Nvidia & AMD Chips, xAI’s $20B Raise, and the New Arms Race for Compute

CES 2026 announcements and record-breaking funding rounds reveal why hardware, capital, and scale—not just models—will define the next era of AI.

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TensorTeach AI
January 06, 2026

This Week In AI

Artificial intelligence kicked off the year with a surge of momentum, as major hardware launches and historic funding rounds signaled how aggressively the AI ecosystem is scaling. Over the past week, developments from Nvidia, AMD, and xAI made it clear that the next phase of AI progress will be driven as much by infrastructure and capital as by algorithms.

At CES 2026, Nvidia unveiled its Vera Rubin AI computing platform, alongside advanced chip architectures designed to dramatically boost AI training and inference performance. These systems target the growing demands of large-scale data centers, autonomous vehicles, and real-time AI applications—further cementing Nvidia’s role as the backbone of modern AI infrastructure. At the same time, AMD intensified competition by announcing its latest AI-focused processors, including the Ryzen AI 400 and PRO 400 series for client devices and new MI chips aimed at servers and accelerators.

Beyond hardware, the investment landscape reached a new peak. Elon Musk’s AI company xAI raised a record-breaking $20 billion Series E round, backed by major strategic players such as Nvidia and Cisco. The funding will be used to expand compute capacity, accelerate next-generation Grok models, and scale global AI infrastructure at an unprecedented pace.

Zooming out, this week’s news fits into a broader trend: 2025 saw an explosion of mega-rounds in AI, with more than 15 companies raising $2 billion or more. Despite rising valuations, investor appetite remains strong—underscoring long-term confidence in AI as a foundational technology shaping computing, automation, and the global economy.

This Week In AI Research

Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications

Image from arXiv paper.

What’s the research question?

How can a multimodal large language model (MLLM) be optimized for enterprise applications while maintaining strong general-purpose capabilities?

What did the authors do?

The authors developed Yuan3.0 Flash, a multimodal large language model designed for enterprise use, with the following key features:

  • Three-part architecture

    • A high-resolution visual encoder (InternViT-300M)

    • A Mixture-of-Experts (MoE) language backbone with 40 layers and 32 experts per layer using Top-K routing

    • A lightweight multimodal alignment module that maps visual features to text tokens

  • Four-stage training pipeline

    • Pretraining on 3 trillion tokens

    • Multimodal training on 256 million image–text pairs

    • Supervised fine-tuning with high-quality instruction data

    • Reinforcement learning (RL)

  • Novel reinforcement learning strategies

    • Reflection-aware Adaptive Policy Optimization (RAPO)

    • Reflection Inhibition Reward Mechanism (RIRM), which rewards early correct reasoning and penalizes overthinking

  • Diverse training data

    • Web pages, books, academic papers, image–text pairs, and enterprise-specific data such as RAG pipelines and tabular datasets

  • Efficiency-focused techniques

    • Adaptive image segmentation and Dynamic Sampling (ADS) to improve training efficiency and data diversity during RL

What did they find?

Yuan3.0 Flash demonstrated strong performance across a wide range of benchmarks:

  • Perfect accuracy on the Needle-in-a-Haystack long-text retrieval benchmark

  • Outperformed GPT-4o and Claude 3.5 on:

    • Multimodal retrieval (65.07% on Docmatix)

    • Complex table understanding (58.29% on MMTab)

  • Scored 59.31 on SummEval, surpassing Gemini and GPT-5.1

  • Strong tool invocation performance with an average score of 57.97

  • Achieved 88.7% accuracy on MATH-500 and 91.2% on LiveCodeBench, rivaling much larger models

Limitations include slightly lower performance on some general reasoning benchmarks compared to the largest frontier models, as well as reliance on complex training pipelines and high-quality datasets.

Why does this matter?

Yuan3.0 Flash advances enterprise-ready multimodal AI by showing that a carefully optimized Mixture-of-Experts architecture, combined with innovative reinforcement learning, can deliver high performance and efficiency.

Its open-source release provides a valuable foundation for researchers and industry practitioners building AI systems capable of reasoning across text, images, tools, and structured data in real-world enterprise settings. By enabling more efficient and effective AI solutions, Yuan3.0 Flash has the potential to improve productivity, decision-making, and automation—while influencing the design of future multimodal models.

Key Points

  • Combines a visual encoder, MoE-based language backbone, and multimodal alignment module

  • Uses novel RL techniques (RAPO and RIRM) to enhance reasoning and reduce overthinking

  • Achieves state-of-the-art results on enterprise and multimodal benchmarks despite smaller scale

  • Open-source release promotes transparency, collaboration, and innovation in multimodal AI

DeepConf@512: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

What’s the research question?
Can a small language model (SLM) achieve state-of-the-art reasoning performance through targeted training and architectural innovations?


What did the authors do?
The authors developed Falcon-H1R, a 7-billion-parameter reasoning-optimized language model, and introduced a hybrid Transformer-SSM architecture designed for efficient inference and test-time scaling (TTS). Their approach included:

  • Hybrid Architecture: Combining Transformer and SSM (Structured State Space Model) components to balance speed and reasoning ability.

  • Targeted Training: Supervised fine-tuning (SFT) on curated datasets emphasizing long reasoning traces and high-quality, domain-specific data.

