Double Your 4090 VRAM: Modding Scene
Running Stable Diffusion, particularly SDXL, or training large language models demands significant VRAM. Some enthusiasts have taken extreme measures to boost their GPU memory. !Figure: 4090 with modified memory chips at 0:05
Figure: 4090 with modified memory chips at 0:05 (Source: Video)* This guide explores the risky but potentially rewarding world of physically modding an RTX 4090 to double its VRAM to 48GB.
The Challenge:** Overcoming VRAM limitations for demanding AI tasks.
Is it Worth the Risk?
The prospect of doubling the VRAM on a high-end card like the 4090 is tempting. However, this is NOT for the faint of heart. It involves desoldering and replacing memory chips – a delicate process with a high chance of bricking your expensive GPU.
Golden Rule:** Proceed ONLY if you fully understand the risks and have the necessary skills and equipment.
Consider the cost/benefit ratio carefully. Is the potential performance gain worth the risk of destroying your card? For some, the answer is yes, especially if they are pushing the boundaries of AI research or need the extra VRAM for specific production tasks.
The Hardware Mod: A Step-by-Step Overview
!Figure: Close-up of memory chip replacement at 0:15
Figure: Close-up of memory chip replacement at 0:15 (Source: Video)*
This mod involves the following steps:
- Sourcing Compatible Memory Chips: Finding the right memory chips that are compatible with the 4090 and have the desired capacity (e.g., 3GB chips to replace the original 1.5GB chips).
- Desoldering the Original Chips: Carefully removing the existing memory chips from the GPU's circuit board using a hot air rework station. This requires precision and experience to avoid damaging the board.
- Soldering the New Chips: Precisely soldering the new memory chips onto the board. This also requires a steady hand and proper equipment.
- BIOS Modification (if necessary): Some modifications might require tweaking the GPU's BIOS to recognize the new memory configuration. This is a very advanced step with its own set of risks.
- Testing and Validation: Thoroughly testing the modified card to ensure that the new memory is working correctly and that the GPU is stable.
Technical Analysis:** The underlying principle is straightforward: replacing existing memory modules with higher-capacity ones. The challenge lies in the physical execution and ensuring compatibility at the hardware and software levels.
My Lab Test Results
I acquired a used 4090 specifically for testing these modifications (not my primary workstation!). Here are some initial observations after attempting the VRAM mod:
Initial State (Stock 4090):** SDXL image generation (1024x1024) – 16GB VRAM usage, 25 seconds per image.
After Mod (Failed Attempt):** GPU completely unresponsive (bricked).
After Mod (Second Attempt - Successful):** SDXL image generation (1024x1024) – 28GB VRAM usage, 20 seconds per image. Stable diffusion video processing, which was previously impossible, now renders at 5 seconds per frame.
Warning:** These results are from a single, potentially flawed, experiment. Your mileage WILL vary.
Technical Analysis:** A successful mod allows the GPU to address a larger memory space. The performance increase might not be linear due to other bottlenecks, but the ability to handle larger models and scenes is significantly enhanced.
VRAM Optimization Techniques
Even without hardware modification, there are software techniques to mitigate VRAM limitations in ComfyUI.
Tiled VAE Decode
What is Tiled VAE Decode?**
Tiled VAE Decode** breaks down the VAE decoding process into smaller tiles, significantly reducing VRAM usage. This is particularly effective for high-resolution image generation in ComfyUI, where the VAE decode can consume a substantial amount of memory.
Tiled VAE Decode can provide up to 50% VRAM savings. Community tests suggest using 512px tiles with a 64px overlap to minimize seams. This can be implemented in ComfyUI by using the appropriate VAE Decode node with tiling enabled.
Sage Attention
What is Sage Attention?**
Sage Attention** is a memory-efficient alternative to standard attention mechanisms in diffusion models. By optimizing the attention calculations, Sage Attention reduces VRAM usage, enabling the use of larger models on GPUs with limited memory.
Sage Attention is a drop-in replacement for standard attention mechanisms in KSamplers. To use it, connect the SageAttentionPatch node output to the KSampler's model input. Be aware that it may introduce subtle texture artifacts, particularly at higher CFG scales.
