Does Latitude.sh support multi-node GPU clusters?
💡 Answer
Latitude.sh supports multi-GPU configurations with the following specifications:
Interconnect technology: NVLink
Maximum GPUs per instance: 8
Multi-node training: 0
The choice of interconnect is critical for distributed training performance. NVLink provides up to 900 GB/s bidirectional bandwidth between GPUs, while InfiniBand enables high-speed communication across nodes. PCIe-only setups are suitable for inference but may bottleneck multi-GPU training.
Available GPU models: A30, RTX A5000, RTX A6000, L40S, RTX 6000 Ada, A100 SXM, H100 SXM, GH200, RTX PRO 6000
For detailed interconnect specs and multi-GPU topology diagrams, see Latitude.sh official website.
More FAQs about Latitude.sh
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These guides include Latitude.sh alongside other cloud GPU providers, grouped by hardware, pricing, features, and infrastructure.
Latitude.sh vs Vultr vs Massed Compute - GPU Provider Comparison (March 2026)
Side-by-side comparison of Latitude.sh vs Vultr vs Massed Compute. 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 March 2026.
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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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Massed Compute
GPU cloud with direct engineer support
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|---|---|---|---|
| Overview | |||
| Trustpilot Rating | 3.7 | 1.8 | 0 |
| Headquarters | Brazil | United States | United States |
| Provider Type | Bare Metal | Multi-Cloud | GPU-Focused |
| Best For | 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 | AI training inference VFX rendering generative AI fine-tuning HPC Stable Diffusion research |
| GPU Hardware | |||
| GPU Models | 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 | A30 RTX A5000 RTX A6000 L40S A100 SXM H100 PCIe H100 SXM H100 NVL RTX PRO 6000 H200 NVL |
| Max VRAM (GB) | 96 | 288 | 141 |
| Max GPUs/Instance | 8 | 16 | 8 |
| Interconnect | NVLink | NVLink | NVLink |
| Pricing | |||
| Starting Price ($/hr) | $0.35/hr | $0.47/hr | $0.35/hr |
| Billing Granularity | Per-hour | Per-hour | Per-minute |
| Spot/Preemptible | 0 | 1 | 0 |
| Reserved Discounts | N/A | N/A | N/A |
| Free Credits | $200 via referral program | Up to $300 free credit for 30 days | None |
| Egress Fees | None | Standard (varies by plan) | None |
| Storage | 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 | Local NVMe included with instances |
| Infrastructure | |||
| 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) | United States (Tier III data centers) |
| Uptime SLA | 99.9% | 100% | Tier III (99.98% design) |
| Developer Experience | |||
| Frameworks | ML-optimized images PyTorch TensorFlow (user-installed) CUDA | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC | PyTorch TensorFlow CUDA cuDNN ComfyUI pre-configured ML templates |
| Docker Support | 1 | 1 | 1 |
| SSH Access | 1 | 1 | 1 |
| Jupyter Notebooks | 0 | 1 | 0 |
| API / CLI | 1 | 1 | 1 |
| Setup Time | Seconds | Minutes | Minutes |
| Kubernetes Support | 0 | 1 | 0 |
| Business Terms | |||
| Min Commitment | None | None | None |
| Compliance | Single-tenant isolation DPA available | SOC 2+ (HIPAA) PCI ISO 27001 ISO 27017 ISO 27018 ISO 20000-1 CSA STAR Level 1 | SOC 2 Type II HIPAA |