# Spheron

> Spheron is an enterprise GPU rental marketplace. We aggregate NVIDIA GPU compute from Tier 2, 3, and 4 compliant data centers globally into a single platform with live pricing, 1-click deployment, and full root access. Developers and teams rent H100, B200, B300, A100, H200, GH200, L40S, RTX PRO 6000, RTX 5090, and RTX 4090 GPUs for AI training, inference, and research at a fraction of hyperscaler costs.

Spheron is not a single cloud provider. It is a marketplace that aggregates GPU supply from multiple enterprise-grade data center partners. Prices fluctuate based on real-time availability. All infrastructure comes from Tier 2/3/4 compliant facilities (not consumer hardware). Customers get VM or bare metal options, per-minute billing, SSH root access, and zero vendor lock-in through a single account.

Live GPU pricing: https://www.spheron.network/pricing/
Launch a GPU instance: https://app.spheron.ai

## Core Pages

- [Homepage](https://www.spheron.network/): Platform overview, live GPU pricing table, cost comparison vs hyperscalers, compliance info, and getting started
- [GPU Rental Catalog](https://www.spheron.network/gpu-rental/): Browse all available NVIDIA GPU models with specs, pricing, and 1-click rent
- [Pricing](https://www.spheron.network/pricing/): Live marketplace pricing for every GPU model, updated in real time
- [Blog](https://www.spheron.network/blog/): Technical guides, GPU benchmarks, deployment tutorials, cost analysis, and product updates
- [Documentation](https://docs.spheron.ai): Deployment guides, API reference, SSH setup, framework tutorials, and troubleshooting
- [API Reference](https://docs.spheron.ai/api-reference): REST API for programmatic GPU provisioning, instance management, and billing
- [GPU Cloud Dashboard](https://app.spheron.ai): Self-service platform to browse, deploy, and manage GPU instances
- [Contact / Enterprise](https://www.spheron.network/contact/): Reach the Spheron team for enterprise inquiries and support
- [Partners](https://www.spheron.network/partner/): Data center and ecosystem partner information
- [Privacy Policy](https://www.spheron.network/privacy/): Data handling and privacy practices
- [Book Enterprise Consultation](https://meetings-eu1.hubspot.com/prashant-maurya): For bulk GPU needs (100+ GPUs), custom sourcing, and dedicated support

## Feature Pages

Each page covers billing model details, use cases, and how to get started.

- [On-Demand GPU Instances](https://www.spheron.network/features/on-demand-instances/): Per-minute billing with no contracts or minimums. Deploy H100, A100, B200, and other NVIDIA GPUs in under 60 seconds
- [Spot GPU Instances](https://www.spheron.network/features/spot-instances/): Rent NVIDIA GPUs at up to 50% off with spot pricing. Built for batch jobs, training experiments, and flexible workloads
- [Reserved GPU Commitments](https://www.spheron.network/features/reserved-commitments/): Volume pricing with guaranteed availability. Custom clusters from 8 to 512+ GPUs with InfiniBand and dedicated setup

## GPU Rental Pages

Each page covers specs, live pricing, use cases, provider comparison, and a direct rent button.

- [NVIDIA H100 80GB HBM3](https://www.spheron.network/gpu-rental/h100/): Hopper architecture, 3.35 TB/s bandwidth, InfiniBand available. Best for LLM training 175B+ and large-scale inference
- [NVIDIA B200 192GB HBM3e](https://www.spheron.network/gpu-rental/b200/): Blackwell architecture, 8 TB/s bandwidth, NVLink 1.8TB/s. For trillion-parameter models and next-gen LLMs
- [NVIDIA B300 288GB HBM3e](https://www.spheron.network/gpu-rental/b300/): Blackwell Ultra, 10 TB/s bandwidth. Highest memory capacity for the most demanding training workloads
- [NVIDIA H200 141GB HBM3e](https://www.spheron.network/gpu-rental/h200/): Enhanced Hopper, 4.8 TB/s bandwidth. Optimized for LLM inference, RAG systems, and long context windows
- [NVIDIA GH200 96GB HBM3](https://www.spheron.network/gpu-rental/gh200/): Grace-Hopper superchip, up to 432GB system RAM. For memory-intensive AI and HPC workloads
- [NVIDIA A100 80GB HBM2e](https://www.spheron.network/gpu-rental/a100/): Ampere architecture, proven workhorse for training up to 20B parameters and cost-effective inference
- [NVIDIA L40S 48GB GDDR6](https://www.spheron.network/gpu-rental/l40s/): Ada Lovelace, excellent price/performance for inference serving and mixed graphics/compute
- [NVIDIA RTX PRO 6000 96GB GDDR7](https://www.spheron.network/gpu-rental/rtx-pro-6000/): Blackwell professional GPU for AI development, fine-tuning, and rendering
- [NVIDIA RTX 5090 32GB GDDR7](https://www.spheron.network/gpu-rental/rtx-5090/): Latest consumer flagship for AI experimentation and cost-effective development
- [NVIDIA RTX 4090 24GB GDDR6X](https://www.spheron.network/gpu-rental/rtx-4090/): Most affordable GPU for fine-tuning, prototyping, and small-to-medium model training

