Quick Summary: As of Jan 2026, NVIDIA H100 pricing in India typically ranges from ₹28–₹40 lakh per GPU, depending on the variant (PCIe/SXM/NVL), availability, warranty, and import costs. Cloud H100 rates usually start around ₹70/hr on spot/preemptible and go up to ₹249–₹400/hr on on-demand plans. If you want to rent, AceCloud offers H100 at ₹315.07/hr.
NVIDIA H100 Price in India: Buy vs Rent (Cost Comparison Table)
While evaluating the NVIDIA H100 GPU over a 36-month horizon, it’s important to compare the true ownership costs against on-demand rentals. The table below highlights how buying stacks up against AceCloud’s PAYG pricing at different daily usage levels.
Assumptions Used (hours/month, power, depreciation, etc.)
- Buy (1× NVIDIA H100 card): ₹28,00,000 to ₹40,00,000
- Maintenance: 5%/yr of card price
- = ₹1,40,000 to ₹2,00,000 / year
- = ₹4,20,000 to ₹6,00,000 over 36 months
- Electricity: ₹3/hr (300 W @ ₹10/kWh) (same as your existing assumption)
- Rent (PAYG): ₹315.07/hr
- Horizon math: 36 months ≈ 1080 days (30 days/month), so total hours = (hrs/day × 1080)
| Usage (hrs/day) | Buy for 36 months(Card + Maint + Elec = Total) | Rent (PAYG) 36 months | Net Difference (Buy – Rent) |
|---|---|---|---|
| 2 | ₹28,00,000 + ₹4,20,000 + ₹6,480 = ₹32,26,480 | ₹6,80,551 | +₹25,45,929 |
| 4 | ₹28,00,000 + ₹4,20,000 + ₹12,960 = ₹32,32,960 | ₹13,61,102 | +₹18,71,858 |
| 6 | ₹28,00,000 + ₹4,20,000 + ₹19,440 = ₹32,39,440 | ₹20,41,654 | +₹11,97,786 |
| 8 | ₹28,00,000 + ₹4,20,000 + ₹25,920 = ₹32,45,920 | ₹27,22,205 | +₹5,23,715 |
Rent if you’ll use H100 for <10 hrs/day on average; consider buying only if you can run it ~10–14+ hrs/day for 36 months.
Need more memory for larger models? Discover the NVIDIA H200 with 141GB HBM3e in our detailed NVIDIA H200 Price in India analysis, including rental vs purchase decisions for Indian AI teams.
Other Key Considerations
Renting an H100 Cloud GPU isn’t just cheaper than buying it but also comes with several key benefits like:
- Zero Upfront Investment – Enterprises avoid the massive capital outlay of ₹25,00,000 per card and can immediately access H100 performance.
- Operational Flexibility – GPU capacity scales up or down with workload intensity ensuring teams pay only for what they use.
- Latest Hardware Access – Cloud providers refresh their fleets regularly so you always run workloads on current-generation GPUs without worrying about depreciation.
- Reduced Management Burden – Providers handle maintenance, cooling, upgrades and uptime allowing internal teams to focus on model development and deployment.
- Integrated Ecosystem – Cloud GPU rentals typically include CPUs, memory, networking and storage bundled together ensuring balanced performance for AI and HPC tasks.
- Faster Experimentation – By avoiding long procurement cycles teams can spin up resources instantly, test at scale and accelerate time-to-market.
In short, renting H100s through a trusted cloud provider offers cost efficiency, scalability and freedom from hardware lock-in making it the smarter choice for most AI, ML and HPC workloads unless utilization is extremely high and predictable.
Where to Buy NVIDIA H100 in India (Jan 2026)
NVIDIA H100 is typically purchased through enterprise channels (NVIDIA DGX systems and certified partners), while standalone GPU availability varies by stock and the exact variant (PCIe 80GB vs H100 NVL 94GB vs SXM). Always confirm the exact SKU, warranty/RMA terms, and delivery timelines before placing an order.
