What does H100 SXM actually cost on Cyfuture AI?

A single 1Ã H100 SXM instance (1H100.16v.256m) starts at $3.66/hr on-demand, billed per second from launch to termination. Reserved pricing cuts that to $2.92/hr on a 6-month commitment or $2.43/hr annually. The 2Ã H100 node â the most-rented configuration â runs $7.23/hr on-demand, dropping to $4.67/hr on a 12-month reservation. The 8Ã H100 node starts at $28.36/hr on-demand and goes as low as $18.29/hr on a 12-month reservation â that's over $88,000 per year in savings compared to on-demand at the same run time.

Is H100 worth the premium over A100 for my workload?

It depends entirely on the workload â and we'd rather give you an honest answer. For models above 30B parameters, H100's FP8 Transformer Engine delivers 2â2.5Ã higher effective training throughput, which often means a shorter run time and lower total cost even at the higher hourly rate. For inference on 70B+ models, vLLM on H100 consistently delivers 2â3Ã higher token throughput, cutting per-token serving cost at scale. For smaller models under 13B, LoRA fine-tuning, or cost-sensitive burst jobs, A100 remains the better value. If you're unsure, reach out â our team will do a workload analysis before you commit.

How fast can I get an H100 instance running?

Under 60 seconds from console click to SSH on existing verified accounts. New accounts go through a one-time KYC check that takes under 10 minutes during business hours. After that, you can launch any 1Ã to 8Ã H100 SXM configuration on-demand with no quota request or capacity pre-approval. Pre-built images for PyTorch 2.5+, vLLM 0.6, TensorRT-LLM, and NeMo mean you skip stack provisioning entirely and start your job immediately.

What is the FP8 Transformer Engine and does it actually work?

The Transformer Engine is a hardware unit on Hopper that dynamically switches between FP8 and BF16 precision on a per-layer, per-step basis during training â automatically, with no manual tuning. The result is near-FP8 throughput (989 TFLOPS versus 312 TFLOPS FP16 on A100) with BF16-grade accuracy on standard language model workloads. It's not a marketing claim â teams running Llama 3 pre-training and Mixtral fine-tuning on Cyfuture H100 instances measure 1.8â2.4Ã faster wall-clock training versus the same model on A100, same configuration, same framework.

Can I build a multi-node H100 cluster for distributed training?

Yes. A single node supports up to 8Ã H100 SXM5 with the full NVLink 4.0 mesh at 900 GB/s â enough for 70B model training with no inter-node communication at all. For larger runs across 16, 32, or 64 GPUs, we connect nodes over 200/400 Gbps InfiniBand with NCCL-tuned topology. Contact our enterprise team for cluster reservations â setup time for configurations under 64 GPUs is typically 4â6 hours including network fabric validation and NCCL ring testing.

How does MIG partitioning work on H100?

Multi-Instance GPU on H100 lets you split a single card into up to 7 hardware-isolated compute instances, each with its own dedicated HBM3 memory slice, L2 cache, and SM compute allocation. Isolation is at the hardware level â physically separate circuits, not virtualization. Workloads on different MIG slices cannot access each other's memory or compute resources under any conditions. Slices start at $0.55/hr for a 1g.10gb partition â ideal for multi-tenant inference, CI/CD pipelines, or development environments where a full 80GB card is overkill.

Which frameworks and CUDA versions are supported?

CUDA 12.4, cuDNN 9, NCCL 2.20, and Triton 3.x on all H100 images. Pre-built stacks include PyTorch 2.5+, TensorFlow 2.18, JAX 0.4, vLLM 0.6, TensorRT-LLM 0.13, and NeMo 2.0. BYO container images are fully supported via OCI registry â both Docker Hub and NVIDIA NGC. If you have a custom container with specific library pinning, you can bring it directly â no re-packaging required.

Where are the H100 servers located?

Cyfuture AI operates Tier III+ data centres in Noida, Bangalore, and Delhi NCR â all with sub-5ms latency to major Indian metros. We're ISO 27001, SOC 2 Type II, and DPDP-compliant. INR billing is available with full GST invoicing. For international workloads, our Noida facility maintains sub-200ms round-trip latency to Singapore, Dubai, and Frankfurt. All data processed on Indian instances stays within India unless you configure cross-region transfer explicitly.

Rent A100 GPU on Cyfuture AI | 80GB HBM2e, MIG - From $2.20/hr

Built for these workloads

The GPU that just works

The A100 isn't the fastest chip on the rack anymore. But for most teams, that's the wrong question. The real question is: what's the most capable GPU you can rent all year without flinching at the invoice? For workloads under 30B parameters, that answer is still A100 — every time.

Fine-Tuning

Fine-tune open-source LLMs without the H100 markup

LoRA, QLoRA, and full fine-tuning of Llama 3, Mistral, Qwen, and DeepSeek all run natively on the NVIDIA A100. The 80GB SXM4 variant fits a 13B-parameter model in BF16 with zero offloading. You're not paying H100 rates for a job that doesn't need frontier silicon. When you're done fine-tuning, deploy it straight into a production inference endpoint without migrating infrastructure.

