GPU providers

Veri runs training jobs on four GPU providers. You pick one at job submit time with the provider field. Each provider offers different GPU types at different price points — AWS is the default, Hot Aisle is the AMD beachhead, Vast.ai is the NVIDIA marketplace spillover, and DigitalOcean offers single-GPU H100 droplets with per-second billing.

Providers at a glance

Provider	GPUs offered	Architecture	Billing	Status
`aws` (default)	L4, A10G, A100 80GB, H100 80GB	NVIDIA (Ampere/Hopper)	Per-second, on-demand or spot	Available
`hotaisle`	MI300X 192GB	AMD (CDNA3 / ROCm)	Per-hour, on-demand	Available
`vast`	A100 80GB, H100 80GB	NVIDIA (marketplace)	Per-second, marketplace pricing	Available
`digitalocean`	H100 80GB	NVIDIA (Hopper)	Per-second, on-demand	Available

All providers except AWS are gated behind feature flags that may be off in your environment. If a provider is disabled, submit returns a “coming soon” 400. Run veri gpu list to see which providers are live.

Choosing a provider

Pass provider when creating a training job:

from veri_sdk import Client
client = Client()

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    output_name="qwen-0.5b-amd",
    method="grpo",
    gpu_type="MI300X-192GB",
    gpu_count=1,
    provider="hotaisle",
    hyperparameters={
        "learning_rate": 1e-6,
        "max_steps": 50,
    },
)

From the CLI:

veri train --provider hotaisle --gpu-type MI300X-192GB --gpu-count 1

Or in a TOML config:

kind = "train"
provider = "hotaisle"
gpu_type = "MI300X-192GB"
gpu_count = 1

Omit provider to use the default (AWS).

Checking live availability

Capacity is per-provider and per-region. Check what’s available right now before submitting:

veri gpu list                    # all providers, all regions
veri gpu list --provider hotaisle  # Hot Aisle only
veri gpu list --provider vast     # Vast.ai only
veri gpu list --provider digitalocean  # DigitalOcean only
veri gpu list --gpu-type MI300X-192GB  # filter by GPU type

The available column shows live capacity: a real-time count of GPUs that can be launched immediately. 0 means the shape is offered but currently out of stock — retry later.

DigitalOcean rows show one AVAILABLE entry per region with current capacity, or a single UNAVAILABLE row when a shape has no capacity anywhere. This signal comes from DigitalOcean’s size catalog, which can lag real launch acceptance in both directions — a submit may still succeed after the launcher retries across regions, or miss capacity the catalog listed moments earlier.

AWS (default)

AWS is the default provider and the most reliable for NVIDIA training. It offers four GPU types:

GPU type	VRAM	Instance	GPUs per node
`L4-24GB`	24 GB	g6.xlarge	1
`A10G-24GB`	24 GB	g5.xlarge	1
`A100-80GB`	80 GB	p4de.24xlarge	8
`H100-80GB`	80 GB	p5.48xlarge	8

L4 and A10G are single-GPU instances — you rent one card. A100 and H100 are whole 8-GPU nodes: you get (and pay for) all 8 cards regardless of gpu_count, which controls how many are used for training, not how many are rented.

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    gpu_type="L4-24GB",
    gpu_count=1,
    # provider defaults to "aws"
)

Hot Aisle (AMD MI300X)

Hot Aisle is a neocloud offering single- and multi-GPU AMD MI300X VMs. The MI300X has 192 GB of HBM3 memory — more than any NVIDIA single-GPU offering — making it well-suited for large-model finetuning on a single card.

GPU type	VRAM	Architecture	GPUs available
`MI300X-192GB`	192 GB	AMD CDNA3 (ROCm 6.4)	1, 2, or 4

Hot Aisle VMs run ROCm, not CUDA. The training stack (torch, transformers, TRL) is installed from the ROCm wheel index at boot. Unsloth, bitsandbytes, xformers, and vLLM are CUDA-only and are not available on this provider. Vanilla TRL training (GRPO, SFT, DPO) is fully supported.

