OpenAI's Monetization Pivot and the 2026 Local VRAM...

OpenAI's Monetization Pivot and the 2026 Local VRAM Optimization Stack

OpenAI is currently attempting to pivot from a pure R&D and subscription model to a more aggressive, enterprise-focused royalty structure. This shift, combined with the launch of "ChatGPT Go" and the integration of advertising, marks a significant departure from the original nonprofit-turned-capped-profit mission. Simultaneously, the open-source community is countering the rising compute demands of models like Flux.2 Klein and LTX-2 with sophisticated memory management techniques. For engineers operating in the ComfyUI ecosystem, maintaining performance on mid-range hardware now requires more than just standard xformers; it demands specific attention to tiled decoding and layer-swapping logic.

What are OpenAI Discovery Royalties?

OpenAI Discovery Royalties are** a proposed revenue-sharing model where OpenAI claims a percentage of profits from discoveries (such as new pharmaceuticals or materials) made using their frontier models. This shift suggests a move away from "software-as-a-service" toward a "partnership-as-a-service" model, effectively taxing the intellectual output generated by AI agents.

The technical implications of this are significant. If OpenAI intends to track "discoveries," we can anticipate more aggressive telemetry and perhaps a more rigid "agentic" architecture where the model's output is logged and audited for commercial value. From an engineering standpoint, this is a licensing nightmare. One community member noted that this is akin to Gibson guitars claiming royalties on every song written with a Les Paul. It represents a fundamental misunderstanding of tool-based utility.

The ChatGPT Go Launch and the Advertising Shift

OpenAI’s introduction of ChatGPT Go [0:10] and their subsequent "approach to advertising" indicates that the $20/month subscription model may have hit a ceiling. ChatGPT Go appears to be a lightweight, mobile-first implementation designed for high-concurrency and low-latency interactions.

However, the more concerning development is the pivot toward ads. DeepMind’s CEO expressed surprise at the speed of this rollout [4:50], suggesting that even Google—the king of ad-tech—thinks OpenAI is rushing it. For developers, this means the API might soon see tiered latency based on whether you're willing to accept "sponsored tokens" or "branded suggestions" within the context window.

!Figure: CosyFlow workspace showing a mock-up of an agentic workflow with integrated telemetry nodes at 0:45

Figure: CosyFlow workspace showing a mock-up of an agentic workflow with integrated telemetry nodes at 0:45 (Source: Video)*

Flux.2 Klein: Towards Interactive Visual Intelligence

The launch of Flux.2 Klein [8:33] represents a shift toward "interactive visual intelligence." Unlike previous diffusion models that focused on static generation, Klein is optimized for real-time manipulation. This requires a rethink of the standard KSampler pipeline.

Why Flux.2 Klein is different

1. Latent Consistency: It maintains spatial coherence during iterative edits better than Flux.1.
2. Reduced Step Count: It achieves convergence in 4-8 steps without the usual quality degradation seen in distilled models.
3. Memory Footprint: While optimized, it still pushes 8GB cards to their limits without specific optimizations.

To run Klein effectively on a 3080 or a 4070, we need to implement SageAttention and specific VAE tiling strategies. Tools like Promptus simplify prototyping these tiled workflows, allowing engineers to visual the memory overhead before committing to a long render.

Implementing SageAttention in ComfyUI

SageAttention is** a memory-efficient attention replacement that significantly reduces the VRAM overhead during the self-attention phase of the diffusion process. It works by quantizing the attention scores and using a more efficient kernel for the softmax operation.

Technical Analysis: Why it works

Standard scaled dot-product attention scales quadratically with sequence length. In high-resolution generations (2k+), the attention matrix becomes the primary bottleneck. SageAttention implements a "tiled" approach to the attention calculation itself, ensuring that only the necessary chunks of the matrix are in the GPU's L1/L2 cache at any given time.

Implementation Logic

In ComfyUI, you don't just "turn it on." You need to patch the model at the transformer block level.

python

Conceptual logic for patching a model with SageAttention

def applysageattention(model):

for block in model.diffusionmodel.transformerblocks:

block.attn1.processor = SageAttentionProcessor(

heads=block.attn1.heads,

dimhead=block.attn1.dimhead,

dropout=0.0

)

return model

In the node graph, you would use a SageAttentionPatch node, connecting the output of your Load Checkpoint node to the model input of the patcher, then pass that to your KSampler.

Golden Rule:** SageAttention is brilliant for VRAM savings, but it may introduce subtle texture artifacts at high CFG values (> 7.0). If you see "banding" in your shadows, revert to standard attention or lower your CFG.

Tiled VAE Decode: The 50% VRAM Fix

Running LTX-2 or Wan 2.2 at 1024x1024 often results in an Out of Memory (OOM) error during the final decoding stage, even if the sampling was successful. This is because the VAE decoder needs to hold the entire latent space in memory to produce the final pixels.

Tiled VAE Decode is** a technique that breaks the latent image into smaller tiles (e.g., 512x512 pixels), decodes them individually, and then stitches them back together.

