Can I use custom ML frameworks on Cherry Servers?
💡 Answer
The ML framework ecosystem at Cherry Servers:
PyTorch, TensorFlow, CUDA (bare metal — full stack control)
These frameworks come pre-configured and optimized for the available GPU hardware. Custom images (1) allow you to deploy any framework or library not included by default, including nightly builds, custom forks, or specialized inference engines like vLLM or TensorRT.
Persistent storage (1) ensures your datasets and model weights survive instance restarts.
Check which ML frameworks come pre-installed on Cherry Servers official website.
More FAQs about Cherry Servers
- What type of workloads is Cherry Servers ideal for?
- What is Cherry Servers Trustpilot rating and total review count?
- What developer tools are available at Cherry Servers?
- Does Cherry Servers support scale-to-zero GPU deployments?
- What is Cherry Servers uptime SLA guarantee?
- Can I run distributed training across multiple GPUs at Cherry Servers?
- Are spot instances available at Cherry Servers for cost savings?
- How much does Cherry Servers charge for outbound data transfer?
- How can I get free GPU credits at Cherry Servers?
- What is the maximum VRAM available on Cherry Servers GPU instances?
- What are the pricing plans and billing options at Cherry Servers?
Guides Where Cherry Servers Is Featured
- Best Cloud GPU Providers with NVIDIA RTX 3090
- Best Cloud GPUs for Fine-Tuning LLMs
- Cheapest Cloud GPUs Under $1/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 Cherry Servers alongside other cloud GPU providers, grouped by hardware, pricing, features, and infrastructure.
Cherry Servers vs RunPod vs Massed Compute - GPU Provider Comparison (April 2026)
Side-by-side comparison of Cherry Servers vs RunPod 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 April 2026.
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Cherry Servers
Bare metal GPU servers with 24 years of hosting experience and full hardware-level control.
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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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Massed Compute
GPU cloud with direct engineer support
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|---|---|---|---|
| Overview | |||
| Trustpilot Rating | 4.6 | 3.8 | 0 |
| Headquarters | Lithuania | United States | United States |
| Provider Type | N/A | GPU-Focused | GPU-Focused |
| Best For | AI training inference fine-tuning rendering research HPC generative AI deep learning | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI | AI training inference VFX rendering generative AI fine-tuning HPC Stable Diffusion research |
| GPU Hardware | |||
| GPU Models | A100 A40 A16 A10 A2 Tesla P4 | 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 A100 SXM H100 PCIe H100 SXM H100 NVL RTX PRO 6000 H200 NVL |
| Max VRAM (GB) | 80 | 288 | 141 |
| Max GPUs/Instance | 2 | 8 | 8 |
| Interconnect | PCIe | NVLink | NVLink |
| Pricing | |||
| Starting Price ($/hr) | $0.16/hr | $0.06/hr | $0.35/hr |
| Billing Granularity | Per-hour | Per-second | Per-minute |
| Spot/Preemptible | 0 | 1 | 0 |
| Reserved Discounts | N/A | 15-29% (1-month to 1-year plans) | N/A |
| Free Credits | None | $5-$500 bonus after first $10 spend | None |
| Egress Fees | N/A | None (Free) | None |
| Storage | NVMe SSD, Elastic Block Storage ($0.071/GB/mo) | Container/Volume ($0.10/GB/mo), Idle Volume ($0.20/GB/mo), Network Storage ($0.07/GB/mo 1TB) | Local NVMe included with instances |
| Infrastructure | |||
| Regions | Lithuania, Netherlands, Germany, Sweden, US, Singapore (6 locations) | 31 global regions | United States (Tier III data centers) |
| Uptime SLA | 99.97% | 99.99% | Tier III (99.98% design) |
| Developer Experience | |||
| Frameworks | PyTorch TensorFlow CUDA (bare metal — full stack control) | PyTorch TensorFlow JAX ONNX CUDA | 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 | Minutes | Instant | Minutes |
| Kubernetes Support | 1 | 0 | 0 |
| Business Terms | |||
| Min Commitment | None | None | None |
| Compliance | ISO 27001 ISO 20000-1 GDPR PCI DSS | SOC 2 Type II | SOC 2 Type II HIPAA |