1) Official and authorized route (recommended for DGX systems)
- NVIDIA (Official): Where to Buy DGX Systems (India)
NVIDIA lists DGX systems through select NVIDIA Partner Network (NPN) partners and provides an India region selector. - SHI India (formerly Locuz): NVIDIA DGX Systems
Publishes a DGX-focused page with a “Get a Quote” flow and DGX offerings such as DGX H100.
2) Standalone GPU sellers
- Serverstore.in: NVIDIA H100 94GB Graphics Card
Standalone listing for an H100 94GB variant (commonly associated with NVL-class configurations). Confirm exact model and system requirements before purchase. - ASA Computers: NVIDIA H100 80GB (NVH100TCGPU-KIT)
Standalone listing for an H100 80GB kit. Confirm shipping, import duties (if applicable), and warranty handling.
H100 variants and what to choose (PCIe vs SXM vs NVL)
NVIDIA H100 is sold in multiple form factors, and the right choice depends less on “which is best” and more on your server platform, scale, and workload (training vs inference, single GPU vs multi-GPU).
| Variant | Best for | Why choose it |
|---|---|---|
| H100 PCIe | Broad compatibility, single/dual GPU servers, inference, mixed workloads | Fits standard PCIe GPU servers, easier procurement, typically simpler to deploy/maintain |
| H100 SXM | Maximum performance training, large multi-GPU nodes (HGX/DGX-class systems) | Highest performance potential in dense multi-GPU systems, designed for high-speed GPU interconnect in HGX/DGX platforms |
| H100 NVL | LLM inference / serving, high-throughput inference, memory-heavy inference setups | Built for inference-focused deployments where you want strong throughput and large effective GPU memory bandwidth/connectivity in supported systems |
Quick decision guide
- Choose H100 PCIe if you want the easiest path to deploy (standard GPU server), you’re scaling horizontally, or you’re primarily doing inference / mixed workloads.
- Choose H100 SXM if your priority is maximum training performance in a multi-GPU node (HGX/DGX-style), especially when you’ll benefit from the platform’s high-speed GPU-to-GPU connectivity.
- Choose H100 NVL if your priority is LLM inference/serving efficiency and you’re deploying on a supported NVL server configuration designed for inference throughput.
Tip: If you’re renting H100 in the cloud, the provider has already made the platform choice for you – so focus on what matters most: VRAM needs, multi-GPU scaling, interconnect, and cost per training/inference hour.
Where to Rent NVIDIA H100 in India?
Finding a reliable place to rent NVIDIA H100 GPUs in India can feel overwhelming given demand and limited local supply. The table below compares top providers, highlights their strengths and points out what to consider before you choose.
| Provider | Best For | Key Strengths | Considerations |
| AceCloud | India-based teams, regulated workloads, fast scaling | India DCs, predictable pricing, quick provisioning, strong support, broad GPU choices | Best when you need India residency + flexible scaling |
| E2E Networks | Budget experiments, startups, academia | Competitive hourly rates, India DCs, simple console | Fewer integrations than hyperscalers |
| Linode | Small projects, POCs, lightweight inference | Simple setup, competitive GPU pricing | Limited high-end GPU availability for large training |
| AWS / GCP / Azure (Hyperscalers) | Global enterprises, managed services, multi-region | Largest ecosystem, managed AI stack, massive scale | Complex pricing, egress costs, India latency and compliance varies |
Key Insight:
If you are running AI or ML workloads in India and want low latency, predictable costs, and quick setup, local providers like AceCloud or E2E Networks generally deliver better performance-to-cost ratios than hyperscalers. Hyperscalers still work well for global deployments or when you already use their managed services but watch out for egress charges and compliance overhead. For light workloads or dev/test, Linode offers a simple entry point.
Why NVIDIA H100 Prices Vary in India
H100 pricing in India is not a single fixed number. Quotes vary because the final cost depends on the exact product, the supply route, and what is included in the deal.
