Best GPU for Machine Learning 2026: Complete Hardware & Cloud Comparison Guide

Choosing the right GPU for machine learning in 2026 determines whether your AI project succeeds or drowns in compute costs. The landscape has shifted dramatically: NVIDIA's H200 now dominates high-end training, AMD's MI300X challenges NVIDIA's monopoly, and cloud GPU prices have dropped 40-60% thanks to decentralized providers like io.net competing with AWS and Google Cloud.

This guide covers everything you need to know about selecting GPUs for ML workloads in 2026—including detailed specifications for NVIDIA H100/H200/A100, AMD MI300X, and RTX 4090, real-world benchmarks for training and inference, and comprehensive pricing comparisons across AWS, CoreWeave, Lambda Labs, and io.net.

Whether you're training 405B parameter models, running production inference servers, or fine-tuning open-source LLMs, this guide helps you choose the optimal GPU for your performance requirements and budget.

GPU Architecture Fundamentals for Machine Learning

Modern machine learning workloads demand three critical GPU capabilities: massive parallel compute (measured in TFLOPS), high memory bandwidth (GB/s for feeding data to GPU cores), and large VRAM capacity (for holding model weights and activations).

Key Specifications That Matter

Tensor Cores are specialized matrix multiplication units that accelerate AI operations by 10-20x compared to CUDA cores. NVIDIA's 4th generation Tensor Cores (H100/H200) support FP8 precision—delivering 2x throughput over the 3rd gen cores in A100.

Memory bandwidth determines how fast the GPU can move training data from VRAM to compute cores. H100 delivers 3.35 TB/s with HBM3 memory, compared to A100's 2 TB/s with HBM2e. This 67% improvement translates directly to faster training for memory-bound workloads like transformers.

VRAM capacity limits maximum model size. A 70B parameter LLM requires ~140GB VRAM in FP16 (2 bytes per parameter) or ~70GB with INT8 quantization. H100's 80GB VRAM handles most models; for 405B models, you'll need multi-GPU setups or H200's 141GB configuration.

Interconnect speed (NVLink, Infiniband) determines multi-GPU scaling efficiency. H100 supports NVLink 4.0 at 900 GB/s bidirectional—enabling near-linear scaling across 8-GPU nodes for distributed training.

Best GPUs for Machine Learning in 2026

1. NVIDIA H200 (141GB HBM3e) — Best for Ultra-Large Models

Specs:

141GB HBM3e memory (4.8 TB/s bandwidth)
67 TFLOPS FP64 | 134 TFLOPS FP32 | 989 TFLOPS FP16 | 1,979 TFLOPS FP8
4th Gen Tensor Cores with Transformer Engine
900 GB/s NVLink 4.0 interconnect
700W TDP

Best for: Training 405B+ parameter models, multi-modal models, scientific computing requiring massive VRAM.

Pricing:

AWS (p5e instances): Not yet available (expected Q3 2026)
CoreWeave: $3.50-4.20/hr (waitlist)
io.net: $2.49-2.99/hr (limited availability)

Real-world performance:

Llama 3.1 405B training: 4.2x faster than A100 (8-GPU cluster)
Stable Diffusion XL: 18 seconds per image (batch 1)
GPT-4 class model inference: 85 tokens/sec (FP8 quantization)

Availability: Very limited. H200 production ramped in Q1 2026; most cloud providers won't have stock until Q3-Q4 2026.

2. NVIDIA H100 (80GB SXM / 80GB PCIe) — Best Overall for Training

Specs:

80GB HBM3 memory (3.35 TB/s bandwidth SXM | 2 TB/s PCIe)
60 TFLOPS FP64 | 120 TFLOPS FP32 | 756 TFLOPS FP16 | 1,513 TFLOPS FP8
4th Gen Tensor Cores with FP8 support
900 GB/s NVLink 4.0 (SXM) | 600 GB/s (PCIe)
700W TDP (SXM) | 350W (PCIe)

SXM vs PCIe: SXM offers 67% higher memory bandwidth and 50% faster NVLink—critical for multi-GPU training. PCIe variant costs 30-40% less but sacrifices performance.

Best for: Training 70B-405B models, large-scale fine-tuning, production inference at scale.

