--- title: "Run NVIDIA NGC PyTorch on a cloud GPU from $0.280/hr | PowerGPU" description: "PyTorch NGC on a fixed-price cloud GPU: one-click template, boots in minutes, from $0.280/hr on A100 SXM4. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/pytorch-ngc last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · Base & frameworks # Run NVIDIA NGC PyTorch on a cloud GPU, in minutes NVIDIA's NGC-optimised PyTorch build — tuned kernels for the datacenter cards. NVIDIA's NGC PyTorch container is the tuned build: pre-compiled kernels for Hopper and Ampere, APEX, DALI, Transformer Engine and FP8 support already wired. It is what large-scale training teams run on datacenter cards; rent it here with the same image, no registry login required. From **$0.280** /hr on an interruptible A100 SXM4. PyTorch NGC nvcr.io/nvidia/pytorch (SSH) (VM) Pinned image, rebuilt on upstream releases. Override env, ports and disk at deploy. ### Boots in minutes A full KVM virtual machine with the GPU passed through: your kernel, root, systemd. ### Template is free You pay the GPU price only: PyTorch NGC on a A100 SXM4 is $0.560/hr on-demand, $0.280/hr interruptible, billed per second. ### Volumes for state Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable. ### Private by default Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — SSH behind your own credentials. ## Best GPUs for PyTorch NGC Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks. | Tier | GPU | VRAM | On-demand | Interruptible | Why this card | | | --- | --- | --- | --- | --- | --- | --- | | (Good) | [A100 SXM4](https://powergpu.ai/gpu/a100-sxm4) | 80 GB | $0.560 | $0.280 | Ampere datacenter card the NGC stack is tuned for. | [Deploy](https://cloud.powergpu.ai/?gpu=a100-sxm4) | | (Better) | [H100 SXM](https://powergpu.ai/gpu/h100-sxm) | 80 GB | $1.428 | $0.714 | Transformer Engine FP8 paths shine on Hopper. | [Deploy](https://cloud.powergpu.ai/?gpu=h100-sxm) | | (Best) | [H200](https://powergpu.ai/gpu/h200) | 141 GB | $2.791 | $1.395 | 141 GB for longer context and bigger micro-batches under the same tuned stack. | [Deploy](https://cloud.powergpu.ai/?gpu=h200) | Need more VRAM? The [full catalogue](https://powergpu.ai/gpus) lists all 80 models with live availability; the [VRAM guide](https://powergpu.ai/guides/llm-vram-requirements) sizes models to cards. ## Deploy PyTorch NGC from the console, CLI or API Pick the template in the [console](https://cloud.powergpu.ai/) deploy bar, or script it: - **Console** — filter by GPU, choose *PyTorch NGC* in the template picker, set disk and env, deploy. - **CLI** — pip install powergpu, then the command on the right. [CLI reference](https://powergpu.ai/cli). - **API** — POST /v1/instances with "template": "pytorch-ngc". [REST reference](https://powergpu.ai/api). - **Own image** — any OCI reference works too; we inject the NVIDIA runtime. [Template docs](https://powergpu.ai/docs/templates). *deploy — pytorch-ngc* ``` $ powergpu launch --gpu a100-sxm4 --template pytorch-ngc \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (2m38s) # PyTorch NGC · A100 SXM4 · $0.560/hr · per second # https://i-7a41c0e2.powergpu.ai:8000 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the PyTorch NGC template - **Image**: nvcr.io/nvidia/pytorch - **CUDA**: inherits host driver (any 12.x / 13.x) - **Access**: SSH shell · full virtual machine (KVM) - **Category**: [Base & frameworks](https://powergpu.ai/templates#base) - **Storage**: Instance NVMe disk (sized at deploy) + optional [network volumes](https://powergpu.ai/products/volumes) - **Billing**: GPU price only, per second — no template fee, no setup fee ## Other base & frameworks templates - [NVIDIA CUDA on a cloud GPU](https://powergpu.ai/templates/nvidia-cuda) — The bare CUDA base image — drivers, toolkit, SSH and Jupyter. Build your own stack on top. - [PyTorch on a cloud GPU](https://powergpu.ai/templates/pytorch) — The training and research default: PyTorch 2.6, cuDNN, JupyterLab and SSH out of the box. - [TensorFlow CUDA on a cloud GPU](https://powergpu.ai/templates/tensorflow-cuda) — TensorFlow with GPU support, Keras and TensorBoard on a mapped port. - [NVIDIA RAPIDS on a cloud GPU](https://powergpu.ai/templates/nvidia-rapids) — GPU-accelerated data science — cuDF, cuML, cuGraph in a notebook. - [All-in-One App Studio on a cloud GPU](https://powergpu.ai/templates/all-in-one-app-studio) — A launcher bundling the most-used AI apps behind one desktop — pick and run. - [All 37 templates](https://powergpu.ai/templates) ## PyTorch NGC on a cloud GPU: FAQ Environment variables, ports and custom images are covered in the [template docs](https://powergpu.ai/docs/templates). **How much does it cost to run PyTorch NGC on a cloud GPU?** Only the GPU price — the template is free. From $0.280/hr on an interruptible A100 SXM4, $0.560/hr on-demand on a A100 SXM4. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does PyTorch NGC take to start?** Two to four minutes: this template is a full virtual machine that boots its own kernel. Restarting a stopped instance is faster, and your disk is exactly as you left it. **Can I keep my models and outputs between sessions?** Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy. **Why choose the NGC build over plain PyTorch?** On datacenter GPUs the NGC container is typically 10–30% faster on transformer training thanks to Transformer Engine, fused kernels and NCCL tuning — and it is validated release by release by NVIDIA. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/templates/pytorch-ngc · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt