Chips & Supply · United States

TSMC in talks with Musk over potential role in Texas Terafab, discussions at early stage

Credited to The Hindu BusinessLine · thehindubusinessline.com · 16h ago

Photograph · The Hindu BusinessLine

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Taiwan Semiconductor Manufacturing Co. (TSMC) is in talks over a potential role in Elon Musk’s planned Terafab foundry in Texas, with the discussions still at an early stage, according to a news report by Taiwan Plus. TSMC Chairman and CEO CC Wei said the timeline for building and scaling a new wafer fabrication plant could be a key consideration as the companies discuss the project.

Wei said a new wafer fabrication plant would take about two to three years to build, while expanding its production capacity could require another one to two years, calling it a “fundamental” of the foundry industry, Taiwan Plus reported.

The report said one option under discussion would have TSMC own and operate the Texas plant, while SpaceX would invest in the project and purchase chips produced at the facility. Another option would give SpaceX a majority stake in the plant.

Musk has confirmed the discussions with TSMC on X, saying the talks are still at an early stage, Taiwan Plus reported. The potential Texas project comes as TSMC continues to expand its manufacturing presence in the United States. Taiwan’s Ministry of Economic Affairs had approved applications for TSMC to invest $44 billion in the United States since December 2020, according to an earlier Focus Taiwan report.

TSMC announced in March 2025 that it planned an additional $100 billion investment in Arizona, on top of an earlier $65 billion plan, taking its overall planned investment in the state to $165 billion. The expanded Arizona plan included increasing the number of planned advanced wafer fabrication facilities from three to six, along with two advanced integrated circuit assembly units and a dedicated research facility, according to Focus Taiwan.

For the proposed Texas Terafab, the ownership structure and TSMC’s eventual role remain under discussion. With the talks still at an early stage, the proposed operating and investment arrangements have yet to be finalised.

Chips & SupplyUnited States

Samsung's Austin chip workforce shrinks 32% as Taylor 2nm fab nears opening

Credited to DigiTimes$ · digitimes.com7h ago

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Austin and Taylor campuses supported 28,746 jobs and generated $2B in workers’ salaries

AUSTIN, Texas—Oct. 07, 2026—In a year of steady expansion and local investment, Samsung Austin Semiconductor reported that its Austin and Taylor campuses injected a total of $10.9 billion into the Central Texas economy in 2025. According to an economic impact analysis, the campuses supported 28,746 jobs and paid $2 billion in wages, underscoring the company’s role as a major economic catalyst in the region.

The 2025 Economic Impact Report [1] is a comprehensive third-party report that determines economic contribution and impact generated through operations of Samsung Austin Semiconductor’s two fabs in northeast Austin and the construction of the manufacturing facility in Taylor.

While construction on the Taylor campus progressed throughout 2025, significant campus operations also began in Q4 with the opening of the office building to hundreds of employees. The report highlights the effects resulting from both Taylor campus operations and the construction activities.

In 2026, the company aims to establish the Taylor fab, utilizing extreme ultraviolet (EUV) technology to produce 2nm chips in the U.S. The site is expected to be operational by the end of 2026. Samsung Austin Semiconductor is dedicated to ongoing capital investment, with plans to invest more than $37 billion in the region in the coming years.

Additionally, Samsung Austin Semiconductor is engaged in workforce development and community partnership initiatives that strengthen both the local economy and the semiconductor manufacturing ecosystem in Central Texas.

Click here to read the full 2025 Economic Impact Report [1].

Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop

Credited to The Decoder · the-decoder.com · 15h ago

Nvidia has released Nemotron 3.5 Super VL, an open multimodal model with 120 billion parameters built for enterprise agents. Two compact Bolt models designed to bring AI agents to local PCs and edge devices are launching alongside it.

Nvidia built Nemotron 3.5 Super VL for specialized agents that run continuously in the background, processing text, images, documents, and videos.

