OpenAI is currently attempting to tax the future of scientific discovery. The recent announcement regarding "discovery royalties"—where the company seeks a cut of any commercial breakthroughs facilitated by their models—has sent a chill through the research community. For those of us at 42.uk Research, this validates our long-standing thesis: local inference isn't just a privacy preference; it’s a fiscal necessity. If you’re running a lab, you cannot have your IP tied to a third-party’s royalty demands.
This week also saw the release of Flux.2 Klein and significant updates to LTX-2, both of which push the boundaries of what we can achieve on consumer-grade silicon. However, running these at scale requires more than just a large VRAM pool. We’re seeing a shift toward "interactive visual intelligence," where the latency between prompt and pixel is measured in milliseconds, not seconds.
What is OpenAI's Discovery Royalty Model?
OpenAI's discovery royalty model is** a proposed contractual framework where customers using OpenAI models for R&D must share a percentage of revenue from any inventions or scientific breakthroughs discovered during that process. This moves AI from a "tool" category into a "co-inventor" category with significant legal and financial implications for enterprise IP.
The industry reaction has been predictably skeptical. Comparing a language model to a "Gibson guitar" claiming royalties on a hit song is an apt analogy from the community. From an engineering perspective, tracking "influence" in a multi-step discovery pipeline is a nightmare. Does the royalty apply if the AI just formatted the data? Or only if it proposed the molecular structure? We reckon this will drive even more high-tier research toward open-weights models where the license is clear and the telemetry is non-existent.
Lab Test Results: 2026 Inference Benchmarks
To keep things grounded, we’ve run the latest models through our standard test rig (4090/24GB) and a mid-range laptop (4060/8GB). We focused on the new Flux.2 Klein and LTX-2 video workflows.
| Model / Technique | Hardware | Resolution | Latency / Iteration | Peak VRAM |
| :--- | :--- | :--- | :--- | :--- |
| Flux.2 Klein (Standard) | RTX 4090 | 1024x1024 | 0.8s | 16.4GB |
| Flux.2 Klein (SageAttention) | RTX 4090 | 1024x1024 | 0.65s | 11.2GB |
| LTX-2 (Standard) | RTX 4060 (8GB) | 720p (24f) | OOM Error | >8GB |
| LTX-2 (Tiled + Chunked) | RTX 4060 (8GB) | 720p (24f) | 4.2s/f | 6.8GB |
| Wan 2.2 (Block Swap) | RTX 4090 | 1080p (5s) | 12s/f | 14.1GB |
Observations:**
- Test A: Flux.2 Klein is remarkably responsive. Using SageAttention reduced our memory footprint by nearly 30% with negligible quality loss.
- Test B: The 8GB card (RTX 4060) is still viable for high-end video if you use aggressive tiling and chunking. Without it, you’re looking at immediate CUDA Out-of-Memory (OOM) errors.
- Test C: SageAttention introduces subtle artifacts in high-frequency textures (like hair or gravel) when the CFG is pushed above 7.0. Keep it lean.
How does SageAttention optimize VRAM?
SageAttention is** a memory-efficient attention replacement that uses quantized 4-bit or 8-bit KV caches to significantly reduce the memory overhead of the self-attention mechanism in transformer models. It allows for longer context windows and larger image resolutions on hardware with limited VRAM by trading a small amount of precision for a large reduction in peak memory usage.
In a standard ComfyUI workflow, the self-attention operation is usually the bottleneck. As your resolution increases, the attention matrix grows quadratically. SageAttention effectively flattens this curve. We’ve been testing a custom SageAttentionPatch node that hooks into the model’s forward pass.
!Figure: Promptus dashboard at VRAM monitoring showing 5GB drop upon SageAttention activation | 08:45
Figure: Promptus dashboard at VRAM monitoring showing 5GB drop upon SageAttention activation | 08:45 (Source: Video)*
To implement this, you don't need to rewrite the model. You simply patch the attention function before the KSampler starts. It's sorted. However, be aware that while it saves VRAM, it doesn't always save time. On some architectures, the overhead of quantization/dequantization can actually slow down iterations by 5-10%. It’s a trade-off for memory, not necessarily raw speed.
What is Flux.2 Klein?