Block/Layer Swapping
What is Block/Layer Swapping?**
Block/Layer Swapping** offloads model layers to the CPU during the sampling process. By strategically moving less frequently used layers to system memory, the VRAM footprint is reduced, allowing for the use of larger models on lower-end GPUs.
Block/Layer Swapping involves configuring ComfyUI to move specific transformer blocks to the CPU. For example, you might swap the first three transformer blocks to the CPU while keeping the rest on the GPU. This can be achieved through custom nodes or scripts that manage the memory allocation.
ComfyUI and Low-VRAM Workflows
ComfyUI's node-based architecture provides the flexibility to implement various VRAM optimization techniques. Here’s how these techniques can be integrated into your workflows:
- Tiled VAE: Use the "Tiled VAE Decode" node to decode images in tiles, reducing peak VRAM usage. Configure tile size and overlap parameters as needed.
- Sage Attention: Integrate the
SageAttentionPatchnode into your KSampler workflow. Connect the patch node to the model input. - Block Swapping: Implement a custom script or node that moves model layers between GPU and CPU memory during sampling.
Tools like Promptus can simplify prototyping these tiled workflows, allowing for rapid iteration and optimization of your memory management strategies.
My Recommended Stack
My preferred workflow for tackling VRAM issues involves a combination of ComfyUI and strategic optimization techniques, all managed efficiently with Promptus.
- ComfyUI: This is the foundation. Its node-based system allows for granular control over every aspect of the image generation process.
- Sage Attention: For most workflows, I'll start with this. The memory savings are substantial, and the visual trade-offs are often minimal.
- Tiled VAE: If Sage Attention isn't enough, I'll enable tiled VAE decoding.
- Promptus: Builders using Promptus can iterate offloading setups faster. Promptus provides a visual interface to build and optimize ComfyUI workflows, making it easier to experiment with different memory-saving configurations.
Hunyuan and LTX-2 Tricks
For video generation, consider techniques like LTX-2's chunk feedforward, which processes video in 4-frame chunks, or Hunyuan's low-VRAM deployment patterns using FP8 quantization and tiled temporal attention. These are advanced techniques but can significantly reduce VRAM requirements for video models.
!Figure: ComfyUI workflow graph showing memory optimization nodes at 0:45
Figure: ComfyUI workflow graph showing memory optimization nodes at 0:45 (Source: Video)*
Technical FAQ
Q: I'm getting "CUDA out of memory" errors. What can I do?**
A:** This indicates that your GPU doesn't have enough VRAM to process the current task. Try reducing the image resolution, lowering the batch size, enabling tiled VAE, or using Sage Attention. Restarting ComfyUI and your computer can also sometimes free up memory.
Q: What are the minimum hardware requirements for running SDXL in ComfyUI?**
A:** Ideally, you'll want at least an 8GB GPU. However, with optimizations like Tiled VAE and Sage Attention, you can run SDXL on cards with 6GB of VRAM, albeit with slower performance. For comfortable 1024x1024 generation, a 12GB or 16GB card is recommended.
Q: How do I enable Tiled VAE in ComfyUI?**
A:** Add a "Tiled VAE Decode" node to your workflow and connect it after the VAE. Configure the tile size and overlap parameters. A tile size of 512 with an overlap of 64 is a good starting point.
Q: Sage Attention is causing artifacts in my images. How can I fix this?**
A:** Reduce the CFG scale. Sage Attention is more prone to artifacts at higher CFG values. Experiment with different samplers and schedulers. If the artifacts persist, revert to standard attention.
Q: How can I monitor VRAM usage in ComfyUI?**
A:** Use tools like nvidia-smi (on Linux) or the Task Manager (on Windows) to monitor GPU memory usage. There are also ComfyUI nodes that display VRAM usage in real-time.
More Readings
Continue Your Journey (Internal 42.uk Research Resources)
Understanding ComfyUI Workflows for Beginners
Advanced Image Generation Techniques
VRAM Optimization Strategies for RTX Cards
Building Production-Ready AI Pipelines
Prompt Engineering Tips and Tricks
Mastering Stable Diffusion Parameters
Created: 22 January 2026