How does AMD Instinct MI300X benchmark against H100?
💡 Answer
Benchmarked performance on AMD Instinct MI300X: 1,307 TFLOPS in FP16, 163.4 TFLOPS in FP32, 5,300 GB/s memory bandwidth, 192 GB VRAM.
For the workloads most engineers care about — model training transformer-family models, serving LLM low-latency inference, running diffusion and vision pipelines — those specs are enough to sustain batch sizes that keep tensor cores busy. Expect wall-clock gains versus previous-generation CDNA 3 cards to range from 1.5x to 3x depending on workload shape.
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Vultr vs DigitalOcean vs Runpod - GPU Provider Comparison (August 2026)
Side-by-side comparison of Vultr vs DigitalOcean vs Runpod. Quickly compare GPU models, hourly pricing, spot instances, billing granularity, VRAM, regions, developer tools, Kubernetes support, and compliance to narrow down your cloud GPU provider shortlist. Data updated August 2026.
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Vultr
High-performance cloud GPU across 32 global regions
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DigitalOcean
Simple, scalable GPU cloud for AI/ML
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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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|---|---|---|---|
| Overview | |||
| Trustpilot Rating | 1.6 | 4.6 | 3.7 |
| Headquarters | United States | United States | United States |
| Provider Type | Multi-Cloud | N/A | GPU-Focused |
| Best For | AI training inference video rendering HPC Stable Diffusion game development generative AI fine-tuning research | AI training inference fine-tuning LLM deployment LLM serving computer vision startups generative AI research | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI |
| GPU Hardware | |||
| GPU Models | A16 A40 L40S A100 PCIe GH200 A100 SXM H100 SXM B200 B300 MI300X MI325X MI355X | RTX 4000 Ada RTX 6000 Ada L40S MI300X H100 SXM H200 | 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 |
| Max VRAM (GB) | 288 | 192 | 288 |
| Max GPUs/Instance | 16 | 8 | 8 |
| Interconnect | NVLink | NVLink | NVLink |
| Pricing | |||
| Starting Price ($/hr) | $0.47/hr | $0.76/hr | $0.06/hr |
| Billing Granularity | Per-hour | Per-second | Per-second |
| Spot/Preemptible | Yes | No | No |
| Reserved Discounts | N/A | N/A | 15-29% (1-month to 1-year plans) |
| Free Credits | Up to $300 free credit for 30 days | $200 free credit for 60 days | $5-$500 bonus after first $10 spend |
| Egress Fees | Standard (varies by plan) | None (included in plan) | None (Free) |
| Storage | 350 GB - 61 TB NVMe (included), Block Storage at $0.10/GB/mo, S3-compatible Object Storage | 500-720 GiB NVMe boot (included), 5 TiB NVMe scratch on larger configs, Volumes at $0.10/GiB/mo | Container/Volume ($0.10/GB/mo), Idle Volume ($0.20/GB/mo), Network Storage ($0.07/GB/mo 1TB) |
| Infrastructure | |||
| Regions | 32 regions across 6 continents (Americas, Europe, Asia, Australia, Africa) | New York (NYC2), Toronto (TOR1), Atlanta (ATL1), Richmond (RIC1), Amsterdam (AMS3) | 31 global regions |
| Uptime SLA | 100% | 99% | 99.99% |
| Developer Experience | |||
| Frameworks | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC | PyTorch TensorFlow Jupyter Miniconda CUDA ROCm Hugging Face | PyTorch TensorFlow JAX ONNX CUDA |
| Docker Support | Yes | Yes | Yes |
| SSH Access | Yes | Yes | Yes |
| Jupyter Notebooks | Yes | Yes | Yes |
| API / CLI | Yes | Yes | Yes |
| Setup Time | Minutes | Minutes | Instant |
| Kubernetes Support | Yes | Yes | No |
| Business Terms | |||
| Min Commitment | None | None | None |
| Compliance | SOC 2+ (HIPAA) PCI ISO 27001 ISO 27017 ISO 27018 ISO 20000-1 CSA STAR Level 1 | SOC 2 Type II SOC 3 HIPAA (with BAA) CSA STAR Level 1 | SOC 2 Type II |
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