# Spheron > Spheron is an enterprise GPU rental marketplace. We aggregate NVIDIA and AMD 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, RTX 4090, and AMD MI300X GPUs on demand, reserve GB200 and GB300 capacity, and pre-order NVIDIA Vera Rubin, 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 Every blog post with its summary: https://www.spheron.network/llms-full.txt Markdown of any page: request its URL with `Accept: text/markdown` ## 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 and AMD 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 Offers OpenAPI Spec](https://www.spheron.network/openapi.json): OpenAPI 3.1 description of the public, unauthenticated live GPU offers endpoint - [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 - [Supply GPUs to Spheron](https://www.spheron.network/partner/): GPU supplier program. Data center operators and neoclouds list H100, H200, B200, and B300 capacity, receive vetted buyer demand, and keep their own pricing and contracts - [Rent GPU for Gonka.ai](https://www.spheron.network/partner/gonka/): H100, H200, and B200 bare metal servers for Gonka.ai network participation, with stables payment support (USDT, USDC) - [Customer Stories](https://www.spheron.network/customers/): Case studies of AI teams sourcing NVIDIA GPU capacity through Spheron - [Mitrasish Mukherjee, Co-founder](https://www.spheron.network/authors/mitrasish/): Author profile and post archive covering GPU clouds, AI infrastructure, and large-scale training and inference - [Terms of Use](https://www.spheron.network/terms/): Terms governing use of the Spheron GPU compute marketplace - [Privacy Policy](https://www.spheron.network/privacy/): Data handling and privacy practices ## 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, 3.35 TB/s bandwidth, InfiniBand available. LLM training and large-scale inference - [NVIDIA H200 141GB HBM3e](https://www.spheron.network/gpu-rental/h200/): Hopper, 4.8 TB/s bandwidth. LLM inference, RAG, and long context windows - [NVIDIA B200 192GB HBM3e](https://www.spheron.network/gpu-rental/b200/): Blackwell, 8 TB/s bandwidth, NVLink 1.8 TB/s. Trillion-parameter models and next-gen LLMs - [NVIDIA B300 288GB HBM3e](https://www.spheron.network/gpu-rental/b300/): Blackwell Ultra, 288GB per GPU for the most memory-hungry training and inference - [NVIDIA GB200 192GB HBM3e](https://www.spheron.network/gpu-rental/gb200/): Grace Blackwell, NVLink 5, Grace CPU coupling. Reservation only, any quantity - [NVIDIA GB300 288GB HBM3e](https://www.spheron.network/gpu-rental/gb300/): Grace Blackwell Ultra, NVLink 5. Reservation only, for trillion-parameter training and frontier inference - [NVIDIA Vera Rubin 288GB HBM4 (Pre-Order)](https://www.spheron.network/gpu-rental/vera-rubin/): Rubin GPU (also called R100, R200 or H300), 22 TB/s bandwidth, NVLink 6. Pre-order open now. NVIDIA's first cloud cohort gets capacity from H2 2026; broader Spheron availability is targeted for 2027 - [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 - [NVIDIA A100 80GB HBM2e](https://www.spheron.network/gpu-rental/a100/): Ampere workhorse for fine-tuning and cost-effective inference - [NVIDIA L40S 48GB GDDR6](https://www.spheron.network/gpu-rental/l40s/): Ada Lovelace, 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/): Consumer Blackwell flagship for experimentation and development - [NVIDIA RTX 4090 24GB GDDR6X](https://www.spheron.network/gpu-rental/rtx-4090/): Lowest-cost option for fine-tuning, prototyping, and small-model training - [AMD MI300X 192GB HBM3](https://www.spheron.network/gpu-rental/mi300x/): AMD Instinct on ROCm, 192GB HBM3 per GPU, 1x and 2x instances - [AMD GPU Cloud](https://www.spheron.network/amd-gpu-cloud/): Which AMD GPUs are rentable on Spheron, the ROCm stack and what still needs CUDA, CUDA-to-ROCm migration guides, and MI300X rates against AMD Developer Cloud, Azure and Oracle ## GPU Server Pages Dedicated multi-GPU servers (1x, 2x, 4x and 8x nodes) with the live price of each node, form factors and interconnect. - [GPU Servers](https://www.spheron.network/gpu-servers/): Dedicated H100, H200 and A100 servers, live 1x and 8x node prices, and dedicated vs GPU VPS vs