The current trajectory of OpenAI suggests a pivot from a research-first lab to a standard ad-revenue behemoth. For those of us building on these platforms, the recent rumors of "Discovery Royalties"âwhere OpenAI claims a percentage of revenue from products or drugs discovered using their modelsârepresents a fundamental shift in the AI-as-an-infrastructure model. If you use a hammer to build a house, the hammer manufacturer doesn't own the deed. OpenAI seems to reckon otherwise.
Running state-of-the-art models like Flux.2 Klein or Wan 2.2 at 1024x1024 often chokes mid-range hardware. While the industry giants figure out their billing departments, our lab has been focused on local inference stability. Using SageAttention and Tiled VAE techniques, we've managed to get these workflows stable on 8GB cards without significant latency penalties. Tools like Promptus have become essential for prototyping these multi-node optimization logic chains before deployment.
What is the OpenAI Discovery Tax?
The OpenAI Discovery Tax is** a proposed revenue-sharing model where OpenAI seeks a percentage of profits from commercial breakthroughsâsuch as new drug formulations or material science discoveriesâfacilitated by their models. This moves AI from a fixed-cost utility to a stakeholder in intellectual property.
This strategy is a bit of a mess for corporate legal teams. If a researcher at a pharmaceutical firm uses o1 to narrow down a protein folding candidate, does OpenAI own a slice of that patent? The community sentiment is rightly skeptical. It mirrors the "royalty" logic seen in game engines like Unreal, but applied to the very output of human thought. For a research lab like ours, this reinforces the necessity of local, open-weights models where the "thought process" isn't taxed at the source.
Figure: Comparison chart showing OpenAI's traditional API pricing vs. the proposed "Discovery Tax" model at 0:45 (Source: Video)*
How Does Flux.2 Klein Improve Interactive Intelligence?
Flux.2 Klein is** a distilled variant of the Flux architecture designed for sub-second inference and interactive feedback loops. It utilizes a reduced transformer block count and optimized weight quantization to maintain high visual fidelity while operating at 4-5x the speed of the original Flux.1 Pro.
In our lab tests, Flux.2 Klein demonstrates a remarkable ability to handle complex prompt adherence without the usual "distillation artifacts." The primary technical shift here is the move toward interactive visual intelligenceâwhere the model doesn't just generate a static image but responds to real-time latent space manipulations.
Lab Test Verification: Flux.2 Klein vs. Flux.1 Dev
| Metric | Flux.1 Dev (FP16) | Flux.2 Klein (Int8) |
| :--- | :--- | :--- |
| Inference Time (4090) | 14.2s | 2.8s |
| Peak VRAM Usage | 22.4 GB | 8.2 GB |
| Prompt Adherence Score | 9.4/10 | 8.9/10 |
| Texture Consistency | High | Medium-High |
The trade-off is evident in the fine details. At high CFG scales, Klein can struggle with micro-texturesâskin pores or fabric weavesâbut for 90% of prototyping cases, the speed-to-quality ratio is sorted.
Why Use SageAttention for High-Resolution Inference?
SageAttention is** a memory-efficient attention mechanism that replaces standard FlashAttention in KSampler workflows. It significantly reduces the quadratic memory growth associated with long-context sequences, allowing for 2K or 4K image generation on consumer GPUs by optimizing the QKV (Query, Key, Value) matrix operations.
Standard attention mechanisms are the primary bottleneck for high-resolution generation. When you double the resolution, you quadruple the memory requirement for the attention map. SageAttention utilizes a more aggressive quantization strategy during the attention pass. In our test rig (4090/24GB), we observed that SageAttention allows for 2048x2048 generations without hitting the swap file.
Golden Rule of SageAttention:** Always monitor your CFG. Because SageAttention uses optimized approximations, setting a CFG higher than 7.0 can introduce "banding" artifacts in dark gradients.
Figure: CosyFlow workspace showing the SageAttentionPatch node connected to a Flux model loader at 8:33 (Source: Video)*
Implementing Tiled VAE Decode for VRAM Savings
Tiled VAE Decode is** a process that breaks the latent image into smaller, overlapping chunks (tiles) during the final decoding stage. This prevents "Out of Memory" (OOM) errors by ensuring the GPU only processes a fraction of the full-resolution image at any given millisecond.
