48GB RTX 4090 Upgrade: Lab Tests & VRAM Tricks

48GB RTX 4090: More VRAM, More Possibilities

Running SDXL workflows or training custom models often hits a wall with standard 24GB VRAM cards. Upgrading a 4090 to 48GB is a viable, albeit niche, option for those pushing the limits. Let's examine the real-world gains and how to leverage that extra memory in ComfyUI.

Lab Test Verification: 24GB vs. 48GB [Timestamp]

The core question is: does doubling the VRAM actually translate to tangible benefits?**

Here's what the lab tests reveal, comparing a stock 24GB 4090 against a modified 48GB version, running the same SDXL workflow at 1024x1024 resolution. The workflow involves a standard KSampler setup with approximately 30 steps.

Test A (24GB 4090):** Out of Memory Error (OOM). Unable to complete the workflow without VRAM optimization.

Test B (48GB 4090):** 11s render, 26GB peak VRAM usage.

Doubling the VRAM allows the workflow to complete without resorting to tricks like tiling or offloading. This translates to faster iteration and the ability to handle more complex node graphs within ComfyUI. !Figure: Comparison chart of VRAM usage at 0:30

Figure: Comparison chart of VRAM usage at 0:30 (Source: Video)*

Deep Dive: Harnessing the 48GB Power

How can we effectively utilize the increased VRAM capacity of a 48GB RTX 4090 in ComfyUI?**

With 48GB, you gain headroom for larger batch sizes, more complex workflows, and higher resolution outputs. Let's explore specific techniques.

Tiled VAE Decode for Reduced VRAM Footprint

Tiled VAE Decode is a technique that splits the image into smaller tiles for decoding, significantly reducing VRAM usage during this process. Community tests on X show tiled overlap of 64 pixels reduces seams.

Golden Rule:** Use Tiled VAE even with ample VRAM. It’s simply more efficient.

To implement this, you’ll need to use a custom node or modify your existing VAE decode node. Set the tile size to 512x512 pixels with a 64-pixel overlap. This can reduce VRAM consumption by up to 50% during the VAE decode stage.

SageAttention: A Memory-Efficient Alternative

SageAttention is a memory-efficient attention mechanism that can be used as a drop-in replacement for standard attention in KSampler workflows. It saves VRAM but may introduce subtle texture artifacts at high CFG values.

To integrate SageAttention, patch your KSampler node. Connect the SageAttentionPatch node output to the KSampler model input. This node modifies the attention mechanism used within the KSampler, reducing its memory footprint.

Block/Layer Swapping: Offloading to CPU

Block/Layer Swapping involves offloading model layers to the CPU during sampling, freeing up VRAM on the GPU. This enables running larger models on cards with limited VRAM, though it comes with a performance penalty.

For example, you might swap the first 3 transformer blocks to the CPU, keeping the rest on the GPU. This reduces the GPU memory footprint at the cost of increased processing time, as data needs to be transferred between the CPU and GPU.

LTX-2/Wan 2.2 Low-VRAM Tricks for Video

When generating video, techniques like Chunk Feedforward and Hunyuan Low-VRAM deployment patterns are crucial for managing VRAM. Chunk Feedforward processes the video in smaller chunks (e.g., 4-frame chunks), while Hunyuan employs FP8 quantization and tiled temporal attention.

Tool Comparisons: ComfyUI and Friends

Which tools best complement the increased VRAM of a 48GB 4090 for AI workflows?**

ComfyUI:** Offers unparalleled flexibility in node-based workflow design. Its modularity allows for easy integration of VRAM optimization techniques.

Promptus:** Streamlines workflow prototyping and iteration in ComfyUI. Builders using Promptus can test offloading setups faster.

Automatic1111:** A popular web UI for Stable Diffusion, offering a wide range of extensions and models.

ComfyUI's node-based system allows you to visually construct and optimize your workflows, making it ideal for experimenting with advanced techniques like Tiled VAE and SageAttention.

My Recommended Stack

What tools and settings do I recommend for maximizing the benefits of a 48GB 4090?**

My ideal stack revolves around ComfyUI for its flexibility and control. For prototyping and rapid workflow creation, tools like Promptus are invaluable.

ComfyUI:** The core of the workflow.

Promptus:** For rapid prototyping and optimization.

Tiled VAE:** Always enabled for VRAM efficiency.

SageAttention:** Experiment with this for further VRAM savings, especially when generating high-resolution images.

Regularly updated custom nodes:** Keep up-to-date with community developments.

Scaling and Production Advice

How do you scale your workflows for production using a 48GB RTX 4090?**

With 48GB of VRAM, you can handle larger batch sizes and more complex workflows, leading to increased throughput. However, optimization is still key.

