3NV Cloud vs TensorWave: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for 3NV Cloud and TensorWave. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
3NV Cloud is an Estonian cloud provider offering VMs and bare metal servers from data centers in Tallinn, Estonia, with EU data residency and competitive pricing for European teams. GPU-enabled configurations are available for AI and HPC workloads, making it a niche option for teams that specifically need Estonian or Baltic data residency for compliance or latency reasons.
- Estonian/Baltic data residency
- Bare metal and VM configurations
- EU data residency and GDPR compliance
- Competitive pricing for European teams
- Very small provider — limited scale
- Single-region (Estonia) — no global coverage
- Limited GPU catalog and documentation
No reviews yet — add yours ↗
TensorWave specializes in AMD Instinct MI300X and MI325X GPU instances — the highest-memory GPU accelerators available in any cloud — offering a compelling NVIDIA alternative for large-model LLM inference and distributed AI training via the ROCm ecosystem. On-demand and reserved billing options are available from US-based data centers, with competitive pricing relative to equivalent NVIDIA H100 configurations. The go-to on-demand GPU cloud for teams exploring AMD ROCm or needing massive VRAM for large-context inference.
- AMD MI300X/MI325X — highest VRAM in any cloud
- NVIDIA alternative via ROCm ecosystem
- Competitive pricing vs equivalent H100 configs
- Large-context inference capability
- AMD ROCm ecosystem less mature than CUDA
- Smaller provider — limited scale vs hyperscalers
- Fewer regions — US-only
No reviews yet — add yours ↗
Live GPU Pricing
Region Coverage
Popular Comparisons
Provider pages
Key differences
3NV Cloud
VMs and bare metal hosted in Tallinn, Estonia
TensorWave
AMD MI300X and MI325X GPU cloud — highest-VRAM accelerators for large-model inference