GSI Technology Reports 3-Second Time-to-First-Token for Edge Multimodal LLM Inference on Gemini-II
MWN-AI** Summary
GSI Technology, Inc. recently announced impressive benchmark results for its Gemini-II Compute-in-Memory processor, achieving a time-to-first-token (TTFT) of just 3 seconds for multimodal large language models (LLMs). This performance was demonstrated using the Gemma-3 12B vision-language model while consuming approximately 30 watts, making it the lowest reported TTFT for such a model on a low-power embedded edge processor. Competitors such as Qualcomm's Snapdragon X Elite and NVIDIA's Jetson Thor reported TTFTs of approximately 12 seconds and 3 seconds at over 100 watts, respectively.
The capabilities of the Gemini-II processor are tailored for edge computing environments, particularly in applications requiring rapid responses while adhering to strict power and thermal constraints. According to GSI's CEO, Lee-Lean Shu, the ability to generate initial responses every three seconds significantly enhances the usability of AI in video-based applications without missing pivotal moments. This efficient power usage, combined with low latency, positions the Gemini-II as a suitable option for a variety of "physical AI" sectors, including drones and smart cities, where battery life and thermal design are crucial.
GSI Technology’s focus on compute-in-memory technology aims to reduce data movement, a major contributor to latency and energy consumption in conventional architectures. As the demand for local inference grows—shifting workloads away from cloud-based models—GSI is positioned to capitalize on this transition. While the current benchmark results pave the way for future developments, GSI acknowledges the uncertainties inherent in market dynamics and customer relationships that may influence commercial success.
MWN-AI** Analysis
GSI Technology’s recent announcement of a remarkable 3-second time-to-first-token (TTFT) for its Gemini-II processor marks a significant step forward in edge computing, particularly in the multimodal AI landscape. This achievement not only positions GSI favorably against competitors like Qualcomm and NVIDIA but also highlights the growing importance of power efficiency in edge deployments. With Gemini-II's ability to handle complex tasks while consuming only 30 watts, it offers an attractive solution for sectors that demand both responsiveness and low power consumption, such as drones and smart city infrastructure.
Investors should monitor GSI Technology as it capitalizes on these advancements in “physical AI.” The shift from cloud to local inference is critical, with increasing demand for low-latency, real-time processing. GSI's competitive edge lies in its proprietary compute-in-memory architecture, designed to mitigate the typical latencies and energy consumption seen in conventional designs. The success of Gemini-II could lead to wider adoption of GSI's technology, driving revenue growth as enterprises seek to optimize their edge AI applications.
However, caution is warranted. The press release mentions various risks, including the preliminary nature of benchmark results and potential market adoption challenges. Therefore, while GSI's current trajectory appears promising, investors should weigh the inherent uncertainties, including supply chain constraints and competitive pressures.
In conclusion, GSI Technology stands at a critical junction where it could redefine edge computing. A strategic investment in GSI might be worthwhile for those willing to embrace the volatility typical of emerging tech markets. Nevertheless, ongoing evaluation of market conditions and company performance will be essential to make informed investment decisions.
**MWN-AI Summary and Analysis is based on asking OpenAI to summarize and analyze this news release.
SUNNYVALE, Calif., Jan. 29, 2026 (GLOBE NEWSWIRE) -- GSI Technology, Inc. (Nasdaq: GSIT), the inventor of the Associative Processing Unit (APU), a paradigm shift in artificial intelligence (AI) and high-performance compute processing, providing true compute-in-memory technology, today announced preliminary benchmark results for the Gemini-II Compute-in-Memory processor. These results demonstrated 3-second time-to-first-token (“TTFT”) performance for multimodal large language models operating at the edge with video and text inputs.
Using the Gemma-3 12B vision-language model on GSI’s production Gemini-II processor, GSI achieved the 3-second TTFT while consuming approximately 30 watts at the AI sub-system, including the chip. To GSI’s knowledge, this 3-second TTFT at approximately 30 watts at the AI sub-system is the lowest publicly reported result for a multimodal 12B model running on an embedded edge processor.
Independent third-party testing of the same workload on competitive embedded platforms reported TTFT measurements of roughly 12 seconds on Qualcomm Snapdragon X Elite with 30W power, and 3 seconds on NVIDIA Jetson Thor with over 100W power. With performance on par with or superior to competitive platforms at lower power usage levels, GSI concludes that Gemini-II offers a favorable responsiveness and power-efficiency profile for power- and thermally-constrained edge environments.
“These benchmark results highlight what compute-in-memory can enable for physical AI,” said Lee-Lean Shu, President and Chief Executive Officer of GSI Technology. “Edge deployments require fast response under tight power and thermal limits. A 3-second TTFT means the system can generate an initial response every three seconds, which is generally fast enough to be useful in video-based applications without missing meaningful events. Gemini-II’s ability to deliver low-latency multimodal inference at low power supports a broader range of real-time applications, from autonomous systems to intelligent machines operating outside the data center.”
GSI believes this performance profile is well-suited to “physical AI” markets, including drones, smart city, and other edge systems where workloads are episodic and constrained by battery life, thermal design, and form factor. Faster TTFT at lower chip power can enable more responsive systems, longer duty cycles, and lower total system cost.
Edge physical AI represents a growing segment of AI compute as workloads shift from cloud-assisted models to local inference to improve latency, reliability and operational efficiency. GSI’s proprietary compute-in-memory architecture is designed to reduce data movement, which is a primary contributor to latency and power consumption in conventional architectures.
GSI’s engineering team continues to work on further optimizing Gemini-II’s responsiveness while collaborating with customers and partners, including G2 Tech, on system integration and proof-of-concept activity. Benchmark results are intended to support ongoing evaluation and do not guarantee future commercial outcomes.
ABOUT GSI TECHNOLOGY
GSI Technology is at the forefront of the AI revolution with our groundbreaking APU technology, designed for unparalleled efficiency in billion-item database searches and high-performance computing. GSI’s innovations, Gemini-I® and Gemini-II®, offer scalable, low-power, high-capacity computing solutions that redefine edge computing capabilities. GSI Technology is headquartered in Sunnyvale, California, and has sales offices in the Americas, Europe, and Asia. For more information, please visit www.gsitechnology.com.
Forward-Looking Statements
The statements contained in this press release that are not purely historical are forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, including statements regarding GSI Technology’s expectations, beliefs, intentions, strategies, products, market opportunities and prospective customer engagements. All forward-looking statements included in this press release are based upon information available to GSI Technology as of the date hereof, and GSI Technology assumes no obligation to update any such forward-looking statements. Forward-looking statements involve a variety of risks and uncertainties, which could cause actual results to differ materially from those expected or implied.
GSI Technology’s participation in a proof-of-concept is exploratory in nature and may not result in any commercial contract, extended engagement, or recurring revenue. There can be no assurance that the scope, performance, or findings