LOS ANGELES, Jan. 24, 2026 /PRNewswire/ -- At CES 2026, VR filmmaker and educator Hugh Hou led a live spatial computing demonstration inside the GIGABYTE suite, showing how immersive video is created in real production environments, not in theory or controlled lab conditions.
The session gave attendees a close look at a complete spatial filmmaking pipeline, from capture through post-production and final playback. Instead of relying on pre-rendered content, the workflow was executed live on the show floor, reflecting the same processes used in commercial XR projects and placing clear demands on system stability, performance consistency, and thermal reliability. The experience culminated with attendees viewing a two-minute spatial film trailer across Meta Quest, Apple Vision Pro, and the newly launched Galaxy XR headsets, alongside a 3D tablet display offering an additional 180-degree viewing option.
Where AI Fits Into Real Creative Workflows
AI was presented not as a feature highlight, but as a practical tool embedded into everyday editing tasks. During the demo, AI-assisted enhancement, tracking, and preview processes helped speed up iteration without interrupting creative flow.
Footage captured on cinema-grade immersive cameras moved through industry-standard software including Adobe Premiere Pro and DaVinci Resolve. AI-based upscaling, noise reduction, and detail refinement were applied to meet the visual requirements of immersive VR, where any artifact or softness becomes immediately noticeable across a 360-degree viewing environment.
Why Platform Design Matters for Spatial Computing
Supporting the entire workflow was a custom-built GIGABYTE AI PC designed specifically for sustained spatial video workloads. The system combined an AMD Ryzen™ 7 9800X3D processor with a Radeon™ AI PRO R9700 AI TOP GPU, providing the memory bandwidth and continuous AI performance required for real-time 8K spatial video playback and rendering. Equally critical, the X870E AORUS MASTER X3D ICE motherboard delivered stable power and signal integrity, allowing the workflow to run predictably throughout the live demonstration.
The experience concluded with attendees viewing a finished spatial film trailer across Meta Quest, Apple Vision Pro, and Galaxy XR devices.
By enabling a demanding spatial filmmaking workflow to operate live and repeatedly at CES, GIGABYTE demonstrated how platform-level system design turns complex immersive production into something creators can rely on, not just experiment with.
** The press release content is from PR Newswire. Bastille Post is not involved in its creation. **
Inside the Computing Power Behind Spatial Filmmaking: Hugh Hou Goes Hands-On at GIGABYTE Suite During CES 2026
A state-of-the-art general-purpose model in its class, built through automated AI research and designed to advance AI and scientific discovery.
BEIJING, Oct. 29, 2026 /PRNewswire/ -- Apex Intelligence has released Apex-35B-V1.0, its first model. Built on Qwen3.6-35B-A3B and further developed through mid-training and post-training, the 35B general-purpose model incorporates extensive proprietary datasets and research trajectories.
According to Apex Intelligence's evaluations, Apex-35B-V1.0 leads the similarly sized models tested in research and development capabilities while maintaining strong general-purpose performance. On selected benchmarks, it is competitive with substantially larger models.
AI participated autonomously in the model's development, including data construction, experiment design, code execution, analysis of training results and training optimization. Apex-35B-V1.0 can perform coding, agent workflows, question answering and deep search. It can also carry out end-to-end computational research workflows in AI research, computational science and robotics simulation.
Benchmark performance
Across 21 benchmarks, Apex-35B-V1.0 outperformed the similarly sized Qwen3.6-35B-A3B on 20 and Recursive Flint (40B) on 18, tying Recursive Flint on one. Beyond its strong performance on research tasks, the model demonstrated broad competitiveness in agentic tasks, coding, and reasoning, and surpassed larger models on selected benchmarks. Further details are available in the technical article.
Tech Blog：https://www.apexin.com/apex-35b/
Building Apex-35B-V1.0 Through Recursive Self-Improvement
Apex Intelligence developed Apex Research, an automated research system designed to enable recursive self-improvement. The system identifies model weaknesses, generates hypotheses, runs experiments and turns findings into measurable improvements. It contributed to the development of Apex-35B-V1.0 through self-evolving training data, automated experimentation, training recipes, GPU kernels and inference optimization.
Apex Research generates targeted training data to address weaknesses revealed by evaluation. When evaluations identified failures in computational fluid dynamics, the system analyzed the reasoning gaps, synthesized and validated new scientific training examples, and incorporated them into subsequent training rounds. Apex Intelligence reports that this process improved performance on its internal CFD-Bench by 32.1 points.
The system also analyzes evaluation results and agent trajectories to identify recurring failure patterns and refine training strategies. After identifying excessively long reasoning contexts and imbalanced data mixtures as factors affecting agent performance, it explored sequence-length distributions, short-to-long trajectory sampling ratios and token-aware batch compositions to improve agent reliability and training efficiency.
Apex Research extends this optimization to training and inference infrastructure. On the training side, its work on GPU load balancing, communication and computation overlap, dynamic batching, and token-aware workload distribution delivered 45–60% higher training throughput, according to Apex Intelligence. On the inference side, optimizations to attention kernels, KV-cache management and prefill/decode execution delivered 1.6–1.8 times faster decoding and 30–35% lower time to first token under comparable workloads.
Together, these improvements across data, training and infrastructure form a feedback loop in which each research iteration supports the next.
Availability
The weights for Apex-35B-V1.0 are available on Hugging Face. Accompanying code, a deployment guide and usage examples are available on GitHub. Developers can download and run the model for AI development, scientific computing and other research tasks. Model versions, hardware requirements, supported inference frameworks and licensing information are detailed on the project pages.
GitHub：https://github.com/apexin-ai/Apex-35B-V1.0
Hugging Face：https://huggingface.co/Apexintelligence-AI/Apex-35B-V1.0
About Apex Intelligence and what comes next
Apex Intelligence was founded by Yongchao Chen, an assistant professor at Tsinghua University's College of AI. The company aims to build the next generation of self-evolving foundation models for research. By "self-evolving," it means enabling AI to participate in research, experimentation and validation, then using the resulting discoveries to improve the model itself.
Apex Intelligence aims to advance AI from an assistant to a research collaborator capable of contributing to new discoveries. Its long-term vision is to develop models whose collective research capability matches and progressively surpasses that of 100,000 leading AI scientists across disciplines.
Following Apex-35B-V1.0, the company plans to release a larger self-evolving foundation model with stronger AI research capabilities and expanded applications in scientific fields including chip design and quantum computing. Apex Intelligence plans to announce further details on the model's capabilities, benchmark results and availability.
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Apex Intelligence Releases Apex-35B-V1.0, a General-Purpose Model Built With Automated AI Research