bare metal - [NVIDIA H100 Server](https://www.spheron.network/gpu-servers/h100/): 1x to 8x H100 SXM5, NVL and PCIe nodes with live node prices and NVLink details - [NVIDIA H200 Server](https://www.spheron.network/gpu-servers/h200/): 1x to 8x H200 SXM5 nodes, 141GB HBM3e per GPU, with live node prices - [NVIDIA A100 Server](https://www.spheron.network/gpu-servers/a100/): 1x to 8x A100 80GB SXM4 and PCIe nodes with live node prices ## GPU Price Pages List prices for GPUs Spheron has no rental page for, each dated and sourced, with specs, price history and the closest GPU Spheron rents at its live hourly rate. - [GPU Prices](https://www.spheron.network/gpu-price/): Index of NVIDIA GPU list prices next to live Spheron rental rates - [NVIDIA GB10 (DGX Spark) Price](https://www.spheron.network/gpu-price/gb10/): DGX Spark price history from the $3,999 launch, GB10 specs, and buy vs rent against RTX PRO 6000 and H200 - [NVIDIA RTX 3090 Price](https://www.spheron.network/gpu-price/rtx-3090/): $1,499 launch MSRP, 24GB GDDR6X specs, and buy vs rent against RTX 4090 and RTX 5090 - [NVIDIA RTX 4080 Super Price](https://www.spheron.network/gpu-price/rtx-4080-super/): $999 launch MSRP, 16GB GDDR6X specs, and when 16GB is enough for AI - [NVIDIA RTX A6000 Price](https://www.spheron.network/gpu-price/rtx-a6000/): $4,650 NVIDIA list price, 48GB GDDR6 ECC specs, and buy vs rent against L40S ## Live Pricing Snapshot (as of 22 Oct 2026) Spheron per-GPU hourly rates captured from the live marketplace API when this file was built. On-demand (dedicated) and spot are separate pools that move independently, so neither is reliably the cheaper one; spot capacity can be reclaimed without notice. GB200, GB300 and Vera Rubin are reservation and pre-order products with no hourly rate. Current rates: https://www.spheron.network/pricing/ | GPU | On-demand per GPU | Spot per GPU | | --- | --- | --- | | NVIDIA B300 | $12.42/hr | $5.93/hr | | NVIDIA H100 | $2.65/hr | $2.20/hr | | NVIDIA B200 | $10.05/hr | $5.23/hr | | NVIDIA H200 | $5.51/hr | $3.35/hr | | AMD MI300X | $3.59/hr (last seen 17 Sep 2026) | none on record | | NVIDIA GH200 | $2.75/hr (last seen 17 Sep 2026) | none on record | | NVIDIA A100 | $1.43/hr | $1.19/hr | | NVIDIA RTX PRO 6000 | $2.35/hr | $1.11/hr | | NVIDIA RTX 5090 | $0.84/hr (last seen 17 Sep 2026) | $0.89/hr (last seen 15 May 2026) | | NVIDIA RTX 6000 Ada | $0.78/hr | $0.93/hr | | NVIDIA L40S | $0.96/hr | $1.05/hr | | NVIDIA RTX 4090 | $0.72/hr (last seen 17 Sep 2026) | none on record | ## Feature Pages - [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 GPUs in under 2 minutes - [Spot GPU Instances](https://www.spheron.network/features/spot-instances/): Spot capacity priced from its own pool, for batch jobs, checkpointed training, and other interruptible work. Spot instances can be reclaimed without notice - [Reserved GPU Commitments](https://www.spheron.network/features/reserved-commitments/): Volume pricing with guaranteed availability. Custom clusters from 8 to 512+ GPUs with InfiniBand ## Tools - [AI Training Cost Calculator](https://www.spheron.network/tools/training-cost-calculator/): GPU cost and wall-clock time to train or fine-tune a model, using live Spheron pricing, side by side with AWS and GCP - [GPU Recommender](https://www.spheron.network/tools/gpu-recommender/): VRAM calculator and GPU picker for any Hugging Face model, with per-model pages at /tools/gpu-recommender/// ## Blog Topic Hubs Each hub collects every post in one topic cluster behind a pillar guide. - [Blog Topics](https://www.spheron.network/blog/topics/): Index of all 16 blog topic clusters - [GPU Selection](https://www.spheron.network/blog/topics/gpu-selection/): Choosing the right GPU by workload, generation comparisons, and non-NVIDIA alternatives - [GPU Deep Dives](https://www.spheron.network/blog/topics/gpu-deep-dives/): Spec sheets and benchmarks for individual GPU models, from single cards to rack-scale systems - [GPU Pricing](https://www.spheron.network/blog/topics/gpu-pricing/): Per-provider on-demand and spot rate breakdowns, plus API-vs-self-hosting cost math - [Provider Comparisons](https://www.spheron.network/blog/topics/provider-comparisons/): Spheron