1) Variant and platform (PCIe vs SXM vs NVL)
Different H100 variants have different platform requirements and availability. SXM is usually sold with HGX or DGX-class systems, while PCIe is more common for standard GPU servers. NVL availability depends on specific OEM server designs.
2) Stock availability and delivery timelines
In-stock units with faster delivery often cost more than backorder quotes. Pricing can also change based on allocation, volume, and urgency.
3) Import, taxes, and currency effects
Import logistics, customs duties, GST, shipping, and INR to USD movement can all impact what you pay in India.
4) Buying channel and margins
Authorized and OEM routes typically provide clearer warranty and support. Reseller or parallel import routes may look cheaper but can vary in warranty coverage and after-sales support.
5) What is included in the quote
Some quotes are GPU-only, while others include a full server configuration, networking, installation, testing, and support. Always compare like for like before deciding.
What is the NVIDIA H100 GPU?
The NVIDIA H100 GPU, built on the Hopper architecture introduces a Transformer Engine optimized for AI workloads. It is designed for large-scale AI training, has high-throughput inference and HPC workloads.
Below, we’ve mentioned key specification table of NVIDIA H100: –
| Factors | H100 SXM | H100 NVL |
|---|---|---|
| FP64 | 34 teraFLOPS | 30 teraFLOPS |
| FP64 Tensor Core | 67 teraFLOPS | 60 teraFLOPS |
| FP32 | 67 teraFLOPS | 60 teraFLOPS |
| TF32 Tensor Core | 989 teraFLOPS | 835 teraFLOPS |
| BFLOAT16 Tensor Core | 1,979 teraFLOPS | 1,671 teraFLOPS |
| FP16 Tensor Core | 1,979 teraFLOPS | 1,671 teraFLOPS |
| FP8 Tensor Core | 3,958 teraFLOPS | 3,341 teraFLOPS |
| INT8 Tensor Core | 3,958 TOPS | 3,341 TOPS |
| GPU Memory | 80GB | 94GB |
| GPU Memory Bandwidth | 3.35TB/s | 3.9TB/s |
| Decoders | 7 NVDEC7 JPEG | 7 NVDEC7 JPEG |
| Max Thermal Design Power (TDP) | Up to 700W (configurable) | 350-400W (configurable) |
| Multi-Instance GPUs | Up to 7 MIGs @ 10GB each | Up to 7 MIGS @ 12GB each |
| Form Factor | SXM | PCIe dual-slot air-cooled |
| Interconnect | NVIDIA NVLink™: 900GB/s PCIe Gen5: 128GB/s | NVIDIA NVLink: 600GB/s PCIe Gen5: 128GB/s |
| Server Options | NVIDIA HGX H100 Partner and NVIDIA-Certified Systems™ with 4 or 8 GPUs NVIDIA DGX H100 with 8 GPUs | Partner and NVIDIA-Certified Systems with 1–8 GPUs |
| NVIDIA Enterprise | Add-on | Included |
Key Benefits of H100 GPU
To maximize the value of an NVIDIA H100 GPU, you need a clear strategy. By understanding its key benefits, you can make an informed decision that aligns with your specific computational goals and long-term vision.
Unprecedented Performance
The H100 delivers a monumental leap in performance, powered by the new Hopper architecture. It accelerates complex AI models and high-performance computing tasks, significantly reducing training and inference times for large-scale applications.
Accelerated AI Training
With its Transformer Engine and fourth-generation Tensor Cores, the H100 drastically speeds up the training of massive language models and other deep learning networks. This enables researchers and developers to iterate faster and bring innovations to the market more quickly.
Enhanced Scalability
Designed for both single-GPU and multi-GPU configurations, the H100 leverages NVIDIA NVLink and NVSwitch technologies. This allows for seamless scaling of computational resources across multiple GPUs and servers, handling even the most demanding workloads.
Optimized for Data Science
The GPU’s massive memory bandwidth and efficient architecture are a boon for data scientists, who can process vast datasets at unparalleled speed. This acceleration is crucial for tasks like data preprocessing, feature engineering, and analytics.
