What interconnect technology does RunPod use for multi-GPU training?
💡 Answer
Multi-GPU and distributed training capabilities at RunPod:
- Interconnect: NVLink
- Max GPUs per instance: 8
- Multi-node clusters: 1
For training large models like LLMs that require multiple GPUs, the interconnect bandwidth directly impacts training throughput. High-bandwidth interconnects like NVLink and InfiniBand minimize the communication overhead during gradient synchronization, resulting in near-linear scaling across GPUs.
View NVLink and InfiniBand configurations at RunPod official website.
More FAQs about RunPod
- Who should use RunPod for cloud GPU?
- What is the current Trustpilot rating and number of reviews for RunPod?
- Does RunPod come with PyTorch, TensorFlow, or JAX pre-installed?
- Does RunPod support Docker, SSH, and Jupyter Notebooks?
- Can I run GPU workloads on RunPod without managing servers?
- What regions does RunPod operate in?
- Can I get discounted GPU rates at RunPod through spot instances?
- Are there any data transfer costs at RunPod?
- Can I try RunPod for free before committing?
- Which NVIDIA and AMD GPUs are available at RunPod?
- How much does RunPod cost per hour for GPU instances?
Guides Where RunPod Is Featured
- Best Cloud GPU Providers with NVIDIA A100
- Best Cloud GPUs for Stable Diffusion & Image Generation
- Cheapest Cloud GPUs Under $0.50/hr
- Cloud GPU Providers with API & CLI Management
- Cloud GPU Providers with Docker & Custom Images
- Cloud GPU Providers with Free Credits
- Cloud GPU Providers with Jupyter Notebook Support
- Cloud GPU Providers with Kubernetes Support
- Cloud GPU Providers with Multi-Node GPU Clusters
- Cloud GPU Providers with NVLink or InfiniBand
- Cloud GPU Providers with Per-Second Billing
- Cloud GPU Providers with Persistent Storage
- Cloud GPU Providers with Serverless GPU Inference
- Cloud GPU Providers with Spot / Preemptible Instances
- Cloud GPU Providers with SSH Access
- Cloud GPU Providers with Zero Egress Fees
These guides include RunPod alongside other cloud GPU providers, grouped by hardware, pricing, features, and infrastructure.
RunPod vs Latitude.sh vs Vultr - GPU Provider Comparison (April 2026)
Side-by-side comparison of RunPod vs Latitude.sh vs Vultr. Quickly scan maximum funding, profit splits, risk rules, leverage, platforms, instruments, payout schedules, payment options, trading permissions and KYC restrictions to narrow down your prop trading firm shortlist. Data updated April 2026.
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RunPod
The cloud built for AI — deploy and scale GPU workloads from serverless inference to instant multi-node clusters on demand.
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Latitude.sh
Bare metal GPU cloud across 23 global locations
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Vultr
High-performance cloud GPU across 32 global regions
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|
|---|---|---|---|
| Overview | |||
| Trustpilot Rating | 3.8 | 3.7 | 1.8 |
| Headquarters | United States | Brazil | United States |
| Provider Type | GPU-Focused | Bare Metal | Multi-Cloud |
| Best For | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI | AI training inference bare metal GPU fine-tuning research dedicated workloads generative AI | AI training inference video rendering HPC Stable Diffusion game development generative AI fine-tuning research |
| GPU Hardware | |||
| GPU Models | B300 B200 H200 H100 SXM H100 PCIe H100 NVL MI300X A100 SXM A100 PCIe RTX 5090 RTX PRO 6000 L40S L40 RTX 6000 Ada RTX 5000 Ada RTX A6000 RTX A5000 RTX 4090 RTX 4080 SUPER RTX 4080 RTX 4070 Ti RTX 3090 Ti RTX 3090 RTX 3080 Ti RTX 3080 RTX 3070 A40 A30 A2 L4 | A30 RTX A5000 RTX A6000 L40S RTX 6000 Ada A100 SXM H100 SXM GH200 RTX PRO 6000 | A16 A40 L40S A100 PCIe GH200 A100 SXM H100 SXM B200 B300 MI300X MI325X MI355X |
| Max VRAM (GB) | 288 | 96 | 288 |
| Max GPUs/Instance | 8 | 8 | 16 |
| Interconnect | NVLink | NVLink | NVLink |
| Pricing | |||
| Starting Price ($/hr) | $0.06/hr | $0.35/hr | $0.47/hr |
| Billing Granularity | Per-second | Per-hour | Per-hour |
| Spot/Preemptible | 1 | 0 | 1 |
| Reserved Discounts | 15-29% (1-month to 1-year plans) | N/A | N/A |
| Free Credits | $5-$500 bonus after first $10 spend | $200 via referral program | Up to $300 free credit for 30 days |
| Egress Fees | None (Free) | None | Standard (varies by plan) |
| Storage | Container/Volume ($0.10/GB/mo), Idle Volume ($0.20/GB/mo), Network Storage ($0.07/GB/mo 1TB) | Local NVMe included (up to 4x 3.8TB), Block Storage $0.10/GB/mo, Filesystem Storage $0.05/GB/mo | 350 GB - 61 TB NVMe (included), Block Storage at $0.10/GB/mo, S3-compatible Object Storage |
| Infrastructure | |||
| Regions | 31 global regions | 23 locations: US (8 cities), LATAM (5), Europe (5), APAC (4), Mexico City. GPU in Dallas, Frankfurt, Sydney, Tokyo | 32 regions across 6 continents (Americas, Europe, Asia, Australia, Africa) |
| Uptime SLA | 99.99% | 99.9% | 100% |
| Developer Experience | |||
| Frameworks | PyTorch TensorFlow JAX ONNX CUDA | ML-optimized images PyTorch TensorFlow (user-installed) CUDA | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC |
| Docker Support | 1 | 1 | 1 |
| SSH Access | 1 | 1 | 1 |
| Jupyter Notebooks | 1 | 0 | 1 |
| API / CLI | 1 | 1 | 1 |
| Setup Time | Instant | Seconds | Minutes |
| Kubernetes Support | 0 | 0 | 1 |
| Business Terms | |||
| Min Commitment | None | None | None |
| Compliance | SOC 2 Type II | Single-tenant isolation DPA available | SOC 2+ (HIPAA) PCI ISO 27001 ISO 27017 ISO 27018 ISO 20000-1 CSA STAR Level 1 |