For the first time, enterprises can run 700-billion-parameter AI models on a single PCIe card without needing massive GPU clusters, high-speed interconnects, or intensive cooling systems. Skymizer Taiwan Inc., an AI inference company, unveiled its HTX301 inference chip ahead of COMPUTEX 2026, powered by HyperThought, a software and hardware co-design platform that fundamentally rethinks how large language models (LLMs) are deployed on-premises.

Why Does Running Giant AI Models on One Card Matter?

Historically, deploying ultra-large AI models required enterprises to build or rent massive data center infrastructure. Companies needed multiple graphics processing units (GPUs), specialized high-speed connections like NVLink or NVSwitch, and expensive cooling systems just to run a single model. This created a barrier that only hyperscalers like OpenAI, Google, and Meta could easily afford. The HTX301 changes that equation by delivering what Skymizer calls "single-card simplicity for every enterprise".

A single card equipped with six HTX301 chips and 384 gigabytes of memory can run 700-billion-parameter model inference at approximately 240 watts per card. To put that in perspective, traditional GPU-based setups for models of this scale consume significantly more power and require dedicated infrastructure teams to manage. The architecture scales flexibly, supporting models ranging from 4 billion to 700 billion parameters, allowing companies to right-size their deployment to actual workload requirements without over-provisioning.

How Does HyperThought Solve the Memory Bandwidth Problem?

The breakthrough behind HyperThought lies in understanding how LLM inference actually works. Large language models operate in two distinct phases: prefill and decode. Prefill is when the model processes your entire input prompt at once, a compute-intensive task. Decode is when the model generates output tokens one at a time, a memory-bandwidth-intensive task that dominates real-world inference latency. Traditional GPU infrastructure forces both phases onto the same silicon, which means either compute power or memory bandwidth sits idle depending on which phase is running.

Skymizer's approach disaggregates these phases by design. The HTX301 is purpose-built for decode, the memory-bandwidth-hungry token generation that determines how fast users see responses. Existing GPUs handle the compute-dense prefill work. A unified software stack then orchestrates these separate pools, managing the key-value cache state across nodes and dynamically rebalancing the prefill-to-decode ratio as workloads shift in real time.

"Purpose-built decode hardware paired with an intelligent software stack that orchestrates every inference workload, that's how you disaggregate P/D at scale," explained Luba Tang, Chief Technology Officer at Skymizer.

Luba Tang, Chief Technology Officer, Skymizer Taiwan Inc.

What Problems Does On-Premises Inference Solve for Enterprises?

Moving AI inference in-house eliminates several pain points that have quietly constrained enterprise AI adoption. Cloud-based inference forces teams to ration queries and throttle AI agents because every token processed costs money. With on-premises deployment, enterprises run unlimited inference at a fixed infrastructure cost once the hardware is deployed. This removes what Skymizer calls "the silent tax on enterprise AI adoption," the per-token spending anxiety that makes teams hesitant to deploy AI agents widely.

On-premises inference also delivers three critical advantages:

• Data Privacy: Sensitive information stays within company firewalls, eliminating the risk of proprietary data being exposed to cloud providers or third parties.
• Low Latency: Inference happens locally without network round-trip delays, enabling real-time applications and responsive user experiences.
• Operational Control: Enterprises maintain full control over their AI infrastructure, deployment schedules, and performance tuning without depending on cloud provider availability or pricing changes.

This matters especially in industries handling confidential information. IC design houses cannot send proprietary register-transfer language (RTL) code to cloud-based AI assistants without risking exposure of multi-billion-dollar silicon intellectual property. Software companies face the same calculus with confidential codebases and customer data. The HTX301 delivers the throughput needed to run private code copilots, RTL generators, and verification agents entirely on-premises, eliminating cloud-exposure risk while preserving productivity gains from AI-assisted engineering.

How to Deploy Agentic AI Workflows Across Your Enterprise

HyperThought and the HTX301 are specifically designed for agentic AI workflows, where AI systems autonomously complete multi-step tasks with minimal human intervention. These systems are rapidly becoming the backbone of enterprise automation. Combined with agent harness frameworks such as OpenClaw, the HTX301 delivers the inference throughput these systems demand with full data sovereignty and deterministic latency.

• Financial Services: Deploy AI agents for compliance monitoring, fraud detection, and portfolio reasoning without sending transaction data to external cloud services.
• Healthcare and Life Sciences: Run clinical decision support systems and drug interaction analysis agents on-premises, keeping patient data and proprietary research protected.
• Manufacturing: Use predictive maintenance agents and quality inspection systems that analyze production data locally without exposing operational secrets to competitors.
• Legal and Professional Services: Build contract review agents and confidential knowledge retrieval systems that never expose client documents or privileged information to cloud infrastructure.
• Government and Defense: Deploy sovereign AI systems and classified analysis agents with full control over data residency and security protocols.
• Retail: Run service automation and inventory reasoning agents that process customer and supply chain data locally.
• Software Engineering: Deploy private code copilots and autonomous continuous integration/continuous deployment (CI/CD) agents that never expose source code to external systems.
• Semiconductor and IC Design: Use on-premises RTL copilots, verification agents, and design-knowledge retrieval systems that protect multi-billion-dollar intellectual property.

What Is LISA and Why Does It Matter?

HyperThought is powered by LISA, Skymizer's proprietary language instruction set architecture (ISA) optimized specifically for transformer inference. An instruction set architecture is the fundamental design that defines how a processor executes instructions. LISA drives performance, power efficiency, and scalability from edge devices to enterprise clusters. The on-premises HTX301 card shares the same LISA architectural foundation as HyperThought's on-device language processing unit (LPU), meaning enterprises deploy one instruction set architecture across edge devices and data centers.

"Inference has become the dominant AI workload, and infrastructure needs to reflect that reality. 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.

The unified architecture means developers write code once and deploy across multiple form factors, from edge servers and AI workstations to smart network-attached storage (NAS) systems and intelligent endpoints. This simplifies the operational burden of managing AI infrastructure across an organization.

How Does This Complement Existing GPU Infrastructure?

HyperThought is not designed to replace GPU infrastructure entirely. Instead, it complements existing GPU clusters by offloading decode-heavy inference from GPUs. This improves overall cluster utilization and power efficiency. GPUs excel at the compute-intensive prefill phase, while the HTX301 handles the memory-bandwidth-intensive decode phase. By separating these workloads, enterprises squeeze more productivity from their existing GPU investments while reducing overall power consumption.

Skymizer plans to share details on HyperThought's extended platform roadmap at its press conference at COMPUTEX 2026. The company is accepting early access requests for the HTX301 through its website.