GMI Cloud vs SF Compute: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for GMI Cloud and SF Compute. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
GMI Cloud provides H100 and H200 GPU clusters across US and APAC regions with NVLink interconnects for high-bandwidth distributed AI training and large-scale LLM inference. Competitive on-demand pricing and large cluster support make it a strong option for APAC-based AI teams that need flagship NVIDIA hardware without the latency of US-only providers. A reliable specialist GPU cloud for organizations running multi-node training workloads across North America and Asia-Pacific.
- Competitive H100/H200 pricing
- APAC region availability
- High-bandwidth interconnects
- Large cluster support
- Small provider — limited scale for very large clusters
- Less mature platform vs CoreWeave or Lambda
- Limited ecosystem integrations
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SF Compute offers H100 SXM5 80GB GPU clusters with both short-term on-demand and longer-term contract pricing. The platform targets AI training and research teams that need reliable H100 access at competitive rates. October 2026 on-demand rate: $5.04/GPU-hr. SF Compute provides VMs, bare metal, and managed Slurm cluster options, making it flexible for different workflow types. A practical choice for teams that want H100 compute without the overhead of hyperscaler pricing.
- H100 SXM5 80GB at competitive on-demand rates
- Bare metal and managed Slurm options
- Short and long-term contract flexibility
- US West region — low latency for West Coast teams
- Limited GPU catalog — primarily H100
- Smaller provider — less geographic diversity
- Less mature ecosystem vs established GPU clouds
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Key differences
GMI Cloud
US and APAC H100/H200 clusters with competitive on-demand pricing
SF Compute
H100 cluster compute — contract and on-demand GPU access from $5.04/hr