vs named competitors, alternatives roundups, and regional availability - [vLLM](https://www.spheron.network/blog/topics/vllm/): Production vLLM deployment guides, model-specific setups, and serving architecture - [Serving Stacks](https://www.spheron.network/blog/topics/serving-stacks/): SGLang, TensorRT-LLM, Triton, and other LLM serving engines - [Inference Optimization](https://www.spheron.network/blog/topics/inference-optimization/): KV cache, quantization, speculative decoding, and attention techniques - [MoE Inference](https://www.spheron.network/blog/topics/moe-inference/): Deploying and fine-tuning mixture-of-experts models with expert parallelism - [Model Deployment](https://www.spheron.network/blog/topics/model-deployment/): Step-by-step deployment guides for open-source LLMs, VLMs, diffusion, and audio models - [LLM Training](https://www.spheron.network/blog/topics/llm-training/): Fine-tuning, RLHF, and distributed pretraining on rented GPU clusters - [Infrastructure](https://www.spheron.network/blog/topics/infrastructure/): Orchestration, networking, storage, and observability for GPU clusters - [Kernel Development](https://www.spheron.network/blog/topics/kernel-development/): Writing and profiling CUDA and Triton kernels - [Cost Optimization](https://www.spheron.network/blog/topics/cost-optimization/): Billing models, right-sizing, and cost engineering for GPU spend - [AI Agents](https://www.spheron.network/blog/topics/ai-agents/): Agent framework deployments, memory, sandboxes, and agent infrastructure - [Industry Use Cases](https://www.spheron.network/blog/topics/industry-use-cases/): Self-hosting vertical AI tools in place of per-seat SaaS products - [Data Privacy](https://www.spheron.network/blog/topics/data-privacy/): Compliance frameworks and confidential computing for regulated AI workloads ## Pillar Guides The main guide for each blog topic hub. Every post is listed in https://www.spheron.network/llms-full.txt. - [GPU Selection: Best GPU for AI Inference in 2026: Benchmarks, Pricing, and Decision Guide](https://www.spheron.network/blog/best-gpu-for-ai-inference-2026/): L40S vs H100 vs H200 vs B200 for AI inference: real tokens/sec/dollar benchmarks, FP8 vs FP4 cost impact, and a workload-based decision guide for 2026. - [GPU Deep Dives: NVIDIA B200: 192GB Specs, Benchmarks, vs H100](https://www.spheron.network/blog/nvidia-b200-complete-guide/): B200 vs H100: 17,500 vs 3,000 tokens/sec on Llama 70B (MLPerf v6.0), 192GB HBM3e, 9,000 TFLOPS FP4. Full specs, benchmarks, and live rental pricing on Spheron. - [GPU Pricing: GPU Cloud Pricing Comparison 2026: H100, H200, B200 Rates](https://www.spheron.network/blog/gpu-cloud-pricing-comparison-2026/): H100, H200, B200, A100 and L40S hourly rates on Spheron vs AWS H100 at $6.88/hr and Azure at $12.29/hr. Full price table across 15+ providers, with hidden costs. - [Provider Comparisons: GPU Cloud Providers 2026: Top 10 Compared (Pricing, Performance, Availability)](https://www.spheron.network/blog/top-10-cloud-gpu-providers/): A practical guide to the ten GPU platforms shaping AI infrastructure in 2026. Compare Spheron, Lambda, Runpod, and others on pricing, performance, and control. - [vLLM: vLLM Production Deployment 2026: Multi-GPU Tensor Parallel + FP8 Docker Setup on H100](https://www.spheron.network/blog/vllm-production-deployment-2026/): vLLM production deployment in 2026: a working Docker command for multi-GPU tensor parallelism and FP8, tested on Spheron H100 SXM5, with Prometheus monitoring. - [Serving Stacks: vLLM vs TensorRT-LLM vs SGLang: Which Is Fastest? (H100 Benchmarks, 2026)](https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks/): vLLM vs SGLang vs TensorRT-LLM on one H100, same model: the throughput, latency, cold start, and VRAM numbers you need to pick an inference engine. - [Inference Optimization: LLM VRAM Requirements: How Much GPU Memory You Need (2026)](https://www.spheron.network/blog/gpu-memory-requirements-llm/): Exact VRAM for 7B to 744B models, including KV cache and quantization math. Find the cheapest GPU that runs your model without running out of memory. - [MoE Inference: MoE Model Inference on GPU Cloud: Expert