Broad Software Ecosystem
The H100 is supported by NVIDIA’s comprehensive software stack, including CUDA, cuDNN and a vast array of libraries and frameworks. This robust ecosystem ensures that developers can easily harness the full power of the GPU without extensive low-level programming.
Energy Efficiency
Despite its immense power, the H100 is engineered for greater energy efficiency compared to previous generations. This reduces operational costs and minimizes the environmental footprint of large-scale data centers and AI clusters.
What Factors to Consider When Renting or Buying NVIDIA H100 GPU?
Renting or buying an NVIDIA H100 GPU is a major decision that depends on various factors. Understanding these key considerations will help you determine the most cost-effective and efficient solution for your specific needs.
Workload and Usage Patterns
Your specific computational needs dictate the best approach. Projects requiring consistent, long-term and high-volume processing, like continuous model training, benefit from ownership. Conversely, renting is ideal for short-term projects, experimental workloads or fluctuating demands, allowing you to pay only for what you use.
Upfront and Long-Term Costs
Buying an H100 GPU involves a substantial initial capital expenditure. Conversely, renting converts this into a more manageable operational expense. However, for continuous, 24/7 use, renting can become more expensive over time, making a purchase a more cost-effective long-term investment.
Flexibility and Scalability
Renting provides unmatched flexibility. You can quickly scale resources up or down to meet project demands without a large hardware investment. Buying, on the other hand, offers complete control and customization but locks you into a specific configuration, making scaling more complex and costly.
Maintenance and Expertise
When you rent, the provider handles all maintenance, cooling and technical support, freeing your team to focus on core tasks. Owning the hardware shifts the responsibility for upkeep, upgrades and troubleshooting to your organization, requiring significant in-house expertise and resources.
Data Security and Control
Purchasing an H100 ensures you have full, on-premise control over your data and infrastructure, which is crucial for sensitive or proprietary workloads. Renting may introduce data security risks depending on the provider’s protocols and your specific compliance needs.
Technological Obsolescence
Technology evolves rapidly, and the H100, while powerful today, will eventually be surpassed by newer models. Buying means your hardware depreciates and can become obsolete. Renting gives you access to the latest technology without the long-term commitment or risk of investing in outdated hardware.
Make the Smart Move with AceCloud
NVIDIA H100 price matters, but the right choice depends on utilization and urgency. If your workload stays below 426 hours monthly, rent H100 capacity to preserve cash and scale instantly. For steady, 24×7 inference, buy and lower your effective cost per hour.
AceCloud helps you choose the right model with real utilization, power and facility inputs. We provision secure H100 clusters, tune Transformer Engine stacks and benchmark tokens per second against your SLA.
Ready to choose with confidence? Request a free TCO comparison and deployment plan from AceCloud today. Start with a pilot on managed H100 nodes, then expand to owned capacity with our hybrid blueprint.
Talk to our specialists and move from debate to delivery. Book a 15-minute consultation now.
Frequently Asked Questions:
The NVIDIA H100 price in India typically starts at around ₹28–40 lakh per GPU, depending on the form factor, vendor and import duties. The final price can vary based on availability, support packages and hardware configuration.
For workloads under 426 hours per month, renting an H100 is more cost-effective. Renting saves upfront investment and offers pay-as-you-go flexibility. For 24×7 production use, buying becomes cheaper in the long run by lowering effective cost per GPU-hour.
Buying an NVIDIA H100 is ideal for continuous, high-intensity workloads like 24×7 LLM training, enterprise-scale inference and compliance-driven HPC tasks. Enterprises with predictable GPU usage can amortize costs and maximize value from ownership.
Startups prefer renting because it avoids capital expenditure, enables instant scaling and ensures access to latest-generation GPUs without worrying about depreciation, maintenance or facility overheads. Renting also accelerates proof-of-concept and research timelines.
Key factors include workload utilization, upfront budget, scalability needs, security requirements and technology lifecycle. If workloads are variable or experimental, renting is best. If usage is heavy and predictable, buying provides long-term savings.
