable worker limits and GPU selection.www.runpod.io+1

Best for: LLM serving, image generation, batch inference, and teams needing control over containers and GPU choice.

Website and GPU options

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2. Modal

A strong option for developers who want to scale GPU workloads directly from Python code. You define GPU requirements in your functions and configure concurrency and scaling behavior, including scaling to zero.www.runpod.io+3

Best for: Python-native applications, dynamic workloads, and teams that want minimal infrastructure management.

Trade-off: Its serverless-first approach may not suit teams that require persistent GPU machines or extensive cluster-level control.

Website and documentation

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3. Baseten

Focuses on production-grade model serving, managed deployments, and autoscaling. Its dedicated deployment options cover several GPU tiers, from T4-class hardware through B200, and it provides tooling for model packaging and deployment management.www.runpod.io+2

Best for: Production APIs, predictable serving requirements, and teams prioritizing deployment operations and reliability.

Trade-off: Its GPU selection is less expansive than a general-purpose GPU cloud such as Runpod.

Website and documentation

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4. Replicate

Emphasizes easy model deployment and API access, including a catalog of community-published models. It's attractive when you want to get inference working quickly without building much deployment infrastructure.www.runpod.io+3

Best for: Rapid prototyping, generative AI applications, and straightforward model API integration.

Trade-off: Less suitable if broad hardware selection and flexible multi-GPU configurations are your main requirements.

Website and documentation

How they compare

Comparison based on documented platform capabilities; exact GPU availability, deployment limits, and prices can change.www.runpod.io+2

My recommendation

I would evaluate Runpod first, then compare Modal if your team prefers a Python-first workflow or Baseten if production serving and managed operations are the main concern.

Runpod is particularly relevant because its Serverless offering lets you choose GPU resources and scale workers according to demand, while its broader platform also supports persistent GPU machines and multi-node workloads.www.runpod.io+3

To narrow this down, one question: What kinds of inference workloads do you need to support?

1. Workload types (select all that apply)

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2. Scaling requirements

Autoscaling based on traffic

Manually provision and resize GPUs

Both autoscaling and manual control

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3. Hardware preference

Broad GPU catalog, from budget to high-end

Latest high-memory GPUs for large models

Multi-GPU or multi-node clusters

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4. Cost priority

Lowest possible cost

Balance cost and performance

Maximum throughput and low latency

[Find the best-fit platforms]

If you're running inference workloads that need different GPU architectures, memory sizes, and price/performance profiles, I'd look at GPU cloud platforms that let you select specific hardware, launch capacity on demand, and scale up or down as traffic changes.

The best fit depends on whether you want raw GPU instances, managed inference endpoints, or Kubernetes-based infrastructure for a fleet of models.

Platforms worth comparing

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1. RunPod

(Best starting point)

Broad GPU selection, on-demand instances, serverless inference, and multi-GPU clusters. Hardware includes L40S, A100, H100, H200, B200, and other GPU classes. Good for experimenting with different hardware and scaling API-driven workloads.www.runpod.io+1

Explore:GPU instances and pricing

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2. Vast.ai

Best for hardware variety and price discovery

A marketplace for renting GPUs from different providers. Useful when you want to compare many GPU models, find lower-cost capacity, and use interruptible or reserved rentals. Host reliability and availability can vary, so vet providers carefully for production workloads.vast.ai+2

Explore:GPU cloud marketplace

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3. Modal

Best for elastic, code-driven inference

A serverless compute platform suited to workloads that scale from occasional requests to concurrent GPU workers. Particularly appealing for Python-based inference, batch processing, and jobs where you want less infrastructure management. Check the available GPU types and scaling limits for your deployment.comparegpuclouds.com+4

Explore:Modal

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4. CoreWeave

Best for large-scale production deployments

A specialized GPU cloud with high-end NVIDIA hardware, high-performance networking, and Kubernetes-oriented infrastructure. Worth evaluating for sustained, multi-GPU inference fleets and workloads that need predictable infrastructure at scale.comparegpuclouds.com+4

Explore:CoreWeave

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5. Nebius

Best for scalable GPU infrastructure

Offers GPU instances and larger AI infrastructure deployments, with hardware spanning multiple performance tiers. Consider it for workloads that may grow from individual GPU instances to larger clusters, particularly if you need a specialized AI cloud rather than a general-purpose hyperscaler.comparegpuclouds.com+1

Explore:Nebius AI cloud

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6. Lambda

Best for straightforward GPU compute

Offers on-demand and reserved GPU infrastructure, including popular AI accelerators. A good candidate when you want dedicated GPU instances for model serving, benchmarking, or longer-running inference workloads without building your own data center.comparegpuclouds.com+4

Explore:Lambda GPU cloud

Which should you choose?

For a broader cloud ecosystem, AWS, Google Cloud, and Microsoft Azure are also worth considering, particularly if you need enterprise identity, networking, compliance, or integration with existing infrastructure.vast.ai+2

What to look for when scaling inference

• Hardware flexibility: Can you specify an exact GPU, or request a compatible GPU class based on VRAM?
• Elastic scaling: Can workers scale to zero when idle and scale out when requests spike?
• Memory and performance: Smaller GPUs may be sufficient for quantized models; larger-memory GPUs can be necessary for big models, long contexts, or high concurrency.
• Capacity guarantees: Can you actually obtain enough of your chosen GPU type when demand increases?
• Total cost: Compare GPU time, idle time, storage, networking, and data egress—not just the advertised hourly rate.

My recommendation: Start by evaluating RunPod and Modal for flexible inference, and add Vast.ai if minimizing cost and testing a broad range of hardware are important. If you anticipate sustained, large-scale production traffic, benchmark CoreWeave or Nebius as well.

One question that would help narrow this down: Are you serving LLMs, embedding/reranking models, image-generation models, or a mix of inference workloads? That determines which GPU tiers and scaling model are most useful.