10 Best LLM Hosting Providers in India (2026)
A team that wants to serve a 70-billion-parameter model to Indian users has to answer three questions before it signs anything. How much GPU memory does the model need once the cache for concurrent requests is counted? Where will prompts and documents be processed? Who responds when the server stops answering at 2 a.m.? At 16-bit precision (FP16), the weights alone occupy about 140 GB, so the first answer decides whether one GPU, several GPUs or a quantized model is enough, and that decision sets most of the monthly bill. This guide compares LLM hosting providers in India for CTOs, ML engineers and founders facing that choice. It covers ten providers: Purvaco, which publishes this article, six Indian GPU and cloud companies, and three global hyperscalers. Prices are dated 15 October 2026. Where a provider does not publish a fact, the article says so. Disclosure: Purvaco publishes this comparison and holds position #1. The ranking is editorial research based on public documentation. It is not independent testing, and no provider was benchmarked or deployed for this article. Quick comparison of the 10 LLM hosting providers Pricing units differ (hourly GPU rates, monthly plans, quote only), so the last column is not a like-for-like ranking. Storage, data transfer and tax are extra unless stated. Provider Hosting model GPU or AI infrastructure India availability Best suited for Pricing 1. Purvaco Cloud GPU servers, managed or self-managed NVIDIA and AMD GPUs; A100, V100, RTX 6000/8000 listed Indian data centers; city not stated Managed, India-hosted GPU servers Quote only 2. E2E Networks GPU VMs, Kubernetes, TIR platform B200 to L4 Delhi NCR, Chennai INR-billed H100 and H200 H100 80 GB $3.99 per GPU-hour 3. Yotta VMs, bare metal, clusters, serverless H100 SXM, L40S Mumbai, Greater Noida, GIFT City Sovereign, large-scale AI Not public 4. Tata Communications Bare-metal GPU as a service H100, H200, L40S, L4 India only Private connectivity INR rate card 5. AceCloud GPU VMs, Kubernetes L40S to H200 Noida One to a few GPUs, INR monthly H100 HGX ₹1,80,000 per month 6. Jarvislabs Per-minute VMs, serverless H200 to L4 India regions Experiments, variable use H100 SXM $3.49 per GPU-hour 7. Cantech Cloud and dedicated GPU servers H200 (1x to 8x), L40S, L4 Mumbai Quoted H200 servers L4 from ₹40 per hour 8. AWS EC2 P5, Capacity Blocks H100 Mumbai Teams on AWS Per instance-hour 9. Google Cloud Compute Engine GPU VMs A3 (H100, H200), G2 (L4) Mumbai, Delhi Teams on Google Cloud Per hour 10. Microsoft Azure Azure VMs NCads H100 v5 [Confirm Central India availability] Teams on Azure Per hour How we evaluated the best LLM hosting providers Each provider was assessed on eight criteria: suitability for production LLM workloads, GPU availability and VRAM options, deployment flexibility, India availability and data residency, cost visibility and billing flexibility, security and data control, support and operational responsibility, and fit for the intended workload. “GPU availability” means a provider lists a card. “Usable VRAM” is the memory a model can occupy after the runtime takes its share. “Deployment control” is whether you get root access and can install your own inference software. Evidence comes from official provider pages retrieved in October 2026, plus a few labelled secondary sources. Prices and GPU stock change often, so confirm before ordering. The order reflects these criteria and the stated audience, not measured performance. Purvaco appears first as publisher. On public pricing and GPU range, E2E Networks, AceCloud and Jarvislabs publish more detail, and the Purvaco section says so. 1. Purvaco Overview Purvaco Technology Pvt. Ltd. sells dedicated servers, bare metal servers, cloud hosting, VPS hosting and a GPU Cloud Server product. Its homepage describes 24×7 support, managed services and cloud migration, and states that its data centers are Tier IV and Tier III facilities across India. The certifications page lists ISO 9001:2015, ISO/IEC 27017:2015, ISO/IEC 20000-1:2018, ISO/IEC 27701:2019, ISO 22301:2019 and ISO 27001:2022. Why consider Purvaco for LLM hosting? The GPU Cloud Server and Cloud GPU Server pages state: Dedicated allocation matters because a shared GPU gives variable token throughput, and a managed option helps teams without GPU operations staff. These are Purvaco’s own claims, and no independent test was run. Not confirmed in public material, and therefore not claimed: H100 or H200 availability, VRAM per configuration, multi-GPU server sizes, GPU-aware Kubernetes scheduling, a managed inference endpoint and a fine-tuning service. [Confirm available GPU models, VRAM and multi-GPU options with Purvaco.] LLM workloads to evaluate Infrastructure and deployment considerations Documented: dedicated NVIDIA GPUs, NVMe storage, up to 40 Gbps networking, Linux and Windows images, container and Kubernetes support, backup options, monitoring and dashboard or API deployment. Recommended for LLM work and needing confirmation: 80 GB of VRAM per GPU for 34B to 70B quantized models, 128 GB or more of system RAM, 1 TB or more of NVMe for weights and checkpoints, root access for vLLM or TGI, and private networking to the application tier. [Confirm GPU model and VRAM per configuration.] [Confirm which city hosts GPU servers.] “RTX 6000/8000” needs care: the Turing RTX 6000 has 24 GB, the RTX 8000 has 48 GB, and the RTX 6000 Ada has 48 GB. Ask which card is installed. Pricing and cost planning Purvaco does not publish GPU prices. Its pages state that quotes depend on GPU type, usage hours and configuration. For an accurate quote, share the model and parameter count, quantization plan, concurrent users, tokens per second, context length, availability needs, monthly utilization, and storage, backup and transfer needs. Limitations and questions to ask before purchasing Documented gaps: no public pricing, no VRAM per configuration, no GPU newer than the A100 generation listed, no data center city, and no evidence of a managed inference service. The GPU page states a 99.99% uptime SLA while the homepage states 99.9%, and no SLA document was found in the pages reviewed. Ask: Who should consider Purvaco? Businesses that want India-hosted, dedicated GPU servers with an optional managed service: internal assistants on small or mid-sized open-weight models, RAG applications, development