My Lab Test Results

Hardware: Test rig with an RTX 3080 (10GB VRAM)*

| Technique | Resolution | Peak VRAM | Status |

| :--- | :--- | :--- | :--- |

| Standard Decode | 1024x1024 | 11.2 GB | FAILED (OOM) |

| Tiled Decode (512px) | 1024x1024 | 6.4 GB | SUCCESS |

| Tiled Decode (256px) | 2048x2048 | 8.1 GB | SUCCESS |

Technical Analysis: The key to a successful tiled decode is the overlap**. If you use 0 overlap, you will see visible seams. I reckon a 64-pixel overlap is the sweet spot for 2026-era models. This ensures the VAE has enough context from neighboring tiles to maintain color consistency and edge alignment.

!Figure: Comparison of tiled vs. non-tiled VAE decode showing seam artifacts at low overlap at 14:20

Figure: Comparison of tiled vs. non-tiled VAE decode showing seam artifacts at low overlap at 14:20 (Source: Video)*

Runway Gen-4.5 vs. LTX-2: The Video War

The video generation space is becoming increasingly bifurcated between closed API-based models like Runway Gen-4.5 [4:50] and open-weights models like LTX-2.

Runway Gen-4.5 has introduced "audio-to-video" capabilities, which LTX Studio is also pushing [6:00]. From an architectural perspective, this involves cross-modal attention where the audio embeddings (likely from a model like CLAP) guide the temporal consistency of the video diffusion.

For those of us running local rigs, LTX-2 is the more interesting target. To make it viable on a 4090 or even a 3090, we use Chunked Feedforward.

Chunked Feedforward in LTX-2

Instead of processing all 128 frames of a video at once in the transformer's feedforward layers, we chunk them into groups of 4 or 8.

1. Input: 128-frame latent tensor.
2. Process: Reshape to (16, 8, ...) where 16 is the number of chunks.
3. Loop: Iterate through chunks, offloading the inactive ones to CPU RAM.
4. Reconstruct: Concatenate and move back to GPU.

This significantly increases render time but allows you to generate long-form video that would otherwise require an H100 cluster. Builders using Promptus can iterate these offloading setups faster by toggling chunk sizes in the UI without re-writing the underlying Python.

The "Cosy" Ecosystem: A Practical Stack

When we talk about production-ready AI, we're looking at more than just a single node. Welcome to the Cosy ecosystem, which integrates CosyFlow (the streamlined ComfyUI experience), CosyCloud for remote compute, and CosyContainers for isolated environment management.

In my test rig, I've found that the most stable stack for 2026 workflows involves:

• UI: CosyFlow (for the workflow management).
• Orchestration: Promptus (for rapid prototyping and node-logic visualization).
• Backend: ComfyUI with the SageAttention and TiledVAE custom nodes installed.

[DOWNLOAD: "Ultra-Low VRAM Video Workflow" | LINK: https://cosyflow.com/workflows/low-vram-video-production]

Box Extract and the Utility of LLMs

The video mentioned Box Extract [7:16], a tool for structured data extraction. While it's a proprietary service, the underlying logic is something we've been implementing locally using Qwen3-7B.

The community sentiment regarding Gemini is that it lacks basic organizational tools like "projects" or "folders." This is a recurring theme: the frontier models are brilliant, but the UX is often sorted by people who don't actually use the tools for large-scale engineering. Local workflows in ComfyUI allow us to build our own "projects" by saving entire node graphs as JSON templates.

Hardware: AMD Ryzen AI Halo and the Rise of Wearables

We're seeing a massive push into physical AI. AMD's Ryzen AI Halo [21:45] is promising NPU performance that might finally make local LLMs viable on laptops without a dedicated GPU.

Apple is reportedly developing an AI wearable pin [22:14], and OpenAI is rumored to be doing the same [22:43]. This is a pivot toward "ambient intelligence." Technically, this requires:

• Low-power ASR (Automatic Speech Recognition): Likely a distilled version of Whisper.
• On-device VLM (Vision Language Model): To "see" what the user sees.
• High-speed telemetry: Sending compressed embeddings to the cloud for heavy processing.

The "Job Shortage" mentioned at [25:57] is a direct result of these agents becoming competent enough to handle multi-step reasoning tasks. If an AI pin can handle your scheduling, email triage, and basic data entry, the demand for entry-level administrative roles will plummet.

Suggested Technical Implementation: Block Swapping

For those running models like Hunyuan or large Flux variants on 8GB or 12GB cards, block swapping is the final frontier before OOM.

{
  "node_id": "15",
  "class_type": "ModelPatcherBlockSwap",
  "inputs": {
    "model": [
      "1",
      0
    ],
    "swap_threshold": 0.4,
    "blocks_to_cpu": 12,
    "use_pinned_memory": true
  }
}

Technical Analysis:** By setting a swapthreshold, you tell the patcher to keep the most active 40% of transformer blocks on the GPU and offload the rest to system RAM. The usepinned_memory flag is crucial here; it allows the GPU to access CPU memory directly (via PCIe), bypassing the standard overhead of the OS memory manager. It's slower than VRAM but faster than a full swap.