Pricing:

AWS (p5.48xlarge): $32.77/hr for 8x H100 SXM = $4.10/hr per GPU
Google Cloud: $3.67/hr (spot) to $5.50/hr (on-demand)
CoreWeave: $2.25-2.75/hr
Lambda Labs: $2.49/hr (frequently sold out)
io.net: $1.49-2.20/hr (50-70% savings vs hyperscalers)

Real-world benchmarks:

Llama 2 70B fine-tuning: 18 hours (8x H100) vs 72 hours (8x A100)
ResNet-50 training (ImageNet): 42 minutes (8x H100) vs 78 minutes (8x A100)
BERT-Large pre-training: 3.2 days (8x H100) vs 11 days (8x A100)

Availability: Moderate. AWS has 3-6 month waitlists; CoreWeave and io.net offer instant provisioning.

3. AMD MI300X (192GB HBM3) — Best VRAM-to-Price Ratio

Specs:

192GB HBM3 memory (5.3 TB/s bandwidth) — 2.4x more VRAM than H100
163 TFLOPS FP64 | 653 TFLOPS FP32 | 1,307 TFLOPS FP16
750W TDP

Strengths: Massive 192GB VRAM enables training 70B models on a single GPU without quantization. Best for researchers who need maximum memory.

Weaknesses: Limited software ecosystem (PyTorch support still maturing), fewer cloud providers, 15-20% slower than H100 for most workloads despite higher theoretical FLOPS.

Best for: Ultra-large context windows (128K+ tokens), genomics/scientific computing, teams avoiding NVIDIA vendor lock-in.

Pricing:

Azure (ND MI300X v5): $3.80-4.50/hr
Vultr: $2.99/hr
io.net: $1.89-2.40/hr

Real-world performance:

Llama 3.1 70B inference: 62 tokens/sec vs 78 tokens/sec (H100)
Stable Diffusion XL: 4.8 sec/image vs 3.2 sec (H100)
Long-context QA (128K tokens): 18% faster than H100 (benefits from extra VRAM)

Availability: Limited but improving. Azure has best stock; io.net added 500+ MI300X units in April 2026.

4. NVIDIA A100 (80GB) — Best Value for Most Workloads

Specs:

80GB HBM2e memory (2 TB/s bandwidth)
19.5 TFLOPS FP64 | 156 TFLOPS FP32 | 312 TFLOPS FP16
3rd Gen Tensor Cores (no FP8 support)
600 GB/s NVLink 3.0
400W TDP

Why still relevant in 2026: A100 delivers 70-80% of H100 performance at 40-50% of the cost. For most training jobs (models under 70B parameters), the cost-performance ratio beats H100.

Best for: Fine-tuning 7B-70B models, medium-scale training, inference servers, teams on tight budgets.

Pricing:

AWS (p4de.24xlarge): $40.96/hr for 8x A100 = $5.12/hr per GPU (ridiculously expensive)
Google Cloud: $2.48/hr (spot) to $3.67/hr (on-demand)
CoreWeave: $2.10/hr
io.net: $1.19-1.89/hr (60-75% cheaper than AWS)

Real-world benchmarks:

Mistral 7B fine-tuning: 4.2 hours (1x A100) vs 2.1 hours (1x H100)
Stable Diffusion 2.1: 2.8 sec/image vs 1.9 sec (H100)
BERT-Base training: 6 hours (8x A100) vs 3.5 hours (8x H100)

Cost analysis (fine-tuning Llama 2 13B for 12 hours):

H100 at $2.20/hr: $26.40
A100 at $1.40/hr: $16.80 (36% cheaper, only ~20% slower)

Availability: Excellent. Most cloud providers have ample A100 stock with no waitlists.

5. NVIDIA RTX 4090 (24GB) — Best for Budget-Conscious Development

Specs:

24GB GDDR6X memory (1 TB/s bandwidth)
82.6 TFLOPS FP32 | 165 TFLOPS FP16 | 330 TFLOPS FP8 (via software)
4th Gen Tensor Cores (Ada Lovelace)
450W TDP

When RTX 4090 makes sense:

Fine-tuning models up to 13B parameters (with quantization)
Inference for 70B models (INT4/INT8 quantization)
Development and prototyping before scaling to H100/A100
Stable Diffusion, ControlNet, and image generation workloads

When to avoid: Training 70B+ models, production inference requiring low latency, multi-GPU distributed training (no NVLink on consumer cards).