  • Reinforcement Learning: Applying RL with the GRPO method on disjoint datasets to enhance reasoning and output quality beyond supervised fine-tuning.

  • Curriculum Learning: Single-stage, difficulty-aware filtering focusing on challenging mathematical reasoning problems.

  • Test-Time Scaling: Leveraging the architecture to enable fast, parallel inference and scaling during testing to improve accuracy and efficiency.

What did they find?
Falcon-H1R-7B achieved impressive results on diverse reasoning benchmarks:

  • 88.1% on AIME24, 83.1% on AIME25, 64.9% on HMMT25, 36.3% on AMO-Bench, and 68.6% on LiveCodeBenchv6.

  • Outperformed larger models like GPT-OSS-20B, Qwen3-32B, and Nemotron-H-47B-Reasoning on reasoning tasks.

  • Demonstrated superior inference efficiency by generating fewer tokens while maintaining high accuracy.

  • Test-time scaling further boosted performance, achieving 96.7% on AIME25 with 38% fewer tokens, reducing computational costs.

  • Limitations include less competitive performance on certain knowledge-intensive benchmarks like MMLU-Pro and GPQA-Diamond, and potential generalization constraints due to curated training data and architectural choices.

Why does this matter?
This work challenges the common assumption that only large models can excel at complex reasoning tasks. By carefully designing a small model with a hybrid architecture and advanced training strategies, the authors show that high reasoning performance can be achieved efficiently and scalably. This has significant implications:

  • Accessibility: Smaller, efficient models can democratize advanced AI capabilities, making reasoning-powered applications more accessible across industries and communities.

  • Resource Efficiency: Test-time scaling and architectural innovations reduce computational costs, enabling deployment in resource-constrained environments like edge devices or real-time systems.

  • Research Impact: Provides a new paradigm for building high-performance reasoning models without relying solely on massive parameter counts, guiding future model design and training methodologies.

  • Broader Societal Benefits: Enhanced reasoning AI can improve education, scientific discovery, and decision-making while supporting safer and more robust deployment.

Key Points

  • Falcon-H1R combines Transformer and SSM architectures for efficient reasoning inference.

  • Targeted training with supervised fine-tuning and reinforcement learning enhances reasoning capabilities.

  • Test-time scaling improves accuracy and reduces computational costs significantly.

  • Small models can outperform larger counterparts on reasoning benchmarks, challenging size-centric AI paradigms.

Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement

Image from arXiv paper.

What’s the research question?
How can failure-driven post-training combined with document knowledge enhancement improve the reasoning capabilities of large language models (LLMs) specifically in STEM (Science, Technology, Engineering, Mathematics) domains?


What did the authors do?
The authors developed Logics-STEM, a reasoning-focused LLM training framework with the following key components:

  • Created a high-quality, diverse 10 million-scale dataset called Logics-STEM-SFT-Dataset through meticulous data collection, annotation, deduplication, decontamination, distillation, and stratified sampling.

  • Implemented a two-stage training process:

    • Supervised Fine-Tuning (SFT): Fine-tuned the model to generate proposal distributions for reasoning tasks.

    • Failure-Driven Post-Training: Identified failure regions via evaluation, retrieved relevant external documents using cosine similarity, synthesized high-quality question-answer pairs from these documents, and continued training on this synthetic data to improve reasoning accuracy.

  • Applied Reinforcement Learning with Verified Rewards (RLVR) to further align the model outputs with human preferences and reasoning standards.

  • Evaluated the model on multiple STEM benchmarks (AIME, HMMT, GPQA) using metrics like Pass@1, Best@N, and Majority@N.

What did they find?
The Logics-STEM-8B model achieved impressive results:

  • 90.42% on AIME2024 and 87.08% on AIME2025, surpassing other models of similar size.

  • 74.79% on HMMT2025 and 62.5% on BeyondAIME, showing strong reasoning performance across diverse benchmarks.

  • 73.93% on GPQA-Diamond, indicating robust generalization in STEM question answering.

  • Abalation studies confirmed the importance of stratified sampling and the source of synthetic data in improving performance.

  • Scaling to larger architectures maintained performance gains, demonstrating the approach’s scalability.

  • Failure-driven synthetic data enhanced the model’s ability to generalize across different reasoning tasks.

Limitations include: The reliance on external document retrieval quality and synthetic data generation, which may introduce biases or errors if not carefully managed.

Why does this matter?
This work advances the development of more capable and robust reasoning models in STEM domains by:

  • Showing that combining large-scale open-source data with targeted synthetic data generation driven by failure analysis can significantly improve reasoning accuracy.

  • Introducing a scalable and generalizable failure-driven post-training paradigm that aligns models more closely with complex reasoning distributions.

  • Providing a practical approach to enhance LLM reasoning in real-world STEM applications, such as automated problem solving, scientific research, and education.

Key Points

  • Failure-driven post-training uses external document retrieval and synthetic question-answer synthesis to improve reasoning.

  • Logics-STEM outperforms similar-sized models on major STEM reasoning benchmarks.

  • Stratified sampling and synthetic data source are critical for effective training.

  • The approach scales well to larger model architectures and diverse tasks.