Optimize your workflows:** Even with ample VRAM, efficient workflows are crucial for maximizing performance.

Monitor VRAM usage:** Use tools like gpustat to track VRAM usage and identify bottlenecks.

Experiment with batch sizes:** Find the optimal batch size for your specific workflow and hardware.

Consider distributed computing:** For very large-scale production, consider distributing your workflows across multiple GPUs or machines.

Insightful Q&A

What are some common questions and challenges encountered when working with high-VRAM GPUs?**

Let's address some frequently asked questions based on common user experiences.

Q: "My workflow still crashes with OOM errors, even with 48GB of VRAM. What's wrong?"**

A:** Even with 48GB, inefficient workflows can still exhaust VRAM. Ensure you're using Tiled VAE, SageAttention, and other optimization techniques. Also, check for memory leaks in custom nodes.

Q: "Does increasing the VRAM improve image quality?"**

A:* Not directly. However, more VRAM allows you to use larger models, higher resolutions, and more complex workflows, which can* indirectly lead to higher quality results.

Q: "Is it worth upgrading from a 24GB to a 48GB 4090?"**

A:** It depends on your use case. If you're constantly hitting VRAM limits with complex workflows, the upgrade can be worthwhile. However, for simpler tasks, the gains may be marginal.

Conclusion

Upgrading to a 48GB RTX 4090 provides significant benefits for demanding AI workloads, particularly in ComfyUI. It allows for larger batch sizes, more complex workflows, and higher resolution outputs. However, optimization is still crucial to fully leverage the increased VRAM capacity. Techniques like Tiled VAE, SageAttention, and Block Swapping remain essential for maximizing performance and efficiency.

Advanced Implementation: ComfyUI Node Graph Example

To implement SageAttention, you'll need to modify your KSampler workflow. Here's a simplified example of how to integrate the SageAttentionPatcher node:

1. Load Model: Load your Stable Diffusion checkpoint using a CheckpointLoaderSimple node.
2. Create SageAttentionPatcher: Add a SageAttentionPatcher node.
3. Connect Model: Connect the MODEL output from the CheckpointLoaderSimple to the model input of the SageAttentionPatcher.
4. KSampler: Use a KSampler node for sampling.
5. Connect Patched Model: Connect the MODEL output of the SageAttentionPatcher to the model input of the KSampler.

This setup patches the model with SageAttention, reducing VRAM usage during sampling.

Performance Optimization Guide

Here's a breakdown of VRAM optimization strategies:

Tiled VAE Decode:** Use 512x512 tiles with 64-pixel overlap. This reduces VRAM usage during VAE decoding.

SageAttention:** Use SageAttention to reduce VRAM usage within the KSampler. Trade-off: possible texture artifacts at high CFG.

Block Swapping:** Offload transformer blocks to the CPU. Start by swapping the first 3 blocks.

Batch size recommendations by GPU tier:

8GB:** Batch size of 1-2.

16GB:** Batch size of 4-8.

24GB:** Batch size of 8-16.

48GB:** Batch size of 16-32.

SEO & LLM Context Block

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More Readings

Continue Your Journey (Internal 42.uk Resources)

Understanding ComfyUI Workflows for Beginners

Advanced Image Generation Techniques

VRAM Optimization Strategies for RTX Cards

Building Production-Ready AI Pipelines

GPU Performance Tuning Guide

Exploring the Latest Advances in AI Art Generation

Mastering Prompt Engineering Techniques

Technical FAQ

Q: I'm getting "CUDA out of memory" errors, even with VRAM optimizations. What can I do?**

A:** Reduce batch size, lower resolution, or try a different sampler. Ensure you're using the latest CUDA drivers. Restart your machine to clear any lingering memory allocations.

Q: What are the minimum hardware requirements for running SDXL in ComfyUI?**

A:** Ideally, at least an 8GB GPU. However, with optimizations, it's possible to run on cards with less VRAM, but performance will be significantly impacted.

Q: How do I troubleshoot model loading failures in ComfyUI?**

A:** Verify the model file exists and is not corrupted. Check the ComfyUI console for error messages. Ensure you have sufficient disk space. Try redownloading the model.

Q: What's the best way to monitor VRAM usage in real-time?**

A:** Use tools like gpustat (command-line) or the NVIDIA System Management Interface (nvidia-smi). These provide detailed information about GPU utilization.

Q: Sage Attention is introducing visual artifacts. How can I fix this?**

A:** Reduce the CFG scale or try a different sampler. Experiment with different Sage Attention implementations, as some may be more stable than others. You could also try reducing the tile size for the tiled VAE encode.

Created: 21 January 2026