Pricing:

AWS: Not available
Vast.ai: $0.29-0.49/hr
RunPod: $0.34/hr
io.net: $0.28-0.42/hr (35,000+ units available)

Real-world performance:

Mistral 7B fine-tuning (QLoRA): 8 hours vs 4.2 hours (A100)
Stable Diffusion XL: 6 sec/image vs 2.8 sec (A100)
Llama 2 70B inference (INT4): 12 tokens/sec vs 45 tokens/sec (A100 FP16)

Cost comparison (100 hours of compute):

A100 at $1.40/hr: $140
RTX 4090 at $0.35/hr: $35 (75% savings)

For workloads that fit in 24GB VRAM, RTX 4090 delivers unbeatable value.

GPU Comparison Table: ML Performance & Pricing

GPU	VRAM	FP16 TFLOPS	Memory BW	Best Price/Hr	AWS Price	Training Speed (Llama 70B)	Cost/Performance Score
H200	141GB	989	4.8 TB/s	$2.49 (io.net)	N/A	Baseline (fastest)	9.5/10
H100 SXM	80GB	756	3.35 TB/s	$1.49 (io.net)	$4.10	1.3x vs H200	10/10
H100 PCIe	80GB	756	2 TB/s	$1.29 (io.net)	$3.50	1.5x vs H200	9/10
MI300X	192GB	1,307	5.3 TB/s	$1.89 (io.net)	$3.80	1.6x vs H200	8/10
A100 80GB	80GB	312	2 TB/s	$1.19 (io.net)	$5.12	2.8x vs H200	9.5/10
A100 40GB	40GB	312	1.6 TB/s	$0.89 (io.net)	$3.06	3.2x vs H200	8.5/10
RTX 4090	24GB	165	1 TB/s	$0.28 (io.net)	N/A	7x vs H200*	7/10

*RTX 4090 requires quantization for 70B models; performance depends on precision.

Cost/Performance Score factors: raw speed, cost per TFLOP, VRAM capacity, availability, ecosystem support.

Cloud Provider Comparison for ML GPUs

io.net — Best Overall (Lowest Cost + Best Availability)

Pricing:

H100 SXM: $1.49-2.20/hr (70% cheaper than AWS)
A100 80GB: $1.19-1.89/hr
RTX 4090: $0.28-0.42/hr
MI300X: $1.89-2.40/hr

Strengths:

200,000+ GPUs available instantly (no waitlists)
Decentralized network = competitive pricing
Bare-metal SSH access (no virtualization overhead)
Kubernetes, Docker, Ray support
Free 1TB egress/month

Weaknesses:

No managed ML services (SageMaker equivalent)
Provider SLAs vary (enterprise tier offers 99.9% uptime)

Best for: Cost-sensitive teams, startups, researchers who need flexibility.

AWS — Best Ecosystem Integration (Worst Pricing)

Pricing:

H100 SXM: $4.10/hr (p5.48xlarge, 8-GPU instance)
A100 80GB: $5.12/hr (p4de.24xlarge)
A100 40GB: $3.06/hr (p4d.24xlarge)

Strengths:

SageMaker for managed training/inference
200+ integrated services (S3, Lambda, EC2)
Enterprise support and compliance (SOC 2, HIPAA, FedRAMP)

Weaknesses:

2-3x more expensive than io.net/CoreWeave
3-6 month waitlists for H100
$0.08-0.12/GB egress fees
Vendor lock-in via proprietary APIs

Best for: Fortune 500 enterprises requiring AWS integration.

CoreWeave — Best for Enterprise GPU-as-a-Service

Pricing:

H100 SXM: $2.25-2.75/hr
A100 80GB: $2.10/hr
H200: $3.50-4.20/hr (waitlist)

Strengths:

100,000+ GPUs with enterprise SLAs
Kubernetes-native infrastructure
Fast provisioning (<2 min)
Dedicated support for large deployments

Weaknesses:

Minimum spend requirements ($1,000+/month)
Contact sales for access (no self-serve)

Best for: Mid-market to enterprise teams scaling beyond 20 GPUs.

Lambda Labs — Good for Persistent Workloads

Pricing:

H100 SXM: $2.49/hr (frequently sold out)
A100 80GB: $1.29-1.99/hr

Strengths:

Simple pricing, no egress fees
Good documentation and support
Persistent storage included

Weaknesses:

Limited GPU availability (frequent sellouts)
Slower provisioning (5-15 min)
No Kubernetes support

Best for: Academic researchers, long-running training jobs.