## Guides: GPU Selection & Pricing

- [GPU Cloud Pricing Comparison 2026](https://www.spheron.network/blog/gpu-cloud-pricing-comparison-2026/): H100, H200, B200, A100, L40S pricing across 15+ providers with hidden cost analysis
- [Best NVIDIA GPUs for LLMs in 2026](https://www.spheron.network/blog/best-nvidia-gpus-for-llms/): B300, B200, H200, H100, RTX 5090 ranked by use case with VRAM needs and benchmarks
- [GPU Cloud Benchmarks 2026](https://www.spheron.network/blog/gpu-cloud-benchmarks/): Side-by-side pricing, specs, and inference throughput from 10+ providers
- [GPU Memory Requirements for LLMs](https://www.spheron.network/blog/gpu-memory-requirements-llm/): VRAM calculator for models from 7B to 685B covering weights, KV cache, and quantization
- [GPU Requirements Cheat Sheet 2026](https://www.spheron.network/blog/gpu-requirements-cheat-sheet-2026/): Quick reference for matching workloads to the right GPU
- [GPU Cost Optimization Playbook](https://www.spheron.network/blog/gpu-cost-optimization-playbook/): Strategies to cut AI compute bills by 60% through instance selection, spot pricing, and idle elimination
- [AI Buyer's Guide](https://www.spheron.network/blog/ai-buyers-guide/): How to evaluate GPU providers beyond raw specs
- [Top 10 Cloud GPU Providers](https://www.spheron.network/blog/top-10-cloud-gpu-providers/): Comparison of major GPU platforms on performance, pricing, and control
- [Best GPU for AI Inference in 2026](https://www.spheron.network/blog/best-gpu-for-ai-inference-2026/): L40S vs H100 vs H200 vs B200 benchmarks, tokens/sec/dollar, and workload-based decision guide
- [MLPerf Inference v6.0 Results Explained](https://www.spheron.network/blog/mlperf-inference-v6-benchmark-results-2026/): What MLPerf v6.0 scores mean for GPU cloud users choosing between H200, B200, and MI355X

## Guides: GPU Deep Dives

- [NVIDIA B200 Complete Guide](https://www.spheron.network/blog/nvidia-b200-complete-guide/): Specs, benchmarks, cloud pricing, and H100 upgrade path
- [NVIDIA B300 Blackwell Ultra Guide](https://www.spheron.network/blog/nvidia-b300-blackwell-ultra-guide/): Architecture, specs, pricing, and benchmark data
- [NVIDIA GH200 Guide](https://www.spheron.network/blog/nvidia-gh200-guide/): Grace-Hopper superchip architecture and performance analysis
- [NVIDIA H100 vs H200](https://www.spheron.network/blog/nvidia-h100-vs-h200/): Benchmarks, specs, and performance comparison for AI inference
- [NVIDIA H200 vs B200 vs GB200](https://www.spheron.network/blog/nvidia-h200-vs-b200-vs-gb200/): Generation-over-generation comparison
- [NVIDIA L40S for AI Inference](https://www.spheron.network/blog/nvidia-l40s-for-ai-inference/): Specs, benchmarks, and pricing for inference workloads
- [RTX 4090 for AI/ML](https://www.spheron.network/blog/rtx-4090-for-ai-ml/): Benchmarks, specs, and pricing for development workloads
- [RTX 5090 vs H100 vs B200](https://www.spheron.network/blog/rtx-5090-vs-h100-vs-b200/): Cross-tier GPU comparison for different budgets
- [NVIDIA Rubin R100 Guide](https://www.spheron.network/blog/nvidia-rubin-r100-guide/): Next-gen architecture overview and what it means for GPU cloud
- [NVIDIA Rubin CPX Explained](https://www.spheron.network/blog/nvidia-rubin-cpx-long-context-inference/): Covers what NVIDIA's Rubin CPX GPU was, why it was replaced by Groq 3 LPX at GTC 2026, and the current hardware hierarchy for long-context inference.
- [NVIDIA A100 vs V100](https://www.spheron.network/blog/nvidia-a100-vs-v100/): Ampere vs Volta architecture, VRAM, Tensor Cores, MIG support, and cloud pricing comparison
- [NVIDIA GB200 NVL72 Guide](https://www.spheron.network/blog/nvidia-gb200-nvl72-guide/): 72 B200 GPUs, 13.4 TB unified memory, 1.44 exaflops per rack, and when rack-scale beats 8xB200
- [NVIDIA Groq 3 LPU Explained](https://www.spheron.network/blog/nvidia-groq-3-lpu-explained/): Non-GPU inference chip architecture, 150 TB/s SRAM bandwidth, LPU vs GPU comparison
- [Rubin vs Blackwell vs Hopper](https://www.spheron.network/blog/nvidia-rubin-vs-blackwell-vs-hopper/): Full specs, HBM evolution, NVLink generations, and workload-based guidance for 2026
- [AMD MI400 vs NVIDIA B300](https://www.spheron.network/blog/amd-mi400-vs-nvidia-b300/): CDNA 5 vs Blackwell Ultra specs, LLM inference projections, ROCm vs CUDA, and GPU cloud pricing