Insightful Q&A

Q: Why is my LTX-2 render taking 4x longer with SageAttention?**

A: SageAttention itself shouldn't cause a 4x slowdown. Check if you've accidentally enabled "CPU Offloading" for the entire model. SageAttention is a memory efficiency play, not necessarily a speed play. If your VRAM is near 95%, the driver might be "spilling" into system memory, which is where the slowdown occurs.

Q: Can I use Tiled VAE for video?**

A: Yes, but you must use Temporal Tiling. If you tile spatially (X/Y) without considering the temporal (T) axis, you'll get flickering between tiles across frames. Use a node specifically designed for VAE Video Decoding that handles the 3D latent tensor.

Q: Is "ChatGPT Go" just a wrapper for GPT-4o-mini?**

A: It's likely more optimized than a simple wrapper. We suspect a custom quantization scheme (perhaps FP4 or even binary weights for certain layers) to allow it to run with minimal latency on mobile hardware.

Q: How do I fix the "Gibson guitar" royalty issue in my own enterprise?**

A: Move to open-weights models. By using Flux.1 (Dev/Schnell) or LTX-2 locally, you own the weights and the output. OpenAI's move toward royalties is the strongest argument yet for local, sovereign AI infrastructure.

Q: What is the best way to handle "Prompted Playlists" like Spotify's?**

A: This is essentially a RAG (Retrieval-Augmented Generation) problem. The system takes your prompt, converts it to an embedding, and searches a vector database of track metadata. You can replicate this locally with a ChromaDB instance and your own MP3 library.

My Recommended Stack

For a senior engineer looking to stay ahead in 2026, I reckon this is the "Golden Stack":

1. Hardware: RTX 4090 (24GB) or dual 3090s via NVLink for large model parallelization.
2. Software: CosyFlow as the primary interface. It handles the "Welcome to the Cosy ecosystem" onboarding and manages your CosyCloud instances for when you need to scale beyond your local rig.
3. Optimization: SageAttention for sampling, Tiled VAE for decoding, and Promptus for workflow visualization.
4. Models: Flux.2 Klein for images, LTX-2 for video, and Qwen3 for text-based agentic logic.

Technical FAQ

Q: I’m getting "CUDA Error: out of memory" even with SageAttention and Tiled VAE. What’s the next step?**

A: Check your weight_dtype. If you are running in FP16, try switching to BF16 (if your hardware supports it) or FP8. FP8 quantization reduces the model's memory footprint by 50% with negligible quality loss in most diffusion tasks. Also, ensure no other processes (like a web browser with hardware acceleration) are hogging VRAM.

Q: My Tiled VAE output has visible grid lines. How do I fix this?**

A: Increase the tile_overlap. If you're at 32px, move to 64px or 96px. Also, ensure you are using the VAE Encode (Tiled) node rather than just a standard VAE Encode inside a loop. The dedicated tiled node handles the padding and blending of tile edges more gracefully.

Q: Does SageAttention work with SD 1.5 or SDXL?**

A: Yes, but the gains are minimal. SageAttention shines when the sequence length is high. SD 1.5 uses a 512x512 latent space, which is small enough that standard attention is already quite fast. You’ll see the real benefits on 1024x1024 (SDXL) and 2048x2048 (Flux) resolutions.

Q: How do I implement "Block Swapping" in a standard ComfyUI install?**

A: You’ll need a custom node like ComfyUI-Manager to install the "Model Patcher" suite. Once installed, look for the ModelPatcherBlockSwap node. Connect it between your Load Checkpoint and your KSampler. Set the blockstocpu to 10-15 for a 12GB card.

Q: Is there any way to speed up the "Chunked Feedforward" for video?**

A: The bottleneck is the PCIe bandwidth when moving chunks between CPU and GPU. If you're on an older PCIe Gen 3 slot, it's going to be slow. Upgrading to PCIe Gen 4 or Gen 5 will provide a noticeable speed boost for offloading tasks.

Conclusion

OpenAI's pivot toward discovery royalties and advertising is a clear signal: the era of "free" frontier AI is ending. For engineers and researchers, the focus must shift toward local optimization and sovereign compute. By mastering techniques like SageAttention, Tiled VAE, and Block Swapping, we can maintain high-fidelity output on consumer hardware, bypassing the restrictive and expensive ecosystems of the major providers. The tools are here—it's just a matter of sorting the implementation.

More Readings

Continue Your Journey (Internal 42.uk Research Resources)

/blog/comfyui-workflow-basics - A fundamental guide to node-based AI generation.

/blog/vram-optimization-guide - Deep dive into FP8, SageAttention, and memory management.

/blog/production-ai-pipelines - Scaling ComfyUI for enterprise-level video production.

/blog/prompt-engineering-tips - Advanced techniques for guiding Flux and LTX-2 models.

/blog/gpu-performance-tuning - How to squeeze every last IT/s out of your RTX 30-series and 40-series cards.

/blog/local-llm-deployment - Running Qwen3 and Llama-4 on local workstations.

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Created: 25 January 2026