AmpereOfertas ao vivo do mercado disponíveis

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.

Implantar NVIDIA A100 no mercado →Toda a frota de hardware

Modos flexíveis de computação

Implante NVIDIA A100 do seu jeito: pods Docker ou VPS KVM root

A NovaPod deixa você escolher como usar a GPU. Um contêiner já em cache, uma máquina virtual root com IP público ou um cluster NVLink de vários nós: você paga só os segundos em que roda, em cripto.

Docker GPU PodBoot a frio < 15 s

Contêineres de IA leves e rápidos

Computação de GPU sem o peso do hipervisor. Entre direto no PyTorch, vLLM, ComfyUI ou JupyterLab.

Medição com precisão de 1 segundo
Mais de 37 modelos de framework de ML já em cache
Sem taxa ociosa quando a instância está parada

Implantar como pod Docker

Root KVM VPSNVMe persistente

Máquina virtual root completa

Sistema dedicado (Ubuntu 24.04 LTS / Windows GPU Server) com SSH root e sudo desde o primeiro minuto.

IPv4 público dedicado (todas as portas abertas)
Pause quando quiser; pague só US$ 0,00014/GB/h pelo disco
Rode Docker-in-Docker, k3s e drivers próprios

Implantar como VPS root

Multi-GPU Cluster900 GB/s NVLink

Supercomputação e treino distribuído

Junte até 8× NVIDIA A100 em um nó com InfiniBand Quantum-2 RDMA para escalar a vários nós.

Troca de gradiente all-reduce sem perda
Pronto para PyTorch FSDP e DeepSpeed Zero-3
GPUDirect Storage nativo e malha RDMA

Configurar cluster

Especificações de hardware

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.

Comparação entre gerações

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

Armazenamento e ferramentas

Armazenamento rápido, portas nativas e trilhos cripto

Cada instância NVIDIA A100 na NovaPod se conecta a uma infraestrutura de alto débito feita para tirar gargalos de E/S e a espera inicial.

Armazenamento de rede compartilhado a 25 Gb/s

Monte checkpoints e conjuntos de treino em uma ou várias instâncias ao mesmo tempo. Os dados duram além de um pod e continuam acessíveis quando as instâncias param.

JupyterLab, VS Code & SSH

Conecte do terminal local com chaves SSH comuns ou abra IDEs na web em abas do navegador, sem configurar proxy reverso.

Trilhos multi-chain sem custódia

Deposite USDT, USDC, SOL, ETH ou BNB no seu endereço pessoal. A confirmação on-chain leva segundos e não há verificação de identidade obrigatória.

Cargas indicadas para 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

Quando escolher 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.

Perguntas frequentes

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.

Outras opções de hardware

Pronto para implantar NVIDIA A100?

Inicie uma instância no mercado ao vivo em menos de 15 segundos. Abasteça o saldo com criptomoeda e pague por segundo, sem compromisso.

Iniciar no mercado →

AmpereOfertas ao vivo do mercado disponíveis

NVIDIA A100

Implantar NVIDIA A100 no mercado →Toda a frota de hardware

Modos flexíveis de computação

Implante NVIDIA A100 do seu jeito: pods Docker ou VPS KVM root

Docker GPU PodBoot a frio < 15 s

Contêineres de IA leves e rápidos

Computação de GPU sem o peso do hipervisor. Entre direto no PyTorch, vLLM, ComfyUI ou JupyterLab.

Medição com precisão de 1 segundo
Mais de 37 modelos de framework de ML já em cache
Sem taxa ociosa quando a instância está parada

Implantar como pod Docker

Root KVM VPSNVMe persistente

Máquina virtual root completa

Sistema dedicado (Ubuntu 24.04 LTS / Windows GPU Server) com SSH root e sudo desde o primeiro minuto.

