HopperOfertas ao vivo do mercado disponíveis

NVIDIA H200

H200 keeps H100's Hopper compute and replaces the memory system: 141 GB of HBM3e at 4.8 TB/s. The menu shape you see in the catalog is a larger host, about 44 vCPUs and 182 GB of RAM, next to 141 GB of VRAM. Pick it when H100 jobs fail on memory, not on FLOPS.

Implantar NVIDIA H200 no mercado →Toda a frota de hardware

Modos flexíveis de computação

Implante NVIDIA H200 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 H200 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 Hopper
VRAM: 141 GB HBM3e
Bandwidth: 4.8 TB/s
Tensor Cores: 528, 4th generation
CUDA cores: 16,896
FP64: 34 TFLOPS
FP32: 67 TFLOPS
TF32: 989 TFLOPS with sparsity
FP16 Tensor: 1,979 TFLOPS with sparsity
FP8 Tensor: 3,958 TFLOPS with sparsity
INT8: 3,958 TOPS with sparsity
NVLink: 900 GB/s
Reference host: 44 vCPUs, 182 GB RAM
Form factor: SXM5
TDP: 700 W
Versus H100 memory: 1.76× capacity, about 1.4× bandwidth

Same Hopper compute, different memory

H200 does not replace the H100's Tensor Cores. It replaces the memory: 141 GB of HBM3e at 4.8 TB/s instead of 80 GB of HBM3 at 3.35 TB/s. That is about 1.76× the capacity and about 1.4× the bandwidth. FP8, the Transformer Engine, and the Hopper software stack stay.

The menu host is larger than the H100 row: about 44 vCPUs and 182 GB of RAM with 141 GB of VRAM. Use the host RAM for the dataloader and the CPU side of a RAG stack. Do not expect it to act like a second GPU.

When 80 GB runs out

FP16 weights for a 70B model are about 140 GB before any cache. They are tight on an H200 and impossible on an H100. FP8 is the practical way to serve 70B with a 32k context and a real batch on one card. Multi-model serving, a resident embedding index, or a long agent trace are the other reasons the extra 61 GB pays for itself.

If the profile says you are compute-bound on a model that already fits in 80 GB, H200 will not feel faster in proportion to its price. If the profile says you are bandwidth-bound or out of memory, it is the Hopper card to take.

Neighbors

B200 is the next architecture: 192 GB HBM3e, 8 TB/s, and FP4. Move there for training that needs Blackwell numerics or for models that do not fit in 141 GB. A100 is the cheaper card when FP8 does not matter. MI300X has 192 GB on one GPU, but only if the image is ROCm.

Launch is the same as H100. Pick the live H200 offer, start a serving template, and stop the pod when the experiment ends. The ask on the card is the rate. The median next to it tells you whether that ask is typical.

Comparação entre gerações

H200 does not replace H100's compute. It replaces the memory system.

	H200	H100	B200
VRAM	141 GB HBM3e	80 GB HBM3	192 GB HBM3e
Bandwidth	4.8 TB/s	3.35 TB/s	8 TB/s
FP4	No	No	Yes
70B FP16 weights	Tight	Does not fit	Fits
70B FP8 + cache	Comfortable	Modest cache	Comfortable

Armazenamento e ferramentas

Armazenamento rápido, portas nativas e trilhos cripto

Cada instância NVIDIA H200 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 H200

Long-context serving

141 GB holds a 70B model in FP8 and a much larger KV cache than 80 GB. FP16 weights alone are about 140 GB, so they are tight.

32k context and beyondHigh-batch 70B serving100B-class with a smaller batchLlama-class 70B in FP8

Several models on one GPU

Colocate a chat model, a smaller specialist, and an embedding model instead of paying for three cards.

Two or three 30B-class modelsSeveral 7B–13B modelsChat plus embeddingsA/B of two checkpoints

Memory-bound training

Use it when activations, optimizer state, or the KV cache spill off an H100. If the step is compute-bound inside 80 GB, H100 is the better rate.

Larger micro-batchesFewer tensor-parallel ranksRLHF that is cache-heavyFine-tunes that OOM on H100

Throughput at scale

Eight H200s on NVLink, then 400 Gb/s class InfiniBand with GPUDirect if you leave the node. Tested paths are vLLM, TensorRT-LLM, SGLang, Megatron, and DeepSpeed.

8-GPU NVLink nodeMulti-node by quoteSame Hopper software as H100Stop the pod when the eval ends

Quando escolher NVIDIA H200

Pick H200 when H100 runs out of memory

Same Tensor Cores. The upgrade is 141 GB of HBM3e at 4.8 TB/s.