Choosing the Right GPU for Your Workload

Training Large Language Models (70B-405B parameters)

Recommended: H100 SXM (80GB) or H200 (141GB)

Why: Training requires massive parallel compute and high memory bandwidth. H100's 3.35 TB/s bandwidth and FP8 Tensor Cores deliver 3-4x faster training than A100.

Multi-GPU setup: 8x H100 SXM cluster for 70B models, 16-32x for 405B models.

Cost example (training Llama 3.1 70B for 72 hours):

8x H100 on AWS: $2,366 (8 × $4.10 × 72)
8x H100 on io.net: $856 (8 × $1.49 × 72)
Savings: $1,510 (64%)

Fine-Tuning Open-Source Models (7B-13B)

Recommended: A100 40GB or RTX 4090

Why: Fine-tuning smaller models doesn't need H100's extra power. A100 or 4090 deliver excellent performance at 50-70% lower cost.

Cost example (QLoRA fine-tuning Mistral 7B for 8 hours):

A100 on io.net: $9.52 (1 × $1.19 × 8)
RTX 4090 on io.net: $2.80 (1 × $0.35 × 8)

If your dataset fits in VRAM and training time isn't critical, RTX 4090 saves 70%.

Production Inference Servers

Recommended: A100 80GB (for <70B models) or H100 (for 70B+ models)

Why: Inference prioritizes throughput and low latency. A100 handles most models; H100 needed for large context windows or very low latency.

Deployment pattern:

Use vLLM or TensorRT-LLM for optimized serving
Enable FP8 quantization on H100 (2x throughput)
Deploy across 3+ GPUs for redundancy

Cost example (serving Llama 2 70B at 100 req/sec):

3x A100 on AWS: $368/day (3 × $5.12 × 24)
3x A100 on io.net: $85/day (3 × $1.19 × 24)
Monthly savings: $8,490

Image Generation (Stable Diffusion, Flux, DALL-E)

Recommended: RTX 4090 or A100 40GB

Why: Image generation models (SDXL, Flux.1) fit in 24GB VRAM. RTX 4090 delivers 80% of A100 speed at 20% of the cost.

Performance comparison (SDXL, 1024×1024, batch 1):

H100: 1.9 sec/image ($2.20/hr) = $0.0012/image
A100: 2.8 sec/image ($1.40/hr) = $0.0011/image
RTX 4090: 6 sec/image ($0.35/hr) = $0.0006/image ← best value

For high-volume generation (10,000+ images/day), RTX 4090 saves 40-50% vs A100.

Research and Prototyping

Recommended: RTX 4090 or A100 40GB (on-demand/spot)

Why: Early-stage research requires flexibility and low costs. Rent GPUs hourly, experiment, then scale to H100 for final training.

Strategy: Prototype on RTX 4090 ($0.35/hr), validate architecture, then train production model on 8x H100.

Getting Started: Deploy ML Workloads on io.net

Step 1: Install io.net CLI

# Install CLI
curl -fsSL https://cli.io.net/install.sh | sh

# Login to your account
ionet login

# Verify installation
ionet --version

Step 2: Browse Available GPUs

# List all available GPUs with pricing
ionet gpu list

# Filter by GPU type
ionet gpu list --type h100 --region us-west

# Check real-time availability
ionet gpu availability --type a100-80gb

Sample output:

GPU Type       | Region    | Available | Price/Hr | Memory  | CUDA Cores
---------------|-----------|-----------|----------|---------|------------
H100 SXM       | us-west-1 | 234       | $1.49    | 80GB    | 16,896
H100 SXM       | eu-west-1 | 89        | $1.69    | 80GB    | 16,896
A100 80GB      | us-east-1 | 1,247     | $1.19    | 80GB    | 6,912
A100 40GB      | us-west-2 | 892       | $0.89    | 40GB    | 6,912
RTX 4090       | us-west-1 | 3,421     | $0.28    | 24GB    | 16,384
MI300X         | us-east-1 | 47        | $1.89    | 192GB   | 19,456