## Guides: LLM Training & Fine-Tuning

- [How to Fine-Tune LLMs in 2026](https://www.spheron.network/blog/how-to-fine-tune-llm-2026/): Costs, GPU requirements, and step-by-step workflows for Llama, Qwen, DeepSeek
- [Multi-Node GPU Training Without InfiniBand](https://www.spheron.network/blog/multi-node-gpu-training-without-infiniband/): Tradeoffs and cost analysis for distributed training
- [Axolotl vs Unsloth vs Torchtune](https://www.spheron.network/blog/axolotl-vs-unsloth-vs-torchtune/): Fine-tuning framework comparison
- [LoRA Multi-Adapter Serving](https://www.spheron.network/blog/lora-multi-adapter-serving-gpu-cloud/): Serve multiple LoRA adapters on GPU cloud
- [Spot GPU Training Case Study](https://www.spheron.network/blog/spot-gpu-training-case-study/): How a 12-person AI startup trained a 70B model for $11,200 using spot GPUs
- [Fine-Tuning at Scale Case Study](https://www.spheron.network/blog/fine-tuning-scale-case-study/): Real-world fine-tuning infrastructure patterns

## Guides: LLM Inference & Deployment

- [LLM Deployment Guide](https://www.spheron.network/blog/llm-deployment-guide/): From prototype to production in 5 phases with real cost numbers
- [vLLM Production Deployment 2026](https://www.spheron.network/blog/vllm-production-deployment-2026/): Multi-GPU tensor parallelism, FP8, load balancing on bare metal
- [SGLang Production Deployment Guide](https://www.spheron.network/blog/sglang-production-deployment-guide/): Alternative serving engine setup and benchmarks
- [vLLM vs TensorRT-LLM vs SGLang Benchmarks](https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks/): Head-to-head inference engine comparison
- [Inference Engineering Guide 2026](https://www.spheron.network/blog/inference-engineering-guide-2026/): What inference engineering is and how GPU cloud fits in
- [Speculative Decoding Production Guide](https://www.spheron.network/blog/speculative-decoding-production-guide/): Speed up inference with speculative decoding on GPU cloud
- [Continuous Batching & Paged Attention](https://www.spheron.network/blog/llm-serving-optimization-continuous-batching-paged-attention/): Key serving optimizations explained
- [KV Cache Optimization Guide](https://www.spheron.network/blog/kv-cache-optimization-guide/): Memory management for large context inference
- [Prefill-Decode Disaggregation](https://www.spheron.network/blog/prefill-decode-disaggregation-gpu-cloud/): Separating prefill and decode for cost-efficient inference
- [Ollama vs vLLM](https://www.spheron.network/blog/ollama-vs-vllm/): When to use each for local and cloud inference
- [OpenAI-Compatible API Self-Hosted](https://www.spheron.network/blog/openai-compatible-api-self-hosted/): Host your own OpenAI-compatible endpoint on GPU cloud
- [Deploy NVIDIA Triton Inference Server on GPU Cloud (2026)](https://www.spheron.network/blog/triton-inference-server-deployment-guide/): Step-by-step guide to deploying Triton with Docker, model repository setup, vLLM backend, dynamic batching, and a Triton vs vLLM vs TensorRT-LLM decision matrix.
- [Self-Host AI Coding Assistant on GPU Cloud](https://www.spheron.network/blog/self-host-ai-coding-assistant-gpu-cloud/): Deploy Tabby or Continue with Qwen2.5-Coder on Spheron for private, self-hosted code autocomplete
- [Why Your LLM Inference Is Slow (And How to Fix It)](https://www.spheron.network/blog/llm-inference-slow/): Seven common causes with fixes: VRAM spillover, no KV cache, FP16 overhead, static batching, and more
- [Inference-Time Compute Scaling on GPU Cloud](https://www.spheron.network/blog/inference-time-compute-scaling-gpu-cloud/): How reasoning models spend more GPU per query for better answers, with GPU sizing and cost control
- [llm-d on Kubernetes: Disaggregated LLM Inference](https://www.spheron.network/blog/llm-d-kubernetes-disaggregated-inference-guide/): CNCF Sandbox project for prefill/decode disaggregation on Kubernetes with H100/B200 configs