As the company explains in a technical blog post, it uses a Mixture-of-Experts architecture where only 12 billion of the 120 billion parameters are active per token. Nvidia developed the model with the Nemotron Coalition, a group that includes Mistral AI, Thinking Machines Lab, Perplexity, and Black Forest Labs. Ad

Two smaller models for local hardware ship at the same time. Nemotron 3.5 Bolt 74B-A9B 24GB runs agent tasks on local machines, while the smaller Bolt E4B 4GB targets fast voice assistants and will follow later this month. Both models offer the "highest intelligence per gigabyte of memory," Nvidia claims. Ad

Super VL and Bolt join the existing Nemotron lineup. Lightning, released in August, is built for high throughput, while Ultra tackles tough reasoning tasks. Every variant is optimized for Nvidia hardware.

Nvidia claims its biggest model's accuracy at one-fifth the size

Nvidia distilled Super VL from Nemotron 3 Ultra, its largest reasoning model. On tasks where an agent follows instructions, calls tools, works through multi-step business processes, or analyzes images, Super VL is supposed to match Ultra's accuracy with only about one-fifth the parameters. In agent tests from Hermes and LangChain, it actually scores higher than Ultra, according to Nvidia, and it also beats its predecessor Nemotron 3 Super on the coding benchmarks SWE-bench and Terminal-Bench. Ad

On a single GPU, Super VL delivers up to five times the throughput of other open models of similar size on agent tasks, according to Nvidia, citing the Artificial Analysis leaderboard. In an independent test by EXO Labs on the DGX Spark, no other model was both more accurate and faster. That speed advantage pays off when agents need to grind through large task volumes over days or weeks.

Against closed frontier models, Nvidia is pitching Super VL to companies that want to fine-tune with their own data and run the model on their own servers, free from a provider's terms of service. Nvidia says that could appeal to an insurer that continuously screens claims for fraud, or a financial services firm that scores creditworthiness by its own criteria. For customization, the company provides its NeMo framework, Python pipelines, and open datasets. Ad

Compression and caching cut compute overhead

Nvidia compresses Super VL with its 4-bit NVFP4 format. On Blackwell GPUs, the model runs entirely in 4-bit precision, while older cards store only the weights at 4 bits and handle math at 16 bits. Sensitive parts of the model keep higher precision. To prevent quality loss, Nvidia trained the compressed version to match the uncompressed original's outputs. Ad

A built-in draft component proposes several tokens ahead of time, which the main model then verifies in a single forward pass. On the DGX Spark, Nvidia recommends its own LiLiCorr method instead, which evaluates all draft tokens together for more coherent output. Nvidia says this approach speeds up text generation by up to 2.3x compared to running without speculative drafts.

Agents send the same instructions, tool descriptions, and conversation history with every request, so Super VL caches the computations for these recurring parts rather than rerunning them each time. Nvidia's inference software Dynamo also routes requests from the same session to the GPU that already holds that session's cache. On 16 B200 GPUs, this lets Super VL serve eight times as many concurrent users, and time to first token drops by 85 percent.

Super VL parses tables, charts, and PDFs without a separate vision model

For vision, Nvidia focused on the kind of material agents encounter in daily work, including OCR, tables, charts, multi-page documents, and videos. Super VL can interpret a chart, link it to a table elsewhere in the same document, and summarize both without needing a separate image model.

On the chart benchmark CharXiv, Super VL scores 80.8 percent, according to Nvidia, while the older Nemotron 3 Nano Omni hit 63 percent. When the model can also run Python code, the score rises to 84 percent. On the OCRBench v2 leaderboard, it ranks among the best open models, Nvidia says.

Local agents still demand expensive hardware

Bolt 74B-A9B 24GB is a stripped-down version of Super VL built as a hybrid Mixture-of-Experts model where only 9 of its 74 billion parameters are active per step. To squeeze it into 24 GB of memory, Nvidia applies aggressive 2-bit quantization to the weights.