Step 3: Launch GPU Instance

# Launch single H100 with PyTorch image
ionet deploy \
  --name llama-training \
  --gpu h100-sxm \
  --gpu-count 1 \
  --image pytorch/pytorch:2.5.0-cuda12.4 \
  --port 8888 \
  --ssh-key ~/.ssh/id_rsa.pub

# Launch 8x A100 cluster for distributed training
ionet deploy \
  --name distributed-cluster \
  --gpu a100-80gb \
  --gpu-count 8 \
  --image nvcr.io/nvidia/pytorch:24.11-py3 \
  --network-mode cluster \
  --ssh-key ~/.ssh/id_rsa.pub

Response:

✓ Deploying llama-training...
✓ Allocated 1x H100 SXM (80GB) in us-west-1
✓ Instance IP: 34.123.45.67
✓ SSH: ssh [email protected]
✓ Jupyter: http://34.123.45.67:8888
✓ Cost: $1.49/hr

Step 4: Deploy Training Job

SSH into instance and run training:

# SSH to instance
ssh [email protected]

# Clone training repo
git clone https://github.com/huggingface/transformers
cd transformers/examples/pytorch/language-modeling

# Install dependencies
pip install -r requirements.txt accelerate bitsandbytes

# Run distributed training (if using multi-GPU)
accelerate launch --multi_gpu --num_processes 8 run_clm.py \
  --model_name_or_path meta-llama/Llama-2-70b-hf \
  --dataset_name wikitext \
  --dataset_config_name wikitext-103-v1 \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 8 \
  --num_train_epochs 3 \
  --output_dir ./llama-70b-finetuned \
  --fp16 \
  --logging_steps 10

Step 5: Monitor GPU Utilization

# View real-time GPU metrics
ionet metrics --instance llama-training

# Check cost accrual
ionet billing --instance llama-training

# SSH into instance and use nvidia-smi
ssh [email protected] nvidia-smi

Step 6: Cleanup

# Stop instance (preserves data)
ionet stop llama-training

# Terminate instance (deletes data)
ionet terminate llama-training

Advanced: Kubernetes GPU Deployment

Deploy multi-GPU workloads using Kubernetes:

# llama-training-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: llama-70b-training
spec:
  template:
    spec:
      containers:
      - name: trainer
        image: nvcr.io/nvidia/pytorch:24.11-py3
        command: ["accelerate", "launch", "--multi_gpu", "train.py"]
        resources:
          limits:
            nvidia.com/gpu: 8  # Request 8x GPUs
        env:
        - name: CUDA_VISIBLE_DEVICES
          value: "0,1,2,3,4,5,6,7"
      restartPolicy: Never
      nodeSelector:
        gpu-type: h100-sxm  # Target H100 nodes

Deploy to io.net Kubernetes cluster:

# Connect to io.net Kubernetes cluster
ionet k8s login

# Deploy training job
kubectl apply -f llama-training-job.yaml

# Monitor job progress
kubectl get pods -w

# Check GPU usage across cluster
kubectl get nodes -o custom-columns=NAME:.metadata.name,GPU:.status.allocatable.'nvidia\.com/gpu'

Cost Optimization Strategies

1. Use Spot Instances for Fault-Tolerant Workloads

Spot instances offer 50-70% discounts but can be reclaimed with 2-minute notice.

Good for: Data preprocessing, hyperparameter sweeps, checkpointed training jobs.

Bad for: Production inference, time-sensitive training without checkpointing.

# Launch spot A100 instance
ionet deploy --gpu a100-80gb --pricing spot --max-price 0.90

2. Right-Size Your GPU Selection

Don't overpay for H100 when A100 or RTX 4090 suffice.

Example: Fine-tuning Mistral 7B

H100 at $1.49/hr: 2 hours = $2.98
A100 at $1.19/hr: 4 hours = $4.76
RTX 4090 at $0.35/hr: 8 hours = $2.80 ← best value

For non-urgent workloads, slower GPUs save money.

3. Implement Auto-Shutdown

Terminate idle instances to avoid paying for unused time:

# Auto-terminate after 2 hours of idle GPU (<10% utilization)
ionet deploy --gpu h100 --auto-shutdown 2h

4. Use Persistent Storage

Separate storage from compute to avoid re-downloading datasets:

# Create persistent volume
ionet storage create --name my-datasets --size 1TB

# Mount volume to instance
ionet deploy --gpu a100 --volume my-datasets:/data

Cost: Storage costs $0.10/GB/month vs re-downloading 500GB dataset at $0.05/GB egress = $25.