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    output_name="qwen-amd-grpo",
    gpu_type="MI300X-192GB",
    gpu_count=1,
    provider="hotaisle",
    hyperparameters={
        "learning_rate": 1e-6,
        "rollouts_per_prompt": 4,
        "max_steps": 50,
    },
)

Cold-start behavior

Hot Aisle VMs boot from a stock image and install the ROCm training stack at boot via cloud-init. The first boot includes:

ROCm driver verification (rocm-smi)
PyTorch ROCm wheel installation (torch 2.9.1 from the ROCm 6.4 index)
Training stack install (transformers, accelerate, peft, TRL)
GPU sanity gate (SA5_ROCM_OK — torch must see the AMD GPU)

Cold-start time depends on Hot Aisle’s VM provisioning and network throughput. If the cloud-init path is slow, a prebuilt ROCm worker container may be used in the future to reduce boot time.

Supported training methods

All managed training methods work on Hot Aisle: grpo, sft_text, dpo, and sft_video_gen. The grpo_agentic method is unavailable on all providers (the agentic engine is being rebuilt).

use_unsloth and load_in_4bit (QLoRA) are not supported on Hot Aisle — both depend on CUDA-only libraries. Set them to false (the default) when using provider="hotaisle".

Vast.ai (NVIDIA marketplace)

Vast.ai is a GPU marketplace that rents per-host NVIDIA chunks (1-8 GPUs within a single machine). It serves as a capacity spillover when AWS is out of A100 or H100 stock.

GPU type	VRAM	Architecture	GPUs available
`A100-80GB`	80 GB	NVIDIA Ampere	1-8 (per-host)
`H100-80GB`	80 GB	NVIDIA Hopper	1-8 (per-host)

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    gpu_type="H100-80GB",
    gpu_count=1,
    provider="vast",
)

Vast.ai rents individual GPU cards on shared hosts, so gpu_count=1 rents exactly one GPU (unlike AWS where A100/H100 are whole 8-GPU nodes). Availability and pricing vary by host and fluctuate in real time.

DigitalOcean (H100)

DigitalOcean GPU Droplets are on-demand single- and 8-GPU VMs with per-second billing and fast (about one minute) provisioning.

GPU type	VRAM	Architecture	GPUs available
`H100-80GB`	80 GB	NVIDIA Hopper	1 or 8

DigitalOcean sells fixed 1x and 8x droplet shapes only — a gpu_count of 2 or 4 is rejected at submit.

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    method="sft_text",
    output_name="qwen-do-sft",
    gpu_type="H100-80GB",
    gpu_count=1,
    provider="digitalocean",
)

DigitalOcean also lists AMD MI300X droplets, but they are not yet enabled on Veri — a provider="digitalocean" submit with gpu_type="MI300X-192GB" returns a 400 listing the offered shapes. For AMD MI300X training today, use provider="hotaisle".

DigitalOcean GPU capacity fluctuates minute-to-minute. A submit that hits CAPACITY_UNAVAILABLE after retrying across regions is not billed — check veri gpu list --provider digitalocean and retry.

Pricing

Each provider has its own rate card. Check current pricing with:

veri gpu list --format json   # includes usd_per_hour per GPU type

Rates are per-GPU-hour. You are billed from when the GPU is provisioned (provisioning state) until the job terminates and the instance is reaped. A job that never secures a GPU (e.g. CAPACITY_UNAVAILABLE) is not billed.

Where to go next

Managed training

How the training loop works across all providers.

GRPO algorithm

The default RL method — works on all providers.

Check live availability

veri gpu list shows real-time capacity per provider.

Regions

Launch in a specific AWS region for lower latency or capacity.

Get started

Training

Deployments

Evaluations

CLI

Volumes

Providers at a glance

Choosing a provider

Checking live availability

AWS (default)

Hot Aisle (AMD MI300X)

Cold-start behavior

Supported training methods

Vast.ai (NVIDIA marketplace)

DigitalOcean (H100)

Pricing

Where to go next

Managed training

GRPO algorithm

Check live availability

Regions

​Providers at a glance

​Choosing a provider

​Checking live availability

​AWS (default)

​Hot Aisle (AMD MI300X)

​Cold-start behavior

​Supported training methods

​Vast.ai (NVIDIA marketplace)

​DigitalOcean (H100)

​Pricing

​Where to go next

Managed training

GRPO algorithm

Check live availability

Regions

Providers at a glance

Choosing a provider

Checking live availability

AWS (default)

Hot Aisle (AMD MI300X)

Cold-start behavior

Supported training methods

Vast.ai (NVIDIA marketplace)

DigitalOcean (H100)

Pricing

Where to go next