Bolt handles everyday local tasks where an agent needs to work through multiple steps, call tools, or search the web. Users can have it write and debug code or figure out which PC components fit their system, according to Nvidia.

The model is optimized for Nvidia's RTX Spark, a new chip for AI-capable Windows laptops, and the DGX Spark, a compact AI workstation with up to 128 GB of shared memory. It also runs on GeForce RTX PCs, DGX Station, and RTX PRO workstations, and supports llama.cpp, Ollama, LM Studio, and vLLM.

Even with heavy compression, 24 GB is a high bar. Only pricey high-end hardware offers that much memory, and at current GPU prices, most consumers can't justify the cost. For now, the local agent is aimed at developers and owners of powerful workstations.

For weaker devices, Nvidia has Bolt E4B 4GB planned, though it hasn't shipped yet. It's designed to run on GeForce RTX cards, Jetson modules, and RTX/DGX Spark, powering voice assistants, interactive characters, or device interfaces. The model is supposed to hold natural conversations while using tools across multiple turns and staying aware of what's happening in the current app.

Early adopters span finance and cybersecurity

Dozens of organizations are already fine-tuning Super VL for their own needs, Nvidia says, mainly in industries with heavy document loads and sensitive data. Scale AI adapted the model for an agent that handles vendor credits at media company Skydance, with costs expected to drop to one-fortieth and response time to one-fifth. Ramp uses the model to turn photos of receipts into structured data and expects ten times faster responses. EY uses it for acquisition due diligence, and Hebbia for analyzing credit agreements.

A second wave of adopters comes from cybersecurity. AI21 fine-tuned Super VL for security firm Alice, which uses it to analyze threats and vulnerabilities, with accuracy improving by 40 percent according to Nvidia. Dream uses the model to hunt for vulnerabilities, and CrowdStrike is building agents to defend enterprise networks. Salesforce plans to automate government workflows, R1 is auditing medical billing, and Perplexity, Dassault Systèmes, and Mercor are also using the model.

Nvidia publishes datasets and training recipes

Nvidia is releasing the weights in a base version, a post-trained version, and a 4-bit compressed version, along with the datasets and training recipes and reinforcement learning environments. That makes Super VL more open than many open-weight models, which typically only share the weights.

The model was trained on over 25 trillion tokens during pre-training and continued pre-training, Nvidia says, with post-training and reinforcement learning adding roughly four million examples across about 100 open training environments.

On Hugging Face, Nvidia is also releasing several datasets that other developers can use to train or fine-tune their own models. They cover how agents use tools, follow instructions, handle long texts, and understand images, while other datasets deal with finance, safety, and fairness.

One dataset targets languages spoken in India. Nvidia built it with the Indian Institute of Technology Madras and its AI lab AI4Bharat, and it contains about 2.83 trillion tokens of text in 22 Indian languages.

Open models sell Nvidia hardware

Super VL runs on desktop systems like DGX Spark, DGX Station, RTX PRO, and RTX, on Jetson modules in edge devices, and in data centers and the cloud. Through Nvidia NIM, enterprises get the model as a ready-to-deploy microservice with validated configs and support, with updates planned every two weeks. Weights are available on Hugging Face for Nemotron 3.5 Super VL 120B-A12B and Nemotron 3.5 Bolt 74B-A9B 24GB.

Nvidia releases the models openly but tunes them for its own products, from data center servers to workstations. Anyone who wants to get the most out of Nemotron ends up buying Nvidia hardware.

The strategy behind Nvidia's open models is self-serving. The more companies run their own open models, the greater the demand for compute hardware, and open models will likely stay optimized for Nvidia for the foreseeable future. Meanwhile, companies like Meta, Google, Anthropic, and OpenAI are building their own chips and optimizing models for those instead. To push the spread of open AI models further, Nvidia recently invested $12.9 billion in Hugging Face, one of the most important platforms for developing and sharing them.

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