## Guides: Model Deployment Tutorials

- [Deploy DeepSeek V4](https://www.spheron.network/blog/deploy-deepseek-v4-gpu-cloud/): Step-by-step GPU cloud deployment
- [Deploy Llama 4](https://www.spheron.network/blog/deploy-llama-4-gpu-cloud/): GPU requirements and deployment walkthrough
- [Deploy Qwen 3](https://www.spheron.network/blog/deploy-qwen3-gpu-cloud/): Setup guide for Qwen 3 on GPU cloud
- [Deploy Gemma 4](https://www.spheron.network/blog/deploy-gemma-4-gpu-cloud/): Google's open model on GPU cloud
- [Deploy Vision-Language Models](https://www.spheron.network/blog/deploy-vision-language-models-gpu-cloud/): Multi-modal model deployment guide
- [Deploy WAN 2.1 AI Video Generation](https://www.spheron.network/blog/deploy-wan-2-1-ai-video-generation-gpu-setup/): Video generation model setup
- [NVIDIA NIM Self-Host Guide](https://www.spheron.network/blog/nvidia-nim-self-host-deployment-guide/): Deploy NVIDIA NIM containers on Spheron
- [Deploy DeepSeek R2](https://www.spheron.network/blog/deploy-deepseek-r2-gpu-cloud/): Self-host the open-source MoE reasoning model with vLLM, FP8 quantization, and H100/H200/B200 benchmarks
- [Deploy DeepSeek V3.2 Speciale](https://www.spheron.network/blog/deploy-deepseek-v3-2-speciale/): Hardware requirements and vLLM setup for the top-tier open-source reasoning model
- [Deploy Gemma 3](https://www.spheron.network/blog/deploy-gemma-3-gpu-cloud/): Run Gemma 3 (1B-27B) with vLLM or Ollama, GPU requirements and cost breakdown
- [Deploy GLM-5.1](https://www.spheron.network/blog/deploy-glm-5-1-gpu-cloud/): Self-host the 754B MoE model with vLLM and SGLang, GPU configs and benchmarks
- [Deploy GPT-OSS](https://www.spheron.network/blog/deploy-gpt-oss-gpu-cloud/): Self-host OpenAI's first open-source model (20B and 120B MoE) with vLLM and SGLang
- [Deploy MiMo-V2-Flash](https://www.spheron.network/blog/deploy-mimo-v2-flash-gpu-cloud/): Xiaomi's 309B MoE model with vLLM, expert parallelism, and hybrid thinking mode
- [Deploy Open-Source TTS on GPU Cloud](https://www.spheron.network/blog/deploy-open-source-tts-gpu-cloud-2026/): Kokoro, Fish Speech, and Hume TADA deployment with GPU requirements and cost analysis
- [Deploy Qwen 3.5](https://www.spheron.network/blog/deploy-qwen-3-5-gpu-cloud/): 397B MoE model VRAM requirements and vLLM setup for every model size
- [Deploy Qwen 3.6 Plus](https://www.spheron.network/blog/deploy-qwen-3-6-plus-gpu-cloud/): Hybrid MoE with 1M context, VRAM requirements and vLLM setup
- [Deploy Qwen3.5-Omni](https://www.spheron.network/blog/deploy-qwen3-5-omni-gpu-cloud/): Self-host real-time multimodal AI (text, audio, video) with GPU sizing and vLLM setup
- [Deploy Nemotron 3 Super](https://www.spheron.network/blog/nemotron-3-super-deployment-guide/): NVIDIA's hybrid Mamba-Transformer MoE on H100 or B200 with vLLM config and quantization tradeoffs
- [Deploy NeuTTS Air](https://www.spheron.network/blog/neutts-air-spheron-voice-ai/): Ultra-realistic on-device voice AI with instant voice cloning, architecture and benchmarks
- [Deploy NVIDIA Cosmos for Synthetic Data Generation](https://www.spheron.network/blog/deploy-nvidia-cosmos-gpu-cloud-synthetic-data/): Deploy NVIDIA Cosmos world foundation models on GPU cloud for physical AI synthetic training data generation