Pick H100 when 80 GB is comfortable

You would be paying for memory you never touch. Train on H100 and serve on H200 is a common split.

Pick B200 for FP4 or 192 GB

Blackwell is the next compute step: more memory, more bandwidth, and a format Hopper does not have.

Pick A100 when FP8 does not matter

Classic training and smaller inference are cheaper on Ampere.

The 141 GB rule

FP16 weights for a 70B model are about 140 GB before cache. H200 can hold that and little else. FP8 is the practical way to serve 70B with a long context on one card.

Menu host

The catalog row is 44 vCPUs and 182 GB of RAM with 141 GB of VRAM. That host is for the KV cache and the data loader, not a second GPU.

Perguntas frequentes

How much does an H200 cost here?

The live ask on the marketplace, billed per second. Specialist clouds and hyperscalers publish their own H200 rates, often in a wide range. Use those only as context. The number you pay is the offer you deploy.

What is the cheapest way?

A reclaimable offer, if one is listed and your job checkpoints. Production serving stays on a card the host will not take back. Tear the machine down when the run ends.

Can I rent it by the hour?

Yes, and the meter is seconds. A one-hour benchmark costs about one hour. There is no commit fee on a normal deploy.

What is the difference from H100?

Compute stays in the same Hopper class: fourth-generation Tensor Cores and FP8. Memory changes from 80 GB HBM3 at 3.35 TB/s to 141 GB HBM3e at 4.8 TB/s.

Inference or training?

Both, and it is especially strong for inference, because batch, context, and colocated models all consume the extra memory. Training helps when memory is the stall. If it is not, H100 often has the better price.

What fits on one GPU?

70B in FP8 with room for cache. 70B in FP16 is about 140 GB before cache, so it is tight. 100B-class needs a smaller batch. 200B and up wants tensor parallel across several H200s.

Can I serve more than one model?

Yes. 141 GB can hold two or three models around 30B, or several 7B–13B models, which is how a chat, code, and embedding stack shares one card.

Clusters?

Eight H200s per node on 900 GB/s NVLink is the unit. Multi-node wants 400 Gb/s class networking and GPUDirect. Larger shapes are quoted.

Is it worth it over H100?

When you are out of memory, when the KV cache is the limiter, or when you want several models resident. If you are comfortably under 80 GB, H100 is the cheaper Hopper card.

What is the host in the menu?

44 vCPUs and 182 GB of RAM next to 141 GB of VRAM. That RAM is for the dataloader and the CPU side of a RAG stack. It is not a second GPU. A specific offer can differ.

When do I skip it for MI300X?

When you need more than 141 GB on one GPU and the image is already ROCm. CUDA serving stays here or on B200.

Outras opções de hardware

Pronto para implantar NVIDIA H200?

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 →

HopperOfertas ao vivo do mercado disponíveis

NVIDIA H200

Implantar NVIDIA H200 no mercado →Toda a frota de hardware

Modos flexíveis de computação

Implante NVIDIA H200 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 H200 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 Hopper
VRAM: 141 GB HBM3e
Bandwidth: 4.8 TB/s
Tensor Cores: 528, 4th generation
CUDA cores: 16,896
FP64: 34 TFLOPS
FP32: 67 TFLOPS
TF32: 989 TFLOPS with sparsity
FP16 Tensor: 1,979 TFLOPS with sparsity
FP8 Tensor: 3,958 TFLOPS with sparsity
INT8: 3,958 TOPS with sparsity
NVLink: 900 GB/s
Reference host: 44 vCPUs, 182 GB RAM
Form factor: SXM5
TDP: 700 W
Versus H100 memory: 1.76× capacity, about 1.4× bandwidth

Same Hopper compute, different memory

When 80 GB runs out

Neighbors

Comparação entre gerações

H200 does not replace H100's compute. It replaces the memory system.

	H200	H100	B200
VRAM	141 GB HBM3e	80 GB HBM3	192 GB HBM3e
Bandwidth	4.8 TB/s	3.35 TB/s	8 TB/s
FP4	No	No	Yes
70B FP16 weights	Tight	Does not fit	Fits
70B FP8 + cache	Comfortable	Modest cache	Comfortable

Armazenamento e ferramentas

Armazenamento rápido, portas nativas e trilhos cripto

Cada instância NVIDIA H200 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 H200

Long-context serving

141 GB holds a 70B model in FP8 and a much larger KV cache than 80 GB. FP16 weights alone are about 140 GB, so they are tight.

32k context and beyondHigh-batch 70B serving100B-class with a smaller batchLlama-class 70B in FP8

Several models on one GPU

Colocate a chat model, a smaller specialist, and an embedding model instead of paying for three cards.