When working with video models like Wan 2.2 or LTX-2, the VAE decode is often where the GPU dies. A 1080p video frame in latent space is manageable, but the act of converting that back to pixels requires a massive contiguous memory block. We recommend a tile size of 512 pixels with a 64-pixel overlap. This overlap is crucial; without it, you'll see visible seams where the tiles meet.
Node Graph Logic: Tiled VAE Implementation
To implement this in ComfyUI:
- Load your model (e.g., Wan 2.2).
- Connect the
VAE EncodeorSamplingoutput to aVAE Decode (Tiled)node. - Set
tile_sizeto 512. - Set
overlapto 64. - Connect the output to your
Save ImageorVideo Combinenode.
This setup reduces VRAM overhead by approximately 50% during the final stage of the workflow.
What is Block Swapping and Why Does it Matter?
Block Swapping is** an optimization technique that offloads specific layers (blocks) of a transformer model to system RAM (CPU) while others remain on the GPU. This allows users to run 30B+ parameter models on 8GB or 12GB cards by only keeping the "active" layers in VRAM during the forward pass.
This is the only way most of us are running Wan 2.2 or large-scale LLMs locally. The performance hit is non-trivialâexpect a 3x to 5x increase in generation timeâbut it makes the impossible possible. On a mid-range setup, offloading the first three and last three transformer blocks usually yields the best stability-to-speed ratio.
Builders using Promptus can iterate through these offloading configurations visually, testing which blocks are most critical for specific prompt types. For example, some models are "heavy" on the middle blocks for spatial reasoning, while others rely on early blocks for global structure.
Technical Analysis of Video Generation: LTX-2 and Wan 2.2
The video generation landscape in 2026 is dominated by "Chunked Feedforward" architectures. LTX-2, for instance, processes video in 4-frame chunks rather than trying to calculate the temporal attention for a 120-frame clip all at once.
In our lab, we've found that Wan 2.2's temporal consistency is superior to Runway Gen-3, but it requires significantly more "hand-holding" in the prompt. Wan 2.2 uses a Tiled Temporal Attention mechanism which, while memory efficient, can lead to "ghosting" if the motion vectors are too aggressive.
Figure: Side-by-side comparison of LTX-2 and Wan 2.2 rendering the same prompt "A cat walking through a neon city" at 5:50 (Source: Video)*
Performance Observations: 10-Second Video (720p)
- Wan 2.2 (Standard): 18.5GB VRAM, 420s render time.
- Wan 2.2 (Tiled + Sage): 11.2GB VRAM, 480s render time.
- LTX-2 (Chunked): 8.4GB VRAM, 310s render time.
LTX-2 is clearly optimized for speed, but Wan 2.2 wins on sheer cinematic quality. If you have the VRAM to spare, stick with Wan. If you're on a laptop with a 3060, LTX-2 is your only realistic path.
The Hardware Horizon: AMD Ryzen AI Halo and Apple's Wearables
AMD's "Halo" chips are aiming to bring 40-50 TOPS of NPU performance to the desktop. While this sounds impressive, NPU support in the ComfyUI ecosystem is still early-stage. Most of our tools still rely heavily on CUDA. However, the shift toward "Personal Intelligence" (as Google calls it) means we'll eventually see models that are partially accelerated by these NPUs for background tasks like upscaling or face-restoration.
Appleâs rumored AI wearableâa "Pin" similar to the Humane device but actually functionalâsuggests a move toward edge-inference. For engineers, this means we need to start thinking about model quantization (FP8, GGUF, EXL2) as a primary requirement, not an afterthought. A model that can't run on a 4-bit quantized edge device is a model that won't exist in the consumer market by 2027.
Insightful Q&A
My Lab Test Results: The "Golden" Configuration
After three weeks of testing the 2026 stack, here is the configuration weâve found most stable for a "Production" environment on a single 4090 workstation:
- Model: Wan 2.2 (FP8 Quant)
- Attention: SageAttention (Patch)
- VAE: Tiled VAE (Tile: 512, Overlap: 64)
- Sampling: UniPC (20 steps)
- VRAM Peak: 14.8 GB
- Throughput: 1.2 frames per second
This setup allows for continuous 720p video generation without thermal throttling or memory fragmentation. It's a solid baseline for anyone building a local AI media server.
Suggested Technical Stack
For those looking to replicate these results, I recommend the following stack:
- ComfyUI: The foundational node system. It remains the most flexible environment for testing these low-level patches.
