Ollama Prompt Encode
A prompt generator and CLIP encoder using AI provided by Ollama.
Quick Technical Summary: Ollama Prompt Encode
- Base VRAM Footprint:
- 256 MB (4 GB Tier)
- Primary Dependencies:
- ollama==0.4.2
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/ScreamingHawk/comfyui-ollama-prompt-encode
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Ollama Prompt Encode require?
Direct Answer: The ComfyUI node Ollama Prompt Encode requires a minimum base VRAM of 256MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 256MB (0.3GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- HIGH
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for Ollama Prompt Encode:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
Tired of renting cloud rigs? Run ComfyUI locally with absolute zero latency. Best entry-level ComfyUI experience. Avoids immediate VRAM limitations on basic LoRA training.
Are you the author of this node?
Help your users avoid out-of-memory errors by displaying this professional, dynamic VRAM badge on your GitHub README. Copy the markdown below to embed it with a backlink directly to this hardware specification profile.
What Python packages are required for Ollama Prompt Encode?
Direct Answer: Running Ollama Prompt Encode requires installing the following Python package dependencies: ollama==0.4.2. Ensure your ComfyUI environment has these packages active before launching.
ollama==0.4.2Interactive Setup & Dependency Resolver
# Loading command...Frequently Asked Questions
How much VRAM does Ollama Prompt Encode require?
Ollama Prompt Encode requires a minimum of 256MB (0.3GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Ollama Prompt Encode on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.2GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.4GB headroom
How much VRAM does Ollama Prompt Encode take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Ollama Prompt Encode runs smoothly on an RTX 3060 (12GB) with 10.6GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 21.4GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What Python packages are required for Ollama Prompt Encode?
To run Ollama Prompt Encode, you need to install: ollama==0.4.2. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running Ollama Prompt Encode?
Ollama Prompt Encode supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Ollama Prompt Encode in ComfyUI?
To install Ollama Prompt Encode: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/ScreamingHawk/comfyui-ollama-prompt-encode, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.