RunPod Development Environment

RunPod gives you on-demand cloud GPUs for FastVideo development. It's useful when you need a beefy GPU to test training runs, benchmark inference, or reproduce results without waiting for shared cluster time.

Prerequisites

• A RunPod account with billing configured
• An SSH key pair. If you don't have one, generate it with ssh-keygen -t ed25519
• Your public key (~/.ssh/id_ed25519.pub) ready to paste into RunPod

Step 1: Create a Pod

1. Verify your account

Make sure you're logged into the right RunPod account before spending credits.

2. Filter by CUDA version

Use "Additional Filters" to select CUDA 12.8.

3. Select a GPU

Click "Deploy" and pick a GPU. See GPU Recommendations below for guidance on which GPU to choose.

4. Pick the FastVideo template

Select the "FastVideo" or "fastvideo-dev" Pod Template. This pulls the pre-built image that includes all dependencies, Flash Attention, and a ready-to-use uv environment.

5. Name your pod

Use a memorable name like yourname-fastvideo-2026-03-28. This helps if you have multiple pods running.

6. Add a persistent volume (recommended)

Attach a network volume to /root/.cache or /models for storing downloaded model weights. Models can be 10-50 GB each, and re-downloading them every session wastes time and bandwidth.

7. Deploy

Click Deploy. The pod takes a few minutes to start while the image pulls. You'll see it transition to "Running" in your dashboard.

Step 2: Connect via SSH

Once the pod is running, find the "SSH over exposed TCP" connection string in the pod dashboard.

Connect with:

ssh root@<pod-ip> -p <port> -i ~/.ssh/id_ed25519

RunPod also supports VS Code Remote SSH if you prefer an IDE.

Custom template (advanced)

If you're setting up a pod from scratch instead of the FastVideo template, use this image:

ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest

And paste this as the Container Start Command to enable SSH (RunPod docs):

bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"

Step 3: Set Up FastVideo

After SSH'ing in, the uv virtual environment at /opt/venv is already activated (configured in .bashrc and .profile). You land in the /FastVideo directory.

Clone or pull the repo

If the pod already has the FastVideo repo:

cd /FastVideo
git pull

If starting from a blank pod:

git clone https://github.com/hao-ai-lab/FastVideo.git /FastVideo
cd /FastVideo

Install the package

uv pip install -e .[dev]

The Docker image already includes Flash Attention and most heavy dependencies, so this is fast.

Build the custom kernels (optional)

VSA and STA attention kernels aren't in the Docker image by default. Build them if you're working on attention backends or need maximum inference performance:

cd /FastVideo/fastvideo-kernel
./build.sh

The build script detects your GPU architecture automatically. An A100 or H100 takes about 5-10 minutes.

Verify the setup

cd /FastVideo
python -c "import fastvideo; print('OK')"
pytest tests/ -q --no-header

Development Workflow

Editing code on RunPod

Two common approaches:

Option A: Edit on RunPod directly

Use VS Code Remote SSH or vim/nano on the pod. Commit and push when you're ready:

cd /FastVideo
git add .
git commit -m "your change"
git push

Option B: Edit locally, sync to RunPod

Work in your local repo, then pull on the pod:

# On RunPod:
cd /FastVideo
git pull

This keeps your local tools (editor, linters) intact while running GPU workloads on the pod.

Running linters and tests

# Lint
pre-commit run --all-files

# Full test suite
pytest tests/

# Just package tests
pytest fastvideo/tests/ -v

Storing models

If you attached a persistent volume, point your model downloads there:

export HF_HOME=/models/huggingface
export TRANSFORMERS_CACHE=/models/huggingface

Add these to /root/.bashrc so they persist across SSH sessions. The volume survives pod termination, so you only download models once.

Terminating the pod

When you're done, push any commits you want to keep. RunPod does not save pod storage after termination.

Go to your RunPod dashboard, click "Terminate", then "Delete". A pod that's stopped but not deleted still charges you for storage. Fully delete it to stop all charges.

GPU Recommendations

GPU	VRAM	Good for
RTX 4090	24 GB	Inference testing, small model fine-tuning, quick iteration
A40	48 GB	Mid-size training runs, 480p video generation
A100 (40 GB)	40 GB	Multi-GPU inference, training with sequence parallelism
A100 (80 GB)	80 GB	Large model training, 720p+ video generation
H100	80 GB	Heavy training, benchmarking, kernel development

For most development work, a single RTX 4090 or A40 is sufficient and cost-effective. Use A100/H100 when you need to reproduce training results at scale or test multi-GPU features.