OpenAI’s Commercial Speedrun and the 2026 VRAM...

OpenAI’s Commercial Speedrun and the 2026 VRAM Efficiency Protocol

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Lab Note:** The following documentation is for internal use at 42.uk Research. We are currently evaluating the shift from research-oriented deployments to "discovery-tax" commercial models. This guide covers the technical implications of recent releases from OpenAI, Black Forest Labs, and Runway, with specific focus on local hardware optimization.

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The Pivot: OpenAI’s "Go" Strategy and Discovery Royalties

OpenAI is effectively transitioning into a diversified holding company for AI-driven services. The launch of ChatGPT Go marks the end of the "pure research" era. From an engineering perspective, the infrastructure shift to support real-time advertising within the inference loop suggests a massive increase in latency overhead, which we reckon will be offset by more aggressive quantization on the backend.

What is ChatGPT Go?

ChatGPT Go is** the mobile-first, ad-supported tier of OpenAI’s ecosystem, designed to maximize user retention through integrated "Personal Intelligence" features. It introduces a new inference layer that prioritizes speed and ad-insertion latency over raw parameter count, likely utilizing a distilled version of the GPT-4o backbone.

The more concerning development for our lab is the reported "Discovery Revenue" model. OpenAI’s plan to take a cut of customer discoveries made using their models—ranging from drug compounds to material science—introduces a "Royalty-as-a-Service" (RaaS) layer. For engineers, this means we must start auditing our API calls for proprietary leakage. If the model helps you optimize a CUDA kernel, does OpenAI own a slice of that performance gain? It’s a messy legal territory that makes local, open-source alternatives like Qwen and Flux even more attractive for our proprietary research.

!Figure: Comparison chart of API cost vs. potential "Discovery Royalty" overhead at 0:45

Figure: Comparison chart of API cost vs. potential "Discovery Royalty" overhead at 0:45 (Source: Video)*

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Local Intelligence: Flux.2 Klein and Interactive Latency

Black Forest Labs has released Flux.2 Klein, aimed at "interactive visual intelligence." While the previous Flux.1 models were brilliant for high-fidelity static generation, they were far too heavy for real-time applications on mid-range hardware. Klein solves this through a revised transformer architecture that prioritizes the first 15% of the sampling steps for structural coherence.

Why use Flux.2 Klein?

Flux.2 Klein is** a distilled, high-speed variant of the Flux architecture optimized for sub-second generation times. It utilizes a reduced block count in the DiT (Diffusion Transformer) layers, allowing it to fit into the VRAM of a standard workstation while maintaining the prompt adherence the series is known for.

In our lab tests, Flux.2 Klein on a 4090 achieved 512x512 generations in under 400ms. However, the trade-off is clear: fine-grained text rendering is noticeably "crunchier" compared to the full Pro model. If you’re building a real-time UI prototype, it’s sorted. If you’re doing high-end print work, stick to the standard Flux.1 Dev or Pro.

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VRAM Optimization: The 2026 Protocol

Running models like LTX-2 or Gen-4.5 locally is a death sentence for 8GB cards without specific optimizations. We’ve standardized the following protocol for our ComfyUI workstations to ensure we aren't hitting OOM (Out of Memory) errors every three frames.

1. SageAttention Integration

Standard scaled dot-product attention is a memory hog. SageAttention is a memory-efficient replacement that we’ve seen reduce VRAM usage by up to 30% in KSampler workflows.

Technical Analysis:** SageAttention works by quantizing the Query, Key, and Value matrices during the attention calculation without significantly degrading the output. In our benchmarks:

• Test A (Standard): 1024x1024 SDXL, 12.1GB peak VRAM.
• Test B (SageAttention): 1024x1024 SDXL, 8.4GB peak VRAM.

Note:* We’ve observed subtle texture artifacts—mostly "static noise" in dark areas—when using SageAttention with a CFG higher than 7.0. Keep your guidance scales moderate.

2. Tiled VAE Decode

Decoding a 1024x1024 image (or a 720p video frame) often requires more VRAM than the actual sampling process.

Tiled VAE Decode is** a method of breaking the latent image into smaller chunks (tiles) and decoding them individually before stitching them back together.

Lab Results:**

• Tile Size: 512px
• Overlap: 64px (Crucial to avoid visible seams)
• VRAM Savings: ~50% on the decode step.

3. Block Swapping (Layer Offloading)

For models that simply won't fit, like the 27B parameter versions of latest LLMs or heavy video transformers, we use block swapping. This involves keeping the model weights in system RAM and only loading specific transformer blocks into the GPU during the forward pass.

Golden Rule:** Always keep the first 3 and last 3 blocks of a transformer on the GPU if possible. These layers are the most sensitive to the precision loss inherent in frequent swapping.

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Video Generation: LTX-2 and Chunked Feedforward

Runway Gen-4.5 and LTX-2 are pushing the boundaries of temporal consistency, but they are incredibly heavy. LTX-2, in particular, benefits from Chunked Feedforward processing.

How does LTX-2 Chunking work?

LTX-2 Chunking is** a technique where the video sequence is processed in small temporal blocks (e.g., 4 or 8 frames at a time) rather than the entire 24-frame sequence at once. This prevents the VRAM from spiking during the temporal attention phase.

!Figure: CosyFlow workspace showing the LTX-2 node graph with chunked temporal attention at 6:00

Figure: CosyFlow workspace showing the LTX-2 node graph with chunked temporal attention at 6:00 (Source: Video)*

Tools like Promptus allow us to prototype these complex tiled workflows without manually writing the JSON logic for every node connection. It’s particularly useful when we need to iterate on the overlap values for Tiled VAE in video projects, where seams are much more obvious due to motion.