## Guides: Kernel Development

- [CUDA 13 Tile Programming on GPU Cloud (2026)](https://www.spheron.network/blog/cuda-13-tile-programming-gpu-cloud/): Write custom GPU kernels with CUDA Tile and the cuTile Python DSL on A100 and B300 SXM6 bare-metal instances

## Guides: Infrastructure & Architecture

- [Production GPU Cloud Architecture](https://www.spheron.network/blog/production-gpu-cloud-architecture/): Failover, monitoring, and reliability patterns for marketplace GPU clouds
- [Kubernetes GPU Orchestration 2026](https://www.spheron.network/blog/kubernetes-gpu-orchestration-2026/): DRA, KAI Scheduler, and Grove setup
- [Migrate from AWS/GCP/Azure](https://www.spheron.network/blog/migrate-from-aws-gcp-azure/): Step-by-step migration to alternative GPU clouds
- [Serverless vs On-Demand vs Reserved GPU](https://www.spheron.network/blog/serverless-gpu-vs-on-demand-vs-reserved/): Choosing the right GPU billing model
- [Dedicated vs Shared GPU Memory](https://www.spheron.network/blog/dedicated-vs-shared-gpu-memory/): When to use each allocation approach
- [GPU Monitoring for ML](https://www.spheron.network/blog/gpu-monitoring-for-ml/): Tracking utilization, thermals, and cost efficiency
- [100 Concurrent AI Agents Case Study](https://www.spheron.network/blog/100-concurrent-ai-agents-case-study/): Running agent infrastructure at scale on GPU cloud
- [MCP Server GPU Deployment](https://www.spheron.network/blog/mcp-server-gpu-deployment/): Deploy MCP servers on dedicated GPU instances
- [Agentic RAG on GPU Cloud](https://www.spheron.network/blog/agentic-rag-gpu-infrastructure-guide/): Deploy embedding, vector search, and LLM on one stack with sub-200ms TTFT
- [NVIDIA OpenShell and Agent Toolkit](https://www.spheron.network/blog/nvidia-openshell-agent-toolkit-gpu-cloud-guide/): Deploy secure agentic AI with NemoClaw, seccomp sandboxing, and H100/B200 GPU sizing
- [AI's Memory Wall Problem](https://www.spheron.network/blog/ai-memory-wall-inference-latency-guide-2026/): Why more GPUs don't fix inference latency when you're memory-bound, and how to fix it

## Guides: Cost & Provider Comparisons

- [AWS/GCP/Azure GPU Alternative](https://www.spheron.network/blog/aws-gcp-azure-gpu-alternative/): Why teams are leaving hyperscalers for GPU marketplaces
- [Avoid Unexpected AWS GPU Costs](https://www.spheron.network/blog/avoid-unexpected-aws-costs/): Hidden fees and how to eliminate them
- [AI Inference Cost Economics 2026](https://www.spheron.network/blog/ai-inference-cost-economics-2026/): Unit economics of inference at scale
- [GPU Cloud for Startups 2026](https://www.spheron.network/blog/gpu-cloud-startups-2026/): How early-stage teams should approach GPU infrastructure
- [How Renting GPUs Cuts Training Costs](https://www.spheron.network/blog/renting-gpus/): Rental vs on-prem cost comparison over 3 years
- [Spheron vs RunPod](https://www.spheron.network/blog/spheron-vs-runpod/): Feature and pricing comparison
- [Spheron vs Vast.ai](https://www.spheron.network/blog/spheron-vs-vastai/): Full VM access and pricing comparison
- [Spheron vs CoreWeave](https://www.spheron.network/blog/spheron-vs-coreweave/): Cost and flexibility comparison
- [Spheron vs Lambda Labs](https://www.spheron.network/blog/lambda-labs-alternatives/): Alternative provider analysis
- [RunPod Alternatives](https://www.spheron.network/blog/runpod-alternatives/): Comparing the top RunPod competitors
- [CoreWeave Alternatives](https://www.spheron.network/blog/coreweave-alternatives/): Options beyond CoreWeave for GPU cloud
- [AWS Outages and Neo Clouds](https://www.spheron.network/blog/aws-outages-neo-clouds/): How the October 2025 AWS outage impacted AI teams and why neo cloud GPU providers offer more resilient alternatives

## Platform Details

### What Spheron Offers

Spheron is a GPU compute marketplace. You browse live pricing from multiple data center partners, pick a GPU configuration, and deploy in 60-90 seconds. Every instance comes with full SSH root access and a dedicated IP.