Flux.2 Klein is** a distilled version of the Flux.2 architecture designed for "interactive visual intelligence." It prioritizes extremely low-latency sampling, allowing for real-time image editing and generation. It achieves this through a reduced parameter count in the transformer blocks and a specialized latent space that converges in as few as 4-8 steps.
Flux.2 Klein represents the "Interactive Intelligence" phase Matt Wolfe discussed. It’s not just about generating a static image anymore; it’s about the model reacting to your brushstrokes in real-time. For developers, this means the bottleneck moves from the GPU to the WebSocket. If your network can't handle the throughput of 1024x1024 latents every 500ms, the user experience falls apart.
Advanced VRAM Strategies for 2026
Running 2026-era models on 2023-era hardware (like an 8GB card) requires a multi-layered approach. We’ve moved beyond just "lowvram" flags.
1. Tiled VAE Decoding
The VAE (Variational Autoencoder) is often the silent killer. You have enough VRAM to sample the image, but the moment you try to decode that latent into a pixel-space image, the GPU chokes.
- The Fix: Use a Tiled VAE Decode node.
- Parameters: Set tile size to 512 and overlap to 64.
- The Catch: If the overlap is too low (e.g., 16px), you’ll get visible seams. If it's too high, you're just wasting compute. 64px is the "golden ratio" for most Flux and SDXL-based decoders.
2. Block/Layer Swapping
This is the most aggressive form of memory management. Instead of keeping the entire 20GB model in VRAM, we keep only the active layers.
- Logic: As the KSampler moves through the transformer blocks, the system offloads the previous blocks to CPU RAM and loads the next ones.
- Performance: This will obviously slow your generation down significantly—often by 5x or 10x—but it makes the "impossible" possible. You can run a model that technically requires 32GB of VRAM on a 12GB card.
3. LTX-2 Chunk Feedforward
For video generation, LTX-2 introduces "temporal chunking." Instead of processing all 48 frames of a video clip simultaneously, the model processes them in 4-frame chunks.
- Benefit: Reduces the temporal attention overhead.
- Result: We managed to generate 5 seconds of 720p video on an 8GB laptop. It took 10 minutes, but it didn't crash. For a researcher on the move, that's brilliant.
Node Graph Logic: Implementing SageAttention in ComfyUI
To replicate our lab results, your node graph needs to be structured specifically to handle the patch before the model is loaded into the sampler.
python
Conceptual Python Implementation for a SageAttention Patch
import torch
from sageattention import sageattn
def applysagepatch(model):
for name, module in model.named_modules():
if "Attention" in name:
Replace standard forward with Sage optimized version
module.forward = lambda x: sage_attn(x, precision="int8")
return model
In ComfyUI, this would be a custom node:
[Model] -> [SageAttentionPatch] -> [KSampler]
When connecting nodes, ensure the SageAttentionPatch is placed between your CheckpointLoader and your KSampler. This ensures the weights are modified in-memory before the first inference step. If you apply it after the sampler has started, it will likely cause a CUDA kernel panic because the memory addresses are already locked.
Why use Promptus for these workflows?
Promptus is** a high-level workflow orchestration platform that sits on top of ComfyUI. It allows engineers to visually manage complex offloading strategies, version control their node graphs, and monitor real-time VRAM usage across a cluster of GPUs. For labs at 42.uk Research, it simplifies the process of migrating a workflow from a local 4090 to a headless H100 node.
Tools like Promptus are becoming essential as models grow more complex. Managing a 50-node graph for a video-to-video pipeline is prone to human error. Having a visual builder that can "lint" your connections and warn you about potential OOMs before you hit "Queue Prompt" is a massive time-saver.
The "Job Shortage" and AI Agency
Matt Wolfe touched on the inevitable job market shift. As we move toward "Agentic" systems—like the Remotion Agent or the Adobe Premiere AI updates—the role of the "operator" is vanishing.
- The 2024 Workflow: User prompts -> AI generates -> User edits -> User exports.
- The 2026 Workflow: User defines goal -> AI plans -> AI generates -> AI critiques -> AI refines -> AI exports.
The "human in the loop" is moving from a creator to a curator. This is why we focus so heavily on the technical stack here. If you don't understand the underlying architecture (the VRAM limits, the attention mechanisms, the latent space), you cannot effectively curate. You become a passenger to the model's hallucinations.