Parallelism, Memory, and Cost (2026)](https://www.spheron.network/blog/moe-inference-optimization-gpu-cloud/): MoE inference guide: models like DeepSeek V3.2 and Llama 4 hold every expert in VRAM but compute only a few. Deploy them with vLLM, SGLang and Spheron spot. - [Model Deployment: GPU Requirements Cheat Sheet 2026: VRAM + Cost, 18 Models](https://www.spheron.network/blog/gpu-requirements-cheat-sheet-2026/): One table: VRAM from real 4-bit file sizes, cheapest GPU config, and live hourly cost for Llama 4, DeepSeek V3.2, Qwen 3.6, Kimi K2.5, and 14 more open models. - [LLM Training: How to Fine-Tune LLMs in 2026: Costs, GPUs, and Code](https://www.spheron.network/blog/how-to-fine-tune-llm-2026/): Fine-tuning a 7B model with QLoRA takes 2-4 hours on a single GPU. Real costs, GPU requirements, and step-by-step workflows for Llama, Qwen, and DeepSeek. - [Infrastructure: GPU Monitoring for ML: nvidia-smi, DCGM, and Production Observability Guide](https://www.spheron.network/blog/gpu-monitoring-for-ml/): Most AI teams run GPUs below 70% utilization. Learn nvidia-smi commands, PyTorch memory profiling, DCGM with Prometheus, and Grafana dashboards for GPU clusters. - [Kernel Development: FlashAttention-4 on GPU Cloud: Blackwell Inference Guide (2026)](https://www.spheron.network/blog/flashattention-4-blackwell-gpu-cloud-guide/): FlashAttention-4 brings SM100 tile-based kernels to Blackwell GPUs. Full guide: FA4 vs FA3 benchmarks, vLLM/SGLang setup, long-context gains, and migration steps. - [Cost Optimization: The GPU Cloud Cost Optimization Playbook: How to Cut Your AI Compute Bill by 60%](https://www.spheron.network/blog/gpu-cost-optimization-playbook/): Most teams overspend on GPU cloud by 40-60%. This GPU cost optimization playbook covers instance selection, spot pricing, checkpointed training, and idle GPUs. - [AI Agents: Agentic RAG on GPU Cloud: Embedding, Vector Search, LLM (2026)](https://www.spheron.network/blog/agentic-rag-gpu-infrastructure-guide/): Agentic RAG needs more than a retrieval library. Plan GPU memory, colocate embedding, vector search and LLM, and hit sub-200ms TTFT on bare-metal GPU cloud. - [Industry Use Cases: Self-Host AI Code Review on GPU Cloud: Deploy Open-Source PR Review Agents (2026 Guide)](https://www.spheron.network/blog/self-host-ai-code-review-agent-gpu-cloud/): Self-host AI code review on your own GPU: vLLM backend, GitHub App webhook, GPU sizing, and the cost breakeven against CodeRabbit for a 50-engineer team. - [Data Privacy: EU AI Act Compliance on GPU Cloud: Data Residency, Model Governance, and Deployment Guide (2026)](https://www.spheron.network/blog/eu-ai-act-compliance-gpu-cloud-guide-2026/): Practical EU AI Act compliance guide for AI teams on GPU cloud: risk classification, data residency rules, model governance, and a step-by-step checklist. ## Platform Details **Instance types:** Virtual machines or bare metal servers. Multi-GPU configs up to 8x per node. Cluster deployments up to 80+ GPUs with InfiniBand (400 Gb/s) for distributed training. Every instance has full SSH root access and a dedicated IP. **Billing:** Per-minute granularity after a 20-minute minimum runtime (60 minutes on a 2x AMD MI300X), no long-term contracts. Credit card, bank transfer, or stables (USDT, USDC). **Tiers:** On-demand (dedicated, not reclaimable), spot (separate pool, reclaimable without notice), and reserved commitments for guaranteed capacity. On-demand and spot rates move independently, so compare both on the pricing page. **Compliance:** Tier 2/3/4 data centers. HIPAA, ISO 27001, SOC 2 Type I/II certifications available. **Templates and frameworks:** PyTorch, TensorFlow, JAX, CUDA, Jupyter, vLLM, SGLang, Hugging Face Transformers, DeepSpeed, NVIDIA Container Toolkit, and custom Docker images. **Regions:** US, Europe, and Canada, with ongoing expansion. ## 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 and platform announcements - [Twitter/X @SpheronAI](https://twitter.com/spheronai): Product updates and GPU availability announcements - [LinkedIn](https://linkedin.com/company/spheronai): Company news - [Email](mailto:info@spheron.ai): General inquiries and partnership opportunities