For the first time, enterprises can run ultra-large AI models on a single card without GPU clusters, intensive cooling, or cloud dependency. Skymizer Taiwan Inc. unveiled the HTX301 inference chip, which enables 700-billion-parameter language models to run locally on a single PCIe card with 384 gigabytes of memory, consuming just 240 watts of power. This breakthrough challenges the conventional wisdom that only hyperscalers with massive GPU infrastructure can deploy the largest AI models.

Why Does Running Giant AI Models Locally Matter?

Deploying ultra-large models has historically required massive GPU clusters, high-speed interconnects like NVLink and NVSwitch, and expensive cooling systems. This infrastructure burden locked advanced AI capabilities behind prohibitive costs and operational complexity. The HTX301 changes that equation by disaggregating how AI models process information. Instead of forcing all computation onto the same silicon, Skymizer's approach separates the two fundamentally different phases of language model inference: prefill, which processes the input prompt and is compute-intensive, and decode, which generates tokens one at a time and is memory-bandwidth-intensive. The HTX301 is purpose-built for decode, while existing GPUs handle prefill, allowing each piece of hardware to do what it does best.

For enterprises, this shift unlocks three immediate advantages: data privacy, low latency, and operational control. When AI runs on-premise, sensitive information never leaves the building. There's no cloud dependency, no per-token spending anxiety, and no need to ration queries because infrastructure costs are fixed once deployed. Organizations can run unlimited inference at a predictable cost.

How Does This Technology Actually Work?

The HTX301 is the first reference chip implementing HyperThought, Skymizer's software and hardware co-design platform introduced at COMPUTEX 2025. The system scales flexibly across different form factors, from edge devices to mini data centers. A single card can integrate up to six HTX301 chips with memory ranging from 32 gigabytes to 384 gigabytes, supporting models from 4-billion to 700-billion parameters. This flexibility lets enterprises right-size their deployment to actual workload requirements without over-provisioning expensive hardware.

The architecture is powered by LISA, Skymizer's proprietary Language Instruction Set Architecture optimized specifically for transformer inference. The same LISA foundation runs on both the HTX301 enterprise card and HyperThought's on-device LPU, creating a unified deployment workflow from edge devices to data centers. Skymizer's unified software stack includes a KV-cache manager, phase-aware scheduler, and dynamic placement engine that orchestrates prefill and decode pools, carrying KV-cache state across nodes and rebalancing compute ratios in real time as workloads shift.

"The era of needing superscalar GPU clusters for ultra-large LLMs is over. HyperThought shifts AI from hyperscaler-only complexity to single-card simplicity for every enterprise," stated William Wei, Chief Marketing Officer at Skymizer.

William Wei, Chief Marketing Officer, Skymizer Taiwan Inc.

What Industries Benefit Most From On-Premise Inference?

The HTX301 is designed for agentic AI workflows, where AI systems autonomously complete multi-step tasks with full data sovereignty and deterministic latency. This capability unlocks new possibilities across multiple sectors:

• Financial Services: Compliance monitoring, fraud detection, and portfolio reasoning without sending sensitive trading data to the cloud.
• Healthcare and Life Sciences: Clinical decision support and drug interaction analysis where patient data confidentiality is non-negotiable.
• Manufacturing: Predictive maintenance and quality inspection using proprietary production data.
• Legal and Professional Services: Contract review and confidential knowledge retrieval over sensitive documents.
• Government and Defense: Sovereign AI and classified analysis that cannot leave secure facilities.
• Software Engineering: Private code copilots and autonomous CI/CD pipelines that protect intellectual property.
• Semiconductor and IC Design: On-premise RTL copilots, verification agents, and design-knowledge retrieval over proprietary IP.

The semiconductor and IC design use case exemplifies why on-premise inference matters. Design houses cannot send proprietary RTL (register transfer language) code to cloud-based AI assistants without risking exposure of multi-billion-dollar silicon intellectual property. The HTX301 delivers the throughput needed to run private code copilots and RTL generators entirely on-premise, eliminating cloud-exposure risk while preserving the productivity gains of AI-assisted engineering.

How to Deploy On-Premise AI Infrastructure

Organizations planning to move AI inference locally should consider these key implementation steps:

• Assess Workload Characteristics: Determine whether your inference workload is prefill-heavy (compute-bound) or decode-heavy (memory-bandwidth-bound). The HTX301 excels at decode-dominant workloads typical of real-world applications.
• Right-Size Hardware: Choose the appropriate configuration from 1 to 6 HTX301 chips per card, with memory scaling from 32 gigabytes to 384 gigabytes, based on your model size and throughput requirements.
• Plan for Model Management: Establish processes for model download, storage, and lifecycle management. Unlike cloud services, local models require active oversight of version control and updates.
• Design for Latency Expectations: Initial inference has a short latency before results start streaming. Build user interfaces that manage expectations and provide progress feedback.
• Monitor Performance Metrics: Use built-in monitoring tools to track model efficiency, resource utilization, and user experience quality over time.

What About Browser-Based Local AI?

While Skymizer targets enterprise infrastructure, a parallel trend is bringing AI directly into consumer browsers. Google Chrome and Microsoft Edge now support experimental APIs that enable on-device inference for tasks like summarization, translation, and language detection. Chrome integrates the Gemini Nano model, while Edge runs Phi-4-mini. As of April 2026, Chrome supports three main APIs: Translator, Language Detector, and Summarizer, with Edge supporting Translator and Summarizer, and Language Detector expected soon.

The Summarizer API exemplifies how local AI can move from concept to action. Developers can build browser-based tools that generate instant summaries of text executed entirely within the user's device, with no cloud dependency or external API calls. Everything happens locally, and models stay on the device after initial download, keeping subsequent operations fast and efficient. This capability changes how information is digested in business contexts. Imagine internal tools that automatically summarize lengthy reports or brief executives before meetings, all without sending proprietary content to the cloud.

How Is South Korea Positioning Itself in the AI Chip Race?

The broader AI chip market is experiencing explosive growth, particularly in regions with strong semiconductor manufacturing capabilities. South Korea's AI chip market is projected to reach $14.68 billion by 2032, growing from $2.49 billion in 2024, representing a compound annual growth rate of 19.4%. The GPU segment dominates with approximately 60 to 65 percent market share, driven by demand for AI training and high-performance computing workloads. However, the inference segment is projected to grow at the highest rate, driven by increasing adoption of AI applications across edge devices, smart electronics, and enterprise systems.

South Korea's leadership in memory technologies, particularly high-bandwidth memory (HBM), is strengthening its position in the global AI semiconductor value chain. The HBM segment is expected to register the highest growth with a projected compound annual growth rate of around 25 to 30 percent, supported by strong production capabilities of SK Hynix and Samsung. Emerging startups like Rebellions Inc., FuriosaAI, and DeepX are gaining traction through innovation in AI accelerators and increasing adoption in data center and edge AI applications.

The shift toward on-premise and edge inference represents a fundamental change in how enterprises deploy AI. Rather than relying on centralized cloud infrastructure, organizations are gaining the ability to run sophisticated models locally, maintaining data privacy, reducing latency, and controlling costs. Skymizer's HTX301 and the broader ecosystem of inference-optimized hardware signal that the era of GPU-only AI deployment is ending. The next phase belongs to purpose-built inference architectures designed for the real-world workloads that dominate enterprise AI today.