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NVIDIA A100

A100 80GB SXM4 is the Ampere workhorse: 80 GB HBM2e, about 2.0 TB/s, NVLink at 600 GB/s, and MIG if you want to split the card. The catalog host is 14 vCPUs and 100 GB of RAM. It remains the right spend for 7B–30B training and quantized 70B inference when you do not need FP8.

Deploy NVIDIA A100 on Marketplace →All Hardware Fleet

Flexible Compute Modes

Deploy NVIDIA A100 Your Way: Docker Pods or Root KVM VPS

NovaPod lets you decide how you want to harness GPU power. Whether you need an instant pre-cached container stack, a full root virtual machine with dedicated public IP, or a multi-node NVLink cluster, pay only for what you run with second-by-second crypto billing.

Docker GPU PodCold Boot < 15s

Fast, Lightweight AI Containers

Pure GPU compute without hypervisor overhead. Boot straight into PyTorch, vLLM, ComfyUI, or JupyterLab.

1-second precision metering
37+ Pre-cached ML framework templates
Zero idle fee when instance is stopped

Deploy as Docker Pod

Root KVM VPSPersistent NVMe

Full Root Virtual Machine

Dedicated operating system (Ubuntu 24.04 LTS / Windows GPU Server) with complete sudo root SSH access from minute one.

Dedicated public IPv4 (all ports open)
Pause anytime; pay only $0.00014/GB/hr for disk
Run Docker-in-Docker, k3s, custom drivers

Deploy as Root VPS

Multi-GPU Cluster900 GB/s NVLink

Supercomputing & Distributed Training

Pool up to 8× NVIDIA A100 on a unified node fabric with Quantum-2 InfiniBand RDMA for multi-node scale.

Zero-loss all-reduce gradient exchange
PyTorch FSDP, DeepSpeed Zero-3 ready
Native GPUDirect Storage & RDMA fabric

Configure Cluster

Hardware Specifications

Architecture: NVIDIA Ampere
VRAM: 80 GB HBM2e
Bandwidth: 2.0 TB/s
Tensor Cores: 432, 3rd generation
CUDA cores: 6,912
FP64: 9.7 TFLOPS
FP32: 19.5 TFLOPS
TF32: 156 TFLOPS
FP16: 312 TFLOPS
INT8: 624 TOPS
FP8: Not supported
NVLink: 600 GB/s on SXM
MIG: Up to 7 instances
Reference host: 14 vCPUs, 100 GB RAM
Form factor: SXM4 or PCIe Gen4
TDP: 400 W SXM, about 300 W PCIe
Local disk: Listed on the offer

The card you use when Hopper is wasted

A100 80GB SXM is Ampere: 80 GB of HBM2e at about 2.0 TB/s, NVLink at 600 GB/s, and MIG if you want to partition the GPU. There is no FP8 and no Transformer Engine. FP16, BF16, and TF32 are the precisions that matter. The menu host is 14 vCPUs and 100 GB of RAM.

That is enough for 7B–30B training and for a quantized 70B serve. A full 70B in FP16 does not fit. QLoRA is how most people touch 70B on one A100. Eight of them on NVLink, with FSDP or DeepSpeed, is a normal fine-tune cluster.

Why it is still in the menu

H100 is about 1.6× the bandwidth and adds FP8. That is a real gap on transformer training above 30B and on FP8 inference. It is not a real gap on a BERT-scale job, a ResNet, a recommender batch, or a 13B LoRA. Paying Hopper or Blackwell rates for those jobs is how budgets disappear.

MIG splits one A100 into smaller instances when several light jobs would otherwise each take a whole card. Use it for inference tenants and notebooks. Do not use it for a job that needed the full 80 GB and NVLink.

Moving up or down

Move up to H100 when the profile shows FP8 or when a 30B-plus training step is the bottleneck. Move up to H200 when the same Hopper job runs out of memory. Move down to a 24 GB consumer card to debug the container, then come back.

The live ask is on the marketplace. A100 offers are usually the cheapest datacenter HBM in the list. Read the median before you take an outlier, and stop the pod when the epoch ends.

Architectural Generation Comparison

A100 is the cost line under Hopper. Move up only when FP8 or more memory shows up in the profile.

	A100 80GB	H100	H200
Memory	80 GB HBM2e	80 GB HBM3	141 GB HBM3e
Bandwidth	2.0 TB/s	3.35 TB/s	4.8 TB/s
FP8	No	Yes	Yes
NVLink	600 GB/s	900 GB/s class	900 GB/s class
MIG	Yes	Yes	Yes

Storage & Tooling Infrastructure

High-Speed Storage, Native Ports & Crypto Rails

Every NVIDIA A100 instance on NovaPod connects directly to high-throughput infrastructure designed to eliminate I/O bottlenecks and onboarding delays.

25 Gb/s Shared Network Storage

Mount large model checkpoints and training sets across one or multiple instances simultaneously. Data outlives single pods and stays accessible when instances stop.

JupyterLab, VS Code & SSH

Connect from your local terminal with standard SSH keys or open web-based IDEs directly in browser tabs without managing complex reverse proxy configs.

