FuriosaAI announced on July 22, 2025, that LG AI Research had adopted its RNGD inference accelerator to run LG’s EXAONE models. The planned enterprise offering is an AI server relationship—not a merger, an LG-wide buying commitment, or a consumer product. The companies have not disclosed order volume, contract value, or system pricing.
What the LG–FuriosaAI deal covers
LG AI Research selected FuriosaAI’s RNGD accelerator for EXAONE inference after evaluating performance, energy efficiency, and the software stack. FuriosaAI said the companies would offer RNGD Server to enterprise customers using EXAONE, including in electronics, finance, telecommunications, and biotechnology. The announcement describes an enterprise adoption and planned supply relationship; it does not establish that every LG business will buy the hardware. FuriosaAI’s July 2025 announcement
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That distinction matters to customers and investors: the announcement does not give a contract value, order quantity, per-system price, or customer-by-customer deployment count. It also does not establish ordinary consumer retail availability.
What FuriosaAI reported about performance
FuriosaAI said LG AI Research evaluated EXAONE 3.5 models with 7.8 billion and 32 billion parameters at 4K and 32K context windows. In that evaluation, RNGD delivered 2.25 times better LLM inference performance per watt than the GPU-based solution used for comparison, while meeting LG’s requirements. This is a vendor-reported result tied to that evaluation, not proof that RNGD is universally more efficient than GPUs. FuriosaAI’s reported evaluation
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- PCIe & USB 4.0 v2: Up to three PCIe slots can be equipped, including a dual-slot x16 GPU. The main slot supports PCIe 5.0, meeting the needs of high-bandwidth creative and computing workloads. USB 4.0 v2 (80Gbps) supports high-bandwidth external storage and displays.
- Ultra-fast Networking: Wi-Fi 7 further enhances wireless performance with next-generation speeds and low-latency stability. Intelligent bandwidth switching optimizes throughput in different network environments, ensuring optimal performance for enterprise or local networks. Dual 25GbE ports (providing up to approximately 3.125 GB/s bandwidth, about 25 times faster than traditional 1GbE), enabling seamless large-scale file transfers and parallel computing. 10GbE and 2.5GbE ports, with support for Intel vPro technology, ensure enterprise-grade remote management and deployment flexibility.
- Server-grade thermal architecture: Utilizing a dedicated CPU/GPU airflow design, equipped with a 6-pipe dual-fan cooler, it maintains stable performance even under sustained loads, delivering up to 140W Turbo power while maintaining a 100W TDP, and operating with noise levels as low as 36 dB. An integrated 350W power supply ensures stable and reliable output for demanding computing tasks and fully loaded extended configurations.
The same announcement gives selected throughput figures for one server with four RNGD cards running EXAONE 3.5 32B at batch size one:
| Context window | Reported throughput | Configuration and source |
|---|---|---|
| 4K | 60 tokens per second | One server with four RNGD cards, batch size one; reported by FuriosaAI in 2025. |
| 32K | 50 tokens per second | One server with four RNGD cards, batch size one; reported by FuriosaAI in 2025. |
FuriosaAI described an RNGD Server as eight accelerators in an air-cooled 4U chassis. The four-card test configuration above should not be confused with that server description. The announcement does not provide enough detail to independently reproduce its headline comparison. A meaningful GPU comparison would need the same model and precision, batch size, context length, latency target, server configuration, power and cooling assumptions, software support, and cost accounting.
What the 2026 LG U+ appliance adds
In March 2026, FuriosaAI said it and LG U+ had launched a Sovereign AI Appliance combining an RNGD accelerator, LG’s EXAONE 4.0 model, and the ixi-Enterprise platform. FuriosaAI claimed 30% lower total cost of ownership than traditional GPU clusters; that is a company claim, not an independently verified result. FuriosaAI’s March 2026 announcement
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LG U+’s March 9 newsroom post describes a March 4 memorandum of understanding to develop the appliance. It says the system is designed to process data on-premises rather than send it to an external cloud, and describes possible future cooperation in NPU-as-a-Service and physical AI. Because FuriosaAI calls the appliance launched while LG U+ describes an MOU to develop it, the sources establish an announcement and development relationship, not general commercial availability or published order terms. LG U+ newsroom post, March 9, 2026
What buyers should verify before comparing it with GPUs
The reported efficiency and throughput numbers are useful starting points, but they do not by themselves establish what an enterprise will pay or save. A buyer assessing an RNGD deployment against GPU infrastructure should request comparable results for its own workload and confirm:
- Model version, precision, context length, batch size, and expected latency.
- Tokens per second under those same conditions, including the number and type of accelerators in each server.
- Power draw, cooling needs, rack capacity, software support, and integration requirements.
- Total cost using the same workload and time horizon, including hardware, deployment, and operating costs.
- Availability, delivery timing, support terms, and any customer-specific commercial commitments.
What LG AI Research said
Kijeong Jeon, Lead, Product Unit, LG AI Research, said in FuriosaAI’s July 22, 2025 announcement: “After extensively testing a wide range of options, we found RNGD to be a highly effective solution for deploying EXAONE models. RNGD provides a compelling combination of benefits: excellent real-world performance, a dramatic reduction in our total cost of ownership, and a surprisingly straightforward integration,” The statement is LG AI Research’s view as quoted by FuriosaAI, not an independent analyst assessment.
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