Two or three 30B-class modelsSeveral 7B–13B modelsChat plus embeddingsA/B of two checkpoints

Memory-bound training

Use it when activations, optimizer state, or the KV cache spill off an H100. If the step is compute-bound inside 80 GB, H100 is the better rate.

Larger micro-batchesFewer tensor-parallel ranksRLHF that is cache-heavyFine-tunes that OOM on H100

Throughput at scale

Eight H200s on NVLink, then 400 Gb/s class InfiniBand with GPUDirect if you leave the node. Tested paths are vLLM, TensorRT-LLM, SGLang, Megatron, and DeepSpeed.

8-GPU NVLink nodeMulti-node by quoteSame Hopper software as H100Stop the pod when the eval ends

Quando escolher NVIDIA H200

Pick H200 when H100 runs out of memory

Same Tensor Cores. The upgrade is 141 GB of HBM3e at 4.8 TB/s.

Pick H100 when 80 GB is comfortable

You would be paying for memory you never touch. Train on H100 and serve on H200 is a common split.

Pick B200 for FP4 or 192 GB

Blackwell is the next compute step: more memory, more bandwidth, and a format Hopper does not have.

Pick A100 when FP8 does not matter

Classic training and smaller inference are cheaper on Ampere.

The 141 GB rule

FP16 weights for a 70B model are about 140 GB before cache. H200 can hold that and little else. FP8 is the practical way to serve 70B with a long context on one card.

Menu host

The catalog row is 44 vCPUs and 182 GB of RAM with 141 GB of VRAM. That host is for the KV cache and the data loader, not a second GPU.

Perguntas frequentes

How much does an H200 cost here?

What is the cheapest way?

A reclaimable offer, if one is listed and your job checkpoints. Production serving stays on a card the host will not take back. Tear the machine down when the run ends.

Can I rent it by the hour?

Yes, and the meter is seconds. A one-hour benchmark costs about one hour. There is no commit fee on a normal deploy.

What is the difference from H100?

Compute stays in the same Hopper class: fourth-generation Tensor Cores and FP8. Memory changes from 80 GB HBM3 at 3.35 TB/s to 141 GB HBM3e at 4.8 TB/s.

Inference or training?

What fits on one GPU?

70B in FP8 with room for cache. 70B in FP16 is about 140 GB before cache, so it is tight. 100B-class needs a smaller batch. 200B and up wants tensor parallel across several H200s.

Can I serve more than one model?

Yes. 141 GB can hold two or three models around 30B, or several 7B–13B models, which is how a chat, code, and embedding stack shares one card.

Clusters?

Eight H200s per node on 900 GB/s NVLink is the unit. Multi-node wants 400 Gb/s class networking and GPUDirect. Larger shapes are quoted.

Is it worth it over H100?

When you are out of memory, when the KV cache is the limiter, or when you want several models resident. If you are comfortably under 80 GB, H100 is the cheaper Hopper card.

What is the host in the menu?

44 vCPUs and 182 GB of RAM next to 141 GB of VRAM. That RAM is for the dataloader and the CPU side of a RAG stack. It is not a second GPU. A specific offer can differ.

When do I skip it for MI300X?

When you need more than 141 GB on one GPU and the image is already ROCm. CUDA serving stays here or on B200.

Outras opções de hardware

Pronto para implantar NVIDIA H200?

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 H200

Implante NVIDIA H200 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

Same Hopper compute, different memory

When 80 GB runs out

Neighbors

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 H200

Long-context serving

Several models on one GPU

Memory-bound training

Throughput at scale

Quando escolher NVIDIA H200

Pick H200 when H100 runs out of memory

Pick H100 when 80 GB is comfortable

Pick B200 for FP4 or 192 GB

Pick A100 when FP8 does not matter

The 141 GB rule

Menu host

Perguntas frequentes

Outras opções de hardware

NVIDIA H100

NVIDIA B200

AMD Instinct MI300X

Pronto para implantar NVIDIA H200?

NVIDIA H200

Implante NVIDIA H200 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

Same Hopper compute, different memory

When 80 GB runs out

Neighbors

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 H200

Long-context serving

Several models on one GPU

Memory-bound training

Throughput at scale

Quando escolher NVIDIA H200

Pick H200 when H100 runs out of memory

Pick H100 when 80 GB is comfortable

Pick B200 for FP4 or 192 GB

Pick A100 when FP8 does not matter

The 141 GB rule

Menu host

Perguntas frequentes

Outras opções de hardware

NVIDIA H100

NVIDIA B200

AMD Instinct MI300X

Pronto para implantar NVIDIA H200?