Non-Custodial Multi-Chain Rails

Deposit USDT, USDC, SOL, ETH, or BNB to your dedicated personal deposit address. Confirmed on-chain in seconds with zero mandatory identity verification.

Target Workloads for NVIDIA A100

LLM training and fine-tuning

The comfortable range is 7B–30B in mixed precision. QLoRA is how a 70B model lands on one 80 GB card. Eight A100s on NVLink are a normal fine-tune cluster.

Llama-class 8B and Mistral 7BQwen 14B and CodeLlama 13BQLoRA on Llama 70BFSDP and DeepSpeed ZeRO up to about 30B

Production inference

INT8, INT4, and FP16 paths are mature. MIG splits one card when several light models would otherwise each take a GPU.

8B and 7B at high concurrency70B in INT4 on one card70B in INT8 across two cardsBERT and T5 embeddings

Classic ML and vision

BERT, ResNet, recommenders, and anything that never asked for FP8. Paying Hopper rates for these jobs is how budgets disappear.

BERT-scaleResNet and detectionRecommendersBatch scoring

Analytics and light HPC

RAPIDS and other data-frame work that wants a large HBM buffer. FP64 is 9.7 TFLOPS, so heavy simulation still wants H100.

GPU dataframesETL that fits in 80 GBGraph jobs in memoryNot FP64-bound CFD

When to Choose NVIDIA A100

Pick the A100 for 7B–30B and a mature stack

Training, fine-tuning, quantized 70B serving, and classic ML. You trade FP8 throughput for a lower ask than Hopper.

Pick the H100 for FP8

Transformer Engine and about 1.6× the bandwidth. Make the jump when that speedup pays for the higher ask, especially above 30B.

Pick an L40S for cheaper inference

48 GB, no NVLink, strong enough for sub-30B inference and batch image or video. Not a multi-GPU training fabric.

Pick a 24 GB consumer card to debug

A 4090-class GPU is the cheap way to prove a container. Move the same image here when you need 80 GB, ECC, MIG, or NVLink.

What still belongs here

7B–30B training, QLoRA on a 70B, BERT-scale jobs, and RAPIDS. The stack is the most exercised CUDA target in production.

Menu host

14 vCPUs and 100 GB of RAM with 80 GB of VRAM. Enough for one training process. Not a multi-GPU chassis.

Frequently Asked Questions

How much does an A100 cost here?

The live ask, billed per second, with no contract and no 20-minute minimum. Published hyperscaler on-demand rates for 80 GB A100s are often a few dollars per GPU-hour. The figure that matters is the offer in front of you.

What is the cheapest way?

A reclaimable offer, if the host lists one, for training you can checkpoint. Steady serving wants a card that is not taken back. Both are still the same per-second meter.

Can I rent it by the hour?

Yes. A one-hour benchmark costs about one hour, because billing follows seconds.

SXM or PCIe?

SXM4 is 400 W with NVLink at 600 GB/s, for multi-GPU training and for 70B FP16 across two or more GPUs. PCIe is about 300 W, easier to mix into a normal server, and has no NVLink.

40 GB or 80 GB?

This page is the 80 GB part. Bandwidth on 80 GB is about 2.0 TB/s versus about 1.55 TB/s on the 40 GB card. Do not plan a 30B fine-tune on 40 GB.

Does MIG work?

Yes. One A100 splits into as many as seven instances, each with its own compute and memory slice. Use it for light inference tenants. Do not use it for a job that needed the full 80 GB and NVLink.

Multi-node?

Eight A100s on NVLink is the node. Between nodes, plan a real RDMA fabric and GPUDirect, and test PyTorch DDP, DeepSpeed, or Megatron before you assume the cluster scales.

Which frameworks ship?

PyTorch, TensorFlow, JAX, vLLM, TensorRT-LLM, Triton, and SGLang on a CUDA 12 image, plus RAPIDS if the job is dataframes. You can bring your own image.

Can it run 70B?

INT4, about 35 GB, fits on one 80 GB card. INT8 wants two cards with tensor parallel. FP16 training or inference at 70B wants two or more 80 GB SXM GPUs on NVLink. The sweet spot remains 7B–30B.

Is it worth it over H100?

If the workload is FP8 or bandwidth-bound, H100 pays for itself. If you are fine-tuning up to 30B, or serving a model that fits in 80 GB without FP8, A100 usually wins on dollars.

Is it faster than a 4090?

On a kernel that fits in 24 GB, a 4090 can be competitive and cheaper. The A100 wins when the job needs 80 GB of HBM, ECC, MIG, or 600 GB/s NVLink. For 70B or any multi-GPU training run, the 4090 is the wrong machine.

Is it faster than a T4?

Yes, by a wide margin: 312 TFLOPS of FP16 tensor versus about 65, 80 GB versus 16 GB, and MIG plus NVLink. The T4 is a 70 W card for small serving. Anything past about 13B, and any real training run, wants the A100 or newer.

Should I buy one?

Street prices move, and an 8-GPU server is a large capital purchase of a generation NVIDIA has already followed with Hopper and Blackwell. Renting wins unless the cards stay busy and you already have the rack.

Alternative Hardware Options

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