Frequently Asked Questions

What's the best GPU for training large language models in 2026?

H100 SXM (80GB) for models up to 70B parameters. For 405B models, use H200 (141GB) or multi-GPU H100 clusters. H100 delivers 3-4x faster training than A100 thanks to FP8 Tensor Cores and 67% higher memory bandwidth.

If budget is a concern, A100 80GB offers excellent value at 40-50% lower cost with only 20-30% slower training times.

Is RTX 4090 good enough for machine learning?

Yes, for fine-tuning models up to 13B parameters and inference for 70B models (with quantization). RTX 4090's 24GB VRAM and 165 TFLOPS FP16 performance handle most development workloads at 20% of A100's cost.

Avoid RTX 4090 for: Training 70B+ models from scratch, production inference requiring <100ms latency, multi-GPU distributed training (no NVLink).

How much does it cost to train a 70B model?

Training Llama 2 70B from scratch: $150,000-300,000 (weeks of 64-128 GPU training).

Fine-tuning Llama 2 70B on your data:

8x H100 on AWS: $2,366 (72 hours)
8x H100 on io.net: $856 (72 hours)
8x A100 on io.net: $686 (120 hours)

Most teams fine-tune rather than train from scratch—cutting costs by 99%.

Should I use AWS or a decentralized provider like io.net?

Use AWS if: You need SageMaker, require SOC 2/HIPAA compliance, or already have AWS infrastructure.

Use io.net if: You want 50-70% cost savings, need instant GPU access (no waitlists), or prefer bare-metal performance without virtualization overhead.

Hybrid approach: Use io.net for training/development, AWS for production inference integrated with existing services.

What's the difference between H100 SXM and H100 PCIe?

H100 SXM:

3.35 TB/s memory bandwidth (67% faster)
900 GB/s NVLink 4.0 (critical for multi-GPU)
700W TDP (requires server-grade power/cooling)
30-40% more expensive

H100 PCIe:

2 TB/s memory bandwidth
600 GB/s NVLink
350W TDP (fits standard servers)
More widely available

Verdict: SXM for multi-GPU training where every second counts. PCIe for single-GPU inference or budget-conscious training.

Can AMD MI300X compete with NVIDIA H100?

MI300X offers 192GB VRAM (2.4x more than H100) and higher theoretical FLOPS, but real-world performance lags H100 by 15-20% due to less mature software (PyTorch, CUDA ecosystem).

Choose MI300X if: You need massive VRAM (ultra-long context windows, genomics), want to avoid NVIDIA vendor lock-in, or your workload is memory-bound rather than compute-bound.

Stick with H100 if: You need maximum performance, rely on NVIDIA-specific features (TensorRT, Triton), or want broadest cloud provider support.

How do I choose between on-demand, reserved, and spot instances?

On-demand: Pay-per-hour, no commitments. Best for unpredictable workloads, experimentation, short-term projects.

Reserved: Pre-commit to 1-month or 1-year, save 20-40%. Best for continuous training, production inference, predictable workloads.

Spot: Bid on unused capacity, save 50-70% but risk termination. Best for fault-tolerant batch jobs, hyperparameter sweeps, checkpointed training.

Strategy: Use reserved for baseline capacity, on-demand for peaks, spot for batch workloads.

Conclusion: Choose the Right GPU for Your ML Workload

The best GPU for machine learning in 2026 depends on your workload, budget, and performance requirements:

Training 70B-405B models: H100 SXM or H200 for maximum speed
Fine-tuning open-source models (7B-13B): A100 or RTX 4090 for best value
Production inference: A100 80GB for most models, H100 for ultra-low latency
Image generation: RTX 4090 delivers 80% of A100 performance at 20% cost
Research/prototyping: RTX 4090 or spot A100 instances

Cloud provider choice matters as much as GPU selection. io.net delivers 50-70% cost savings vs AWS/Google Cloud through decentralized GPU marketplace, with instant provisioning and no waitlists.

Ready to get started? Sign up for io.net test H100, A100, or RTX 4090 GPUs on your workload.

About io.net: io.net operates the world's largest decentralized GPU network with thousands of GPUs available on-demand. Our platform serves ML engineers, AI researchers, and enterprises requiring cost-effective compute for training and inference workloads. Learn more →