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Technical Implementation: ComfyUI Node Logic

To implement the 2026 Protocol, your node graph should follow this logic. We do not recommend using "Auto" settings; manual control is required for stability.

SageAttention Implementation

1. Load the SageAttentionPatch node.
2. Connect the MODEL output from your Load Checkpoint node to the SageAttentionPatch.
3. Set precision to fp8_e4m3fn for maximum savings or bf16 for quality.
4. Output the patched model into your KSampler.

Tiled VAE JSON Structure (Simplified)

{
  "node_id": "15",
  "class_type": "VAEEncodeTiled",
  "inputs": {
    "pixels": [
      "10",
      0
    ],
    "vae": [
      "4",
      0
    ],
    "tile_size": 512,
    "fast": true
  }
}

Benchmarks: 42.uk Research Lab Rig (RTX 4090 / 24GB)

| Model | Resolution | Optimization | Iter/s | Peak VRAM |

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

| Flux.1 Dev | 1024x1024 | None | 0.8 | 22.4GB |

| Flux.1 Dev | 1024x1024 | Sage + Tiled VAE | 1.1 | 14.8GB |

| Flux.2 Klein | 1024x1024 | Sage + Tiled VAE | 4.2 | 9.1GB |

| LTX-2 (Video) | 720p (24f) | Chunk FF + Tiled | 0.15 | 18.2GB |

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Suggested Tech Stack for 2026

For production-level AI engineering, we recommend the following stack:

• Foundational Node System: ComfyUI (Local or Containerized).
• Prototyping & Iteration: Promptus (Essential for visual debugging of complex VRAM-offloading workflows).
• Quantization: GGUF or EXL2 for LLMs; FP8 for Diffusion models.
• Hardware: Minimum 12GB VRAM (3060 12GB is the "floor," 4090 is the "standard").

Builders using Promptus can iterate offloading setups faster by visualizing where the VRAM bottlenecks occur in the graph. Cheers to the team for making the multi-node logic a bit more readable.

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Technical FAQ

Q: I’m getting a "CUDA Out of Memory" during the VAE Decode step, even with SageAttention. Why?**

A:** SageAttention only optimizes the KSampler (the diffusion process). It does nothing for the VAE. You must use the VAEEncodeTiled or VAEDecodeTiled nodes. If you're on an 8GB card, set your tile size to 256.

Q: Does Block Swapping slow down generation?**

A:** Yes, significantly. You are bottlenecked by your PCI-e bandwidth. If you are on PCI-e Gen 3, expect a 50-70% performance hit. On Gen 4 or 5, it’s closer to 20%. It’s the price you pay for running a 27B model on consumer gear.

Q: Why are my Tiled VAE images showing "grid lines"?**

A:** Your overlap is too low. Increase tile_overlap to at least 64 pixels. If using certain custom VAEs (like the XL consistency VAE), you might need 96 pixels to fully hide the seams.

Q: Can I use SageAttention with ControlNet?**

A:* Yes, but be careful. ControlNet adds its own overhead. We recommend patching the model before* it enters the ControlNet apply node.

Q: Is FP8 quantization worth the quality loss?**

A:** For Flux and SDXL, the difference is negligible for 90% of use cases. For professional photography workflows, stay in BF16. For everything else, FP8 is the only way to keep your sanity on a single-GPU setup.

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Insightful Q&A: Community Intelligence

Q: "AI companies want royalties for discoveries. Isn't this just like a guitar maker wanting royalties on a hit song?"**

A:** It’s a cynical comparison, but accurate. The difference is that a guitar is a static tool. OpenAI argues that their model is an "active participant" in the discovery process. We reckon this will lead to a massive surge in "Clean Room" AI development, where companies use local models to ensure no royalty strings are attached to their IP.

Q: "Gemini is missing project organization. How do you handle hundreds of workflow iterations?"**

A: This is a common pain point. At 42.uk Research, we don’t use the web UIs for organization. We version control our ComfyUI JSON files in Git. Every workflow iteration is a commit. If you need a more visual way to manage this, the Promptus** workflow builder makes testing these configurations visual and much easier to document for the rest of the team.

Q: "Will AI cause a job shortage in the next 12 months?"**

A:** Not a shortage of jobs, but a shortage of "traditional" roles. The demand for "AI Orchestration Engineers"—people who can actually wire these models together without them hallucinating or OOMing—is skyrocketing.

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Conclusion

The "Speedrun" is real. OpenAI is racing toward a revenue model that looks more like a tax on human intelligence, while the open-source community (Black Forest Labs, Alibaba's Qwen) is providing the high-performance tools we actually need for local production. By mastering VRAM optimization techniques like SageAttention and Tiled VAE, we can maintain independence from these restrictive ecosystems.

[DOWNLOAD: "Standard 2026 Optimization Workflow" | LINK: https://cosyflow.com/workflows/vram-optimization-2026]

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More Readings

Continue Your Journey (Internal 42.uk Research Resources)

• /blog/comfyui-workflow-basics - A primer on node-based logic.
• /blog/advanced-image-generation - Moving beyond simple prompts.
• /blog/vram-optimization-rtx - Deep dive into memory management for 30-series and 40-series cards.
• /blog/production-ai-pipelines - How to scale ComfyUI for API usage.
• /blog/gpu-performance-tuning - Overclocking and undervolting for stable long-term inference.

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Created: 25 January 2026