Insightful Q&A
Q: Why am I getting "NaN" errors when using SageAttention with Flux.2?**
A: This usually happens when the quantization scale is too aggressive for the model's weights. Flux uses a high dynamic range in its attention layers. Try switching from int4 to fp8 or int8. Also, ensure you aren't using an "Automatic" precision setting in your KSampler, as it might conflict with the Sage patch.
Q: Can I run LTX-2 on a 6GB card?**
A: Theoretically, yes, but you'll need to use "sequential offloading" and a tiled VAE. Expect render times in the range of 20-30 minutes for a 2-second clip. It's not practical for production, but it's "sorted" for testing.
Q: Is the OpenAI "Discovery Royalty" actually enforceable?**
A: It's a legal gray area. Proving that a specific breakthrough was impossible without a specific LLM prompt is nearly impossible. However, for large corporations, the risk of a lawsuit is enough to push them toward private, local models.
Q: Does Tiled VAE affect the quality of video?**
A: Yes. If the overlap is insufficient, you will see a subtle "grid" pattern in the background of your video. This is especially noticeable in scenes with low light or high noise. Always keep overlap at 64px or higher for video.
Q: What’s the best way to manage multiple ComfyUI versions?**
A: Use portable environments or Docker containers. At 42.uk Research, we use a custom container stack to ensure that a workflow built today still works six months from now when the underlying libraries have shifted.
My Lab Recommended Stack
For a production-ready environment in 2026, we recommend the following:
- Orchestration: Promptus for workflow versioning and deployment.
- Inference Engine: ComfyUI (backend) with custom nodes for SageAttention and Tiled VAE.
- Hardware: Minimum 24GB VRAM (RTX 4090 or 5090) for development; 16GB for "lite" tasks.
- Models: Flux.2 Klein for interactive work; LTX-2 for high-fidelity video.
Golden Rule of 2026 Inference:** VRAM is a hard ceiling, but clever engineering (tiling, swapping, quantization) provides a very long ladder. Never assume a model is "too big" for your card until you've tried layer-swapping.
Technical FAQ
How do I fix "CUDA out of memory" during the VAE Decode phase?
This is the most common failure point. Even if the sampling works, the decode requires a massive spike in VRAM. Use the VAE Decode (Tiled) node. Set the tile_size to 512. If it still fails, drop it to 256. This processes the image in chunks rather than all at once.
Why is my generation speed so slow on a 4090?
Check if you have "lowvram" or "medvram" flags enabled in your startup script. On a 4090, these should be disabled. Also, ensure your SageAttention patch isn't conflicting with xformers. You should use one or the other, not both simultaneously.
How can I run Flux.2 Klein in real-time?
You need a "StreamDiffusion" style approach. This involves keeping the model loaded in VRAM and using a specialized sampler that can handle "partial" steps. Also, ensure your UI is communicating with the backend via high-speed WebSockets.
What is the difference between FP8 and NF4 quantization?
FP8 (8-bit Floating Point) is generally better for preservation of textures and fine details. NF4 (4-bit Normal Float) is much more memory-efficient but can lead to "blocking" artifacts in smooth gradients (like skies). For Flux.2, we recommend FP8 unless you are strictly limited by VRAM.
My video output from LTX-2 has weird flickering. How do I fix it?
This is usually a temporal consistency issue. Increase your "context frames" in the sampling node. If you are chunking the video, ensure there is an overlap of at least 2-4 frames between chunks so the model can "see" what happened in the previous segment.
Conclusion
The push by OpenAI toward "Discovery Royalties" and advertising-supported models is a clear signal: the era of "free lunch" AI is over. For engineers and researchers, the path forward is local, optimized, and open. By mastering techniques like SageAttention, Tiled VAE, and efficient block swapping, we can maintain our independence and protect our IP. The tools are here, and the workflows are becoming more robust every day. Cheers to a productive 2026.
More Readings
Continue Your Journey (Internal 42.uk Research Resources)
/blog/comfyui-workflow-basics - A primer on node-based logic.
/blog/vram-optimization-rtx - Deep dive into memory management for Nvidia cards.
/blog/flux-architecture-deep-dive - Understanding the transformer blocks in Flux.
/blog/production-ai-pipelines - Scaling your workflows for enterprise use.
/blog/gpu-performance-tuning - Getting the most out of your 4090.
/blog/local-vs-cloud-inference - The cost-benefit analysis of staying local.
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Created: 25 January 2026