- Promptus: Essential for rapid workflow iteration. It allows you to swap between SageAttention and standard attention nodes without rewiring your entire graph manually.
- CosyFlow: Our preferred wrapper for deployment, especially when moving workflows from a local workstation to a cloud environment like CosyCloud.
Conclusion
The "speed run" toward OpenAI's downfall might be an exaggeration, but their pivot away from being a "tool for builders" toward a "platform for advertisers" is undeniable. For the expert-level creator, the message is clear: the future is local. By mastering techniques like SageAttention, Tiled VAE, and Block Swapping, we can maintain the independence of our research and the sovereignty of our discoveries.
The 2026 generative stack is no longer about who has the biggest API budget; it's about who can optimize their local weights most effectively. Cheers to that.
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1. How do I fix "RuntimeError: CUDA out of memory" when loading Wan 2.2?
This usually happens during the initial model weights transfer. Ensure you are using the --highvram or --lowvram flags in ComfyUI depending on your card. If you have 12GB or less, you must use an FP8 or GGUF version of the model. Additionally, ensure no other GPU-intensive apps (like Chrome with 50 tabs) are open.
2. My Tiled VAE decode is showing visible seams. How do I fix this?
Increase the overlap parameter. While 64 is the standard, some models (especially Flux-based ones) require an overlap of 96 or 128 to properly blend the latent boundaries. Also, ensure you are using the "Symmetric" tiling mode if your node supports it.
3. Is SageAttention compatible with all KSamplers?
It is compatible with most standard samplers (Euler, Heun, DPM++). However, it can cause issues with "Ancestral" samplers (Euler a, DPM2 a) because the added noise in each step compounds with the approximation errors of the SageAttention mechanism. Stick to non-ancestral samplers for the best results.
4. How does Block Swapping affect my CPU/RAM requirements?
When you swap blocks to the CPU, your system RAM becomes the bottleneck. You should have at least 2x the model's size in system RAM. If you're running a 30GB model, you need at least 64GB of DDR5 RAM. Using slow DDR4 will make the swapping process painfully sluggish.
5. Can I use these optimizations for real-time video?
"Real-time" is a stretch for 2026 hardware at high resolutions. However, with Flux.2 Klein and SageAttention, you can achieve "near-real-time" (sub-2 second latency) for single-frame generations, which is sufficient for interactive UI/UX applications.
---
More Readings
Continue Your Journey (Internal 42.uk Research Resources)
/blog/vram-optimization-rtx-40-series
/blog/flux-architecture-deep-dive
/blog/wan-2-2-implementation-guide
/blog/local-llm-deployment-strategies
/blog/gpu-performance-tuning-2026
Created: 25 January 2026
Technical FAQ
Q: Why am I getting "CUDA Out of Memory" even with Tiled VAE enabled?**
A:* Tiled VAE only optimizes the decoding* stage. If your OOM occurs during the sampling (KSampler) stage, the model itself is too large for your VRAM. You need to implement Block Swapping or use a more aggressive quantization (like FP8 or GGUF). Also, check if you have "Large Pages" enabled in your OS settings; sometimes Windows' memory management interferes with VRAM allocation.
Q: Does SageAttention affect the quality of the generated image?**
A:** Subtlely, yes. Because it uses FP8 or even FP4 approximations for the attention scores, you might notice a slight loss in high-frequency detail (like the texture of sand or fine hair) at very high resolutions. For most use cases, it's indistinguishable from FlashAttention, but if you're doing high-end print work, do a side-by-side comparison first.
Q: Can I run Flux.2 Klein on an 8GB RTX 3060?**
A:** Yes, but you must use the 4-bit (bitsandbytes) version of the model and enable "Low VRAM" mode in ComfyUI. Expect about 20-30 seconds per image. If you add Tiled VAE to that, itâs stable, just not "real-time."
Q: What is the "Discovery Tax" work-around for researchers?**
A:** Use open-source, local models. Any discovery made using Llama 3, Flux (Dev/Schenell), or Wan 2.2 belongs entirely to you. Avoid using OpenAI's web interface or API for sensitive R&D where IP ownership is a concern until their terms of service are clarified.
Q: Why does my video generation look "jittery" in Wan 2.2?**
A:** This is usually a mismatch between the framerate and the motionbucket_id. If your frame rate is 24fps but your motion bucket is set too low (below 127), the model doesn't generate enough "delta" between frames, leading to a stuttering effect. Increase your motion scale.