**GPU options:** NVIDIA H100, B200, B300, H200, GH200, A100, L40S, RTX PRO 6000, RTX 5090, RTX 4090.

**Instance types:** Virtual machines (quick provisioning, cost-efficient) or bare metal servers (zero hypervisor overhead, maximum performance). Multi-GPU configs up to 8x per node. Cluster deployments up to 80+ GPUs with InfiniBand (400 Gb/s) for distributed training.

**Billing:** Per-minute granularity, no minimum commitment, no long-term contracts. Pay-as-you-go with credit card, bank transfer, or crypto (USDT, USDC, ETH).

**Pre-configured templates:** PyTorch, TensorFlow, CUDA, JAX, Jupyter, NVIDIA Container Toolkit, and custom Docker images.

**Compliance:** All data centers are Tier 2/3/4 compliant. HIPAA, ISO 27001, SOC 2 Type I/II certifications available. 99.9% uptime SLA.

**Networking:** InfiniBand (400 Gb/s) and NVLink available on select providers for multi-node distributed training. Dedicated IPs for every instance.

### Who Uses Spheron

AI startups building LLM products. ML engineers and data scientists running training and inference. Enterprise teams deploying production AI platforms on compliant infrastructure. Research institutions and academic labs needing access to H100/B200 without capital investment. Generative AI developers working with Stable Diffusion, ComfyUI, and video generation. AI agent developers running distributed inference infrastructure.

### How Spheron Compares to Hyperscalers

Spheron aggregates supply from multiple data center partners, creating a competitive marketplace where prices reflect real supply and demand. This typically results in significantly lower per-hour GPU costs compared to AWS, Google Cloud, and Azure. Unlike hyperscalers, Spheron offers no vendor lock-in (switch providers through one platform), bare metal options alongside VMs, and per-minute billing without reserved instance requirements.

### Supported Frameworks & Tools

PyTorch (2.x with CUDA 12.1+), TensorFlow 2.x, JAX, Hugging Face Transformers, DeepSpeed, Megatron-LM, NVIDIA Triton Inference Server, vLLM, SGLang, RAPIDS (cuDF, cuML, cuGraph), ONNX Runtime, Docker, Kubernetes with NVIDIA Container Toolkit.

## FAQ

- **Is it VM or bare metal?** Both. Choose VMs for quick provisioning or bare metal for maximum performance. Switch between them from the dashboard.
- **Do I get a dedicated IP?** Yes. Every instance includes a dedicated IP with full SSH root access.
- **Can I run containers?** Yes. Full root access with Docker and Kubernetes support. NVIDIA Container Toolkit is pre-installed.
- **Is InfiniBand supported?** On select H100 providers. 400 Gb/s InfiniBand with GPUDirect RDMA. Availability shown in the dashboard before you deploy.
- **What uptime can I expect?** 99.9% availability SLA from Tier 3/4 data centers with redundant power, cooling, and networking.
- **How fast is deployment?** 60-90 seconds for H100, 45-75 seconds for A100. Pre-warmed infrastructure with 1-click deployment.
- **Is there a minimum rental period?** No. Per-minute billing, no contracts, no minimum spend.
- **Multi-GPU support?** Up to 8x GPUs per node with NVLink. Bare metal clusters up to 80+ GPUs with InfiniBand for distributed training.
- **Spot instances?** Yes. Up to 70% savings for fault-tolerant workloads. Best for training jobs with checkpointing.
- **What regions?** US, Europe, and Canada with ongoing expansion.
- **Crypto payments?** Yes. USDT, USDC, and ETH accepted alongside credit card and bank transfer.