Llama 3 Mistral Qwen DeepSpeed PyTorch 2.x

Production Inference

Mature, predictable production inference

Llama 3 8B in FP16 or 70B INT8-quantized — the NVIDIA A100 handles both at stable hourly rates with a rock-solid five-year-old driver stack. For teams running steady-state inference on models up to 30B parameters, A100 is the safer, saner choice. vLLM and TensorRT-LLM are pre-tuned for Cyfuture's Ampere fleet, and you can split a single A100 card into seven MIG slices for multi-tenant workloads. Need to go fully serverless? Check out serverless inferencing — pay per token, no GPU reservation required.

vLLM TensorRT-LLM Triton INT8 quantization

HPC & Scientific

Scientific computing still loves Ampere

Molecular dynamics, CFD, weather modelling, protein folding — the NVIDIA A100 delivers 9.7 TFLOPS of double-precision throughput, which is more than enough for most academic HPC workloads and pharma research pipelines. Need to run multiple experiments in parallel? Multi-node GPU clusters let you scale to 64+ A100 nodes over InfiniBand without any procurement drama.

AMBER GROMACS NAMD OpenFOAM FP64 native

Fractional GPU

Split one A100 across seven workloads

NVIDIA Multi-Instance GPU (MIG) lets you partition a single A100 into up to seven hardware-isolated slices. Perfect for dev environments, Jupyter notebook serving, or running multiple small inference jobs simultaneously. Each slice starts at just $0.37/hr for a 1g.10gb partition — one of the most affordable ways to rent NVIDIA A100 GPU compute anywhere. Pair your MIG slices with a cloud AI dev environment for notebooks, experiment tracking, and quick iteration.

7× isolation From $0.37/hr Hardware-level Zero interference

Honest pricing

Pick a plan, launch in 60s

A100 GPU price starts at $2.20/hr on-demand — billed by the second, in INR or USD. No platform fees, no egress charges, no surprise invoices three weeks in. The longer you commit, the more you save.

1× A100

Starter tier — fine-tuning + inference workloads

$2.20/hr

No commitment

80 GB AI compute memory
1× NVIDIA A100 GPU
8 vCPUs · 64 GB instance RAM
200 GB/s network bandwidth
1,555 GB/s memory bandwidth

Launch this →

2× A100

Most-rented config · LLM fine-tuning sweet spot

$4.36/hr

No commitment

160 GB AI compute memory
2× A100 with 600 GB/s peer-to-peer
16 vCPUs · 128 GB instance RAM
400 GB/s network bandwidth
1,555 GB/s memory bandwidth
MIG partitioning supported

Launch this →

8× A100 NVLink

Multi-GPU training and inference clusters

$17.07/hr

No commitment

640 GB total AI compute memory
8× A100 with 2,400 GB/s peer-to-peer
64 vCPUs · 512 GB instance RAM
1,600 GB/s network bandwidth
1,555 GB/s memory bandwidth
InfiniBand available on Enterprise

Launch this →

Rent A100 vs H100 — what's the difference?

Same memory. Different jobs.

Both have 80GB of GPU memory. Both are NVIDIA flagship-class silicon. But when you rent an A100, you're getting the proven workhorse for sub-13B workloads where A100 GPU price makes a real difference. When you need frontier scale, the H100 is one click away on the same platform.

The Workhorse

A100·

Ampere · TSMC 7nm · 54.2B transistors

Memory80 GB HBM2e
Bandwidth2.0 TB/s
FP16 Tensor624 TFLOPS
FP64 (HPC)9.7 TFLOPS
NVLink3.0 · 600 GB/s
TDP400W
Driver maturity5+ years
On-demand price$2.20/hr

Best for

Fine-tuning ≤13B-param LLMs, production inference, HPC workloads, MIG fractional rental. Default for cost-conscious AI teams.

The Frontier

H100·

Hopper · TSMC 4N · 80B transistors

Memory80 GB HBM3
Bandwidth3.35 TB/s
FP16 Tensor1,979 TFLOPS
FP64 (HPC)67 TFLOPS
NVLink4.0 · 900 GB/s
TDP700W
FP8 Support3,958 TFLOPS
On-demand price$2.39/hr

Best for

Frontier pre-training (30B+), FP8 inference, long-context (32K–128K) workloads, multi-GPU NVLink-bound training jobs.

By the numbers

A100 in five stats

The shorthand version for engineers who want to skim before they read.

80GB HBM2e

Enough memory to fit a 13B-parameter model in BF16 with no offloading.

2.0TB/s

Memory bandwidth — enough for most transformer training and inference.

624TFLOPS

FP16 Tensor Core performance for mixed-precision AI training.

600GB/s

NVLink 3.0 mesh bandwidth in 8-GPU SXM4 clusters.

7× MIG slices

Hardware partition a single card into seven isolated tenants.

<60seconds

From console click to SSH-ready instance, every time.

99.95% SLA

Uptime guarantee with capacity priority for reserved customers.

3India DCs

Tier III+ facilities in Noida, Bangalore, and Jaipur .

