Which NVIDIA GPUs Are Available on Cyfuture AI?

Cyfuture AI offers a broad range of high‑performance NVIDIA GPUs under its GPU‑as‑a‑Service (GPUaaS) and GPU cluster stacks, enabling users to run AI, large‑language models, deep learning, rendering, and HPC workloads with on‑demand access.

Cyfuture AI currently provides access to the following NVIDIA GPUs:

• NVIDIA H200 – latest Hopper‑based data‑center GPU for frontier‑scale LLM training and inference.
• NVIDIA H100 (including H100 SXM5, 80GB HBM3) – enterprise‑grade GPU for large‑scale AI training, LLMs, and high‑throughput inference.
• NVIDIA A100 (40GB / 80GB HBM2e) – widely used for deep learning, scientific computing, and multi‑instance GPU (MIG)‑based workloads.
• NVIDIA V100 – prior‑generation data‑center GPU suited to mature deep‑learning stacks and cost‑efficient training.
• NVIDIA L40S – Ada‑Lovelace‑based GPU targeted at generative AI inference, computer vision, and media rendering.
• NVIDIA T4 – efficient, lower‑power GPU ideal for inference and light‑to‑moderate AI workloads.​
• NVIDIA RTX 6000 Ada / other RTX series – workstation‑class GPUs often used for visualization, rendering, and some AI workloads.

All these GPUs are available via GPU‑as‑a‑Service and GPU clusters, with options ranging from single‑GPU instances to multi‑GPU nodes (e.g., 4× or 8× A100/H100 per node) hosted from Cyfuture’s India‑based data centers.

Key NVIDIA GPU Options on Cyfuture AI

H200 and H100 series

H200 and H100 GPUs are positioned as the top‑tier options for cutting‑edge AI, especially for training 100‑billion‑parameter LLMs and real‑time inference pipelines. They feature 80GB HBM3 memory, fourth‑generation Tensor Cores, transformer‑optimized acceleration, and NVLink or NVSwitch for high‑bandwidth multi‑GPU communication, making them ideal for large GPU clusters.

A100 and V100

The A100 (40GB/80GB) is a compute‑dense GPU optimized for deep learning, scientific simulations, and MIG‑based multi‑tenant workloads. V100, while older, remains a cost‑effective choice for training and inference on well‑established frameworks where the latest Hopper features are not critical. Both are commonly offered in multi‑GPU node configurations (4× or 8× per node) within Cyfuture’s GPU clusters.

L40S and T4

L40S, built on the Ada Lovelace architecture, is tuned for generative AI inference, computer vision, and media workloads, with PCIe packaging and dual NVENC/NVDEC engines. T4 is a smaller, power‑efficient GPU best suited for inference‑only or mixed‑use environments where higher throughput is not required.

RTX‑series GPUs

Cyfuture AI also lists RTX 6000 Ada and likely other RTX‑series GPUs under its GPU‑as‑a‑service and cluster offerings, primarily for visualization, rendering, and specific AI workloads that benefit from workstation‑class GPUs. These can be provisioned as single‑GPU or multi‑GPU nodes depending on the workload profile.

 

How These GPUs Are Delivered

Cyfuture AI exposes these NVIDIA GPUs through:

• GPU‑as‑a‑Service (GPUaaS): on‑demand, pay‑per‑hour access to H100, A100, L40S, V100, T4, and others from India‑based data centers (Noida, Jaipur, Raipur).
• GPU clusters: multi‑node clusters with H200, H100, A100, L40S, V100, and T4, optimized for large‑scale model training, LLM fine‑tuning, and distributed inferencing.

Users can choose instance types (single‑GPU vs multi‑GPU), memory size (40GB vs 80GB), and networking options (NVLink, high‑bandwidth interconnects) to fit their latency, throughput, and budget constraints.

 

Conclusion

Cyfuture AI gives enterprises, startups, universities, and developers access to a full spectrum of modern NVIDIA GPUs—from H200 and H100 for frontier‑scale AI, through A100 and L40S for large‑model training and inference, down to V100 and T4 for more conservative or cost‑driven workloads. By combining these GPUs with GPU‑as‑a‑Service and GPU clusters running from Indian data centers, Cyfuture AI enables scalable, low‑latency, and compliant AI infrastructure for teams across India and globally.

 

Follow‑Up Questions and Answers

Q1: Which NVIDIA GPUs on Cyfuture AI are best for training large LLMs?
The NVIDIA H200 and H100 are best suited for training 100B+‑parameter LLMs due to their 80GB HBM3 memory, transformer engine, and multi‑GPU NVLink‑enabled configurations. A100‑based clusters are also effective for smaller large‑language‑model training if Hopper‑generation GPUs are not required.

Q2: Can I use T4 or L40S just for inference workloads on Cyfuture AI?
Yes. T4 is ideal for cost‑efficient inference pipelines, while L40S is better for generative AI and computer‑vision inference where higher throughput and media‑encoding features are needed. Both are available as single‑GPU or clustered instances under GPU‑as‑a‑Service.

Q3: Are these GPUs available only in clusters or as single‑GPU instances too?
Cyfuture AI offers both: single‑GPU instances (e.g., one A100 or one T4) for light workloads and full multi‑GPU clusters (4× or 8× H100/A100 per node) for distributed training.

Q4: Are there any Intel or AMD GPUs also available alongside NVIDIA?
Yes. In addition to NVIDIA GPUs, Cyfuture Cloud’s GPU‑as‑a‑Service includes Intel Gaudi 2 and AMD MI300X, though NVIDIA GPUs dominate the AI and HPC lineup.

Q5: From which data centers are these NVIDIA GPUs hosted in India?
NVIDIA‑based GPU instances and clusters on Cyfuture AI are hosted from Cyfuture’s Indian data centers, including locations such as Noida, Jaipur, and Raipur, with DPDP‑compliant, pay‑per‑hour billing.