## Contact & Community

- [Launch GPU Instance](https://app.spheron.ai): Sign up and deploy in under 2 minutes
- [Enterprise Consultation](https://meetings-eu1.hubspot.com/prashant-maurya): For 100+ GPU deployments, custom sourcing, and dedicated support
- [Discord Community](https://sphn.wiki/discord): Technical support, peer discussions, and platform announcements
- [Twitter/X @SpheronAI](https://twitter.com/spheronai): Product updates and GPU availability announcements
- [LinkedIn](https://linkedin.com/company/spheronai): Company news and business updates
- [GitHub](https://github.com/spheron-core): Open-source tools and SDK libraries
- [Email](mailto:info@spheron.ai): General inquiries and partnership opportunities

## Optional

- [FP4 Quantization on Blackwell](https://www.spheron.network/blog/fp4-quantization-blackwell-gpu-cost/): Cost implications of FP4 on B200/B300
- [AWQ Quantization for LLM Deployment](https://www.spheron.network/blog/awq-quantization-guide-llm-deployment/): Practical quantization guide
- [MoE Inference Optimization](https://www.spheron.network/blog/moe-inference-optimization-gpu-cloud/): Mixture-of-experts serving on GPU cloud
- [Model Distillation on GPU Cloud](https://www.spheron.network/blog/model-distillation-gpu-cloud-7b-student-70b-teacher/): 7B student from 70B teacher workflow
- [Fractional GPU Inference](https://www.spheron.network/blog/fractional-gpu-inference-vgpu-mps-right-sizing/): vGPU, MPS, and right-sizing for inference
- [Run Multiple LLMs on One GPU](https://www.spheron.network/blog/run-multiple-llms-one-gpu-mig-time-slicing-guide/): MIG and time-slicing guide
- [NVIDIA Dynamo Disaggregated Inference](https://www.spheron.network/blog/nvidia-dynamo-disaggregated-inference-guide/): NVIDIA's inference disaggregation framework
- [NVIDIA NixL Disaggregated Inference](https://www.spheron.network/blog/nvidia-nixl-disaggregated-inference-guide/): NixL for distributed inference
- [DeepSeek vs Llama 4 vs Qwen 3](https://www.spheron.network/blog/deepseek-vs-llama-4-vs-qwen3/): Open model comparison
- [Kimi K2.5 Guide](https://www.spheron.network/blog/kimi-k2-5-guide/): Moonshot AI's model overview
- [ROCm vs CUDA 2026](https://www.spheron.network/blog/rocm-vs-cuda-gpu-cloud-2026/): AMD vs NVIDIA software stack comparison
- [AMD MI300X vs NVIDIA H200](https://www.spheron.network/blog/amd-mi300x-vs-nvidia-h200/): Cross-vendor GPU comparison
- [AMD MI350X vs NVIDIA B200](https://www.spheron.network/blog/amd-mi350x-vs-nvidia-b200/): Next-gen cross-vendor comparison
- [PyTorch vs TensorFlow](https://www.spheron.network/blog/pytorch-vs-tensorflow/): Framework decision guide
- [GPU Shortage 2026](https://www.spheron.network/blog/gpu-shortage-2026/): Supply dynamics and how marketplaces help
- [ComfyUI on GPU Cloud 2026](https://www.spheron.network/blog/comfyui-gpu-cloud-2026/): Running ComfyUI workflows on rented GPUs
- [AI Video Generation GPU Guide](https://www.spheron.network/blog/ai-video-generation-gpu-guide/): GPU requirements for video AI
- [Voice AI GPU Infrastructure](https://www.spheron.network/blog/voice-ai-gpu-infrastructure/): GPU needs for real-time voice AI
- [GPU Infrastructure for AI Coding Tools](https://www.spheron.network/blog/gpu-infrastructure-ai-coding-tools-2026/): Powering AI code assistants
- [GPU Infrastructure for AI Agents](https://www.spheron.network/blog/gpu-infrastructure-ai-agents-2026/): Scaling agent compute
- [Structured Output & Function Calling Guide](https://www.spheron.network/blog/structured-output-function-calling-inference-guide/): Reliable structured output from LLMs
- [RAG Pipeline on Bare Metal Case Study](https://www.spheron.network/blog/rag-pipeline-bare-metal-case-study/): Production RAG infrastructure patterns
- [Reasoning Model Inference Cost Optimization](https://www.spheron.network/blog/reasoning-model-inference-cost-gpu-optimization/): Cutting costs for reasoning-heavy models
- [NVMe KV Cache Offloading](https://www.spheron.network/blog/nvme-kv-cache-offloading-llm-inference/): Extending context with NVMe offloading
- [Hybrid Cloud & Edge AI Inference](https://www.spheron.network/blog/hybrid-cloud-edge-ai-inference-guide/): Combining cloud and edge GPU deployment