Trusted by Industry leaders

FAQs - A100 GPU

The power of AI, backed by human support

At Cyfuture AI, we combine advanced technology with genuine care. Our expert team is always ready to guide you through setup, resolve your queries, and ensure your experience with Cyfuture AI remains seamless. Reach out through our live chat or drop us an email at [email protected] - help is only a click away.

How is A100 priced on Cyfuture AI?

The A100 GPU price starts at $2.20/hour for a 1× A100 (80GB HBM2e, 8 vCPUs, 64GB system RAM) on-demand — billed by the second. Reserved pricing drops as low as $2.08/hour on a 12-month commitment. Multi-GPU configurations scale linearly: 2× starts at $4.36/hr, 4× at $8.62/hr, and 8× NVLink-connected at $17.07/hr on-demand. For most teams running fine-tuning or inference on sub-30B-parameter models, the A100's price-to-performance ratio is hard to beat — and a cost calculator can estimate your exact monthly bill before you commit.

Is A100 still worth renting in 2026?

Absolutely — for the right workloads. When you rent an NVIDIA A100 GPU, you're getting the cost-effective default for fine-tuning open-source models (Llama 3, Mistral, Qwen), running production inference on models up to 13B parameters, and HPC workloads like molecular dynamics and CFD. The five-year-old driver stack is rock-solid, CUDA 12.x runs natively, and the A100 GPU price is considerably lower than H100 for workloads that genuinely don't need frontier silicon. The only scenario where A100 falls decisively behind is pre-training frontier models above 30B parameters — for that, the H100 is the right call.

How much do I save with reserved pricing?

Reserved discounts scale with both term length and GPU count. On a 1× A100 instance, 1-month reserved saves you 1.11% ($2.18/hr), 6-month reserved saves 2.22% ($2.16/hr), and the 12-month commitment saves 5.56% ($2.08/hr). Discounts increase for larger multi-GPU configurations — an 8× A100 cluster on a 12-month commitment is 8.49% off, saving roughly $1.45/hour or around $10,600 per year compared to on-demand A100 GPU price. Reserved customers also get capacity priority during peak demand. If you're trying to figure out the right plan, the pricing page lays out every tier side by side.

Can I split a single A100 across multiple users or jobs?

Yes — that's exactly what NVIDIA's Multi-Instance GPU (MIG) technology is for. A single NVIDIA A100 GPU can be partitioned into up to 7 hardware-isolated slices, each with dedicated memory, cache, and SM compute. MIG slices start at $0.37/hour for a 1g.10gb partition, making it one of the most affordable ways to rent A100 GPU compute for lighter workloads like dev environments, Jupyter notebook serving, or multi-tenant inference. Workloads on different slices are genuinely hardware-isolated — they cannot interfere with each other's performance.

How fast can I get an A100 server running?

Under 60 seconds from console click to SSH on an existing account. New accounts go through a one-time KYC verification (typically under 10 minutes during business hours) — after that, you never wait again. No quota approval is needed for any 1×–8× A100 configuration. Sign up at cyfuture.ai, pick your image (PyTorch, TensorFlow, JAX, or bring your own container), and you're running. The whole experience is designed to feel more like launching a GitHub Codespace than provisioning enterprise infrastructure.

Can I run distributed training across multiple A100 nodes?

Yes. A single node goes up to 8× A100 SXM4 with a full NVLink 3.0 mesh (600 GB/s per GPU). Multi-node training across 16, 32, or 64 A100 GPUs runs over 100/200 Gbps Ethernet on standard plans, or 200/400 Gbps InfiniBand for Enterprise clusters. NCCL is pre-tuned for Cyfuture's network topology, so you don't have to fiddle with environment variables to get decent all-reduce performance. If you're scaling beyond a single node, multi-node GPU clusters are purpose-built for that — same hardware, same platform.

Where are A100 servers physically located?

Cyfuture AI operates Tier III+ data centers in Noida, Bangalore, and Delhi NCR with sub-5ms latency to major Indian metros. Facilities are ISO 27001, SOC 2 Type II, and DPDP-compliant — which means your training data stays on Indian soil, a non-negotiable requirement for BFSI, healthcare, and government workloads. For international workloads, Cyfuture's Noida facility offers sub-200ms latency to Singapore, Dubai, and Frankfurt. Indian customers can be billed in INR with GST invoicing — no forex risk. You can learn more about the facilities on the data centers page.

What frameworks and CUDA versions are supported?

CUDA 11.x and 12.x are both supported natively on the NVIDIA A100 GPU. Pre-built stacks include PyTorch 2.x, TensorFlow 2.x, JAX, vLLM, TensorRT, NeMo, RAPIDS, and DeepSpeed. cuDNN 8 and 9, NCCL 2.x, Triton 2.x and 3.x are all available. Bring your own container via Docker Hub or NVIDIA NGC — full OCI registry support. If you prefer working with a managed notebook interface rather than raw SSH, an AI IDE lab runs directly on A100 GPU compute with pre-installed frameworks and one-click experiment tracking.

Pay $2.20 an hour. Train what you want.

Launch an A100 server in 60 seconds. Billed by the second. Tear it down when your job's done. That's it.

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The GPU that just works