IPv4 público dedicado (todas as portas abertas)
Pause quando quiser; pague só US$ 0,00014/GB/h pelo disco
Rode Docker-in-Docker, k3s e drivers próprios

Implantar como VPS root

Multi-GPU Cluster900 GB/s NVLink

Supercomputação e treino distribuído

Junte até 8× NVIDIA A100 em um nó com InfiniBand Quantum-2 RDMA para escalar a vários nós.

Troca de gradiente all-reduce sem perda
Pronto para PyTorch FSDP e DeepSpeed Zero-3
GPUDirect Storage nativo e malha RDMA

Configurar cluster

Especificações de hardware

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

Why it is still in the menu

Moving up or down

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.

Comparação entre gerações

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

Armazenamento e ferramentas

Armazenamento rápido, portas nativas e trilhos cripto

Cada instância NVIDIA A100 na NovaPod se conecta a uma infraestrutura de alto débito feita para tirar gargalos de E/S e a espera inicial.

Armazenamento de rede compartilhado a 25 Gb/s

Monte checkpoints e conjuntos de treino em uma ou várias instâncias ao mesmo tempo. Os dados duram além de um pod e continuam acessíveis quando as instâncias param.

JupyterLab, VS Code & SSH

Conecte do terminal local com chaves SSH comuns ou abra IDEs na web em abas do navegador, sem configurar proxy reverso.

Trilhos multi-chain sem custódia

Deposite USDT, USDC, SOL, ETH ou BNB no seu endereço pessoal. A confirmação on-chain leva segundos e não há verificação de identidade obrigatória.

Cargas indicadas para 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

Quando escolher 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.

Perguntas frequentes

How much does an A100 cost here?

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?

Is it faster than a T4?

Should I buy one?

Outras opções de hardware

Pronto para implantar NVIDIA A100?

Inicie uma instância no mercado ao vivo em menos de 15 segundos. Abasteça o saldo com criptomoeda e pague por segundo, sem compromisso.

Iniciar no mercado →

NVIDIA A100

Implante NVIDIA A100 do seu jeito: pods Docker ou VPS KVM root

Contêineres de IA leves e rápidos

Máquina virtual root completa

Supercomputação e treino distribuído

Especificações de hardware

The card you use when Hopper is wasted

Why it is still in the menu

Moving up or down

Comparação entre gerações

Armazenamento rápido, portas nativas e trilhos cripto

Armazenamento de rede compartilhado a 25 Gb/s

JupyterLab, VS Code & SSH

Trilhos multi-chain sem custódia

Cargas indicadas para NVIDIA A100

LLM training and fine-tuning

Production inference

Classic ML and vision

Analytics and light HPC

Quando escolher NVIDIA A100

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

Pick the H100 for FP8

Pick an L40S for cheaper inference

Pick a 24 GB consumer card to debug

What still belongs here

Menu host

Perguntas frequentes

Outras opções de hardware

NVIDIA H100

NVIDIA H200

NVIDIA RTX PRO 6000

Pronto para implantar NVIDIA A100?

NVIDIA A100

Implante NVIDIA A100 do seu jeito: pods Docker ou VPS KVM root

Contêineres de IA leves e rápidos

Máquina virtual root completa

Supercomputação e treino distribuído

Especificações de hardware

The card you use when Hopper is wasted

Why it is still in the menu

Moving up or down

Comparação entre gerações

Armazenamento rápido, portas nativas e trilhos cripto

Armazenamento de rede compartilhado a 25 Gb/s

JupyterLab, VS Code & SSH

Trilhos multi-chain sem custódia

Cargas indicadas para NVIDIA A100

LLM training and fine-tuning

Production inference

Classic ML and vision

Analytics and light HPC

Quando escolher NVIDIA A100

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

Pick the H100 for FP8

Pick an L40S for cheaper inference

Pick a 24 GB consumer card to debug

What still belongs here

Menu host

Perguntas frequentes

Outras opções de hardware

NVIDIA H100

NVIDIA H200

NVIDIA RTX PRO 6000

Pronto para implantar NVIDIA A100?