- [LLM Inference Router](https://www.spheron.network/blog/llm-inference-router-gpu-cloud/): Routing inference requests across GPU instances
- [Multi-Agent AI System GPU Infrastructure](https://www.spheron.network/blog/multi-agent-ai-system-gpu-infrastructure/): Scaling multi-agent systems on GPU cloud
- [Nebius Alternatives](https://www.spheron.network/blog/nebius-alternatives/): GPU cloud alternatives to Nebius
- [Hyperstack Alternatives](https://www.spheron.network/blog/hyperstack-alternatives/): GPU cloud alternatives to Hyperstack
- [Shadeform Alternatives](https://www.spheron.network/blog/shadeform-alternatives/): GPU cloud alternatives to Shadeform
- [Modal Alternatives](https://www.spheron.network/blog/modal-alternatives/): GPU cloud alternatives to Modal
- [Lambda Labs Alternatives](https://www.spheron.network/blog/lambda-labs-alternatives/): GPU cloud alternatives to Lambda
- [Vast.ai Alternatives](https://www.spheron.network/blog/vastai-alternatives/): GPU cloud alternatives to Vast.ai
- [FluidStack Alternatives](https://www.spheron.network/blog/fluidstack-alternatives/): GPU cloud alternatives to FluidStack
- [Paperspace Alternatives](https://www.spheron.network/blog/paperspace-alternatives/): GPU cloud alternatives to Paperspace
- [Latitude Alternatives](https://www.spheron.network/blog/latitude-alternatives/): GPU cloud alternatives to Latitude
- [DataCrunch/Verda Alternatives](https://www.spheron.network/blog/datacrunch-verda-alternatives/): GPU cloud alternatives to DataCrunch and Verda
- [Spheron vs Hyperstack](https://www.spheron.network/blog/spheron-vs-hyperstack/): Direct comparison
- [Spheron vs Nebius](https://www.spheron.network/blog/spheron-vs-nebius/): Direct comparison
- [Spheron vs Modal](https://www.spheron.network/blog/spheron-vs-modal/): Direct comparison
- [Spheron vs SF Compute](https://www.spheron.network/blog/spheron-vs-sf-compute/): Direct comparison
- [Spheron vs Shadeform](https://www.spheron.network/blog/spheron-vs-shadeform/): Direct comparison
- [Top GPU Providers South Korea](https://www.spheron.network/blog/top-gpu-providers-south-korea/): Regional GPU cloud guide
- [Top GPU Rental Marketplaces](https://www.spheron.network/blog/top-gpu-rental/): GPU rental market overview and how platforms like Spheron reshape access to compute
- [GPU Capacity for AI Deployment](https://www.spheron.network/blog/gpu-capacity-for-ai-deployment/): How to plan, source, and optimize GPU infrastructure for AI training
- [GPU Cloud for Video AI 2026](https://www.spheron.network/blog/gpu-cloud-video-ai-2026/): VRAM requirements for Wan 2.1, HunyuanVideo, and AnimateDiff with setup guides
- [Run Karpathy's autoresearch on GPU Cloud](https://www.spheron.network/blog/karpathy-autoresearch-spheron-gpu/): Set up Andrej Karpathy's autonomous LLM training agent on Spheron in under 10 minutes
- [Run LLMs Locally with Ollama](https://www.spheron.network/blog/run-llms-locally-ollama/): GPU-accelerated local LLM setup covering installation, model selection, quantization, and API integration
- [GGUF Dynamic Quantization on GPU Cloud](https://www.spheron.network/blog/gguf-dynamic-quantization-gpu-cloud/): Deploy LLMs 50% cheaper with Unsloth Dynamic 2.0 and llama.cpp server
- [Google TurboQuant for LLM Inference](https://www.spheron.network/blog/google-turboquant-llm-compression-gpu-cloud/): 6x KV cache compression and 8x attention acceleration using PolarQuant and QJL
- [Rent NVIDIA A100 GPUs](https://www.spheron.network/blog/rent-nvidia-a100-gpus/): A100 80GB rental at $0.76/hour with bare-metal performance and instant provisioning
- [Rent NVIDIA H200 GPUs](https://www.spheron.network/blog/rent-nvidia-h200-gpus/): H200 141GB HBM3e rental with bare-metal access and pay-as-you-go pricing
- [Rent NVIDIA RTX 5090](https://www.spheron.network/blog/rent-nvidia-rtx-5090/): Real-world LLM throughput benchmarks, cost per million tokens, and VRAM guide
- [Rent NVIDIA RTX PRO 6000](https://www.spheron.network/blog/rent-nvidia-rtx-pro-6000/): 96GB GDDR7 benchmarks, 30B AWQ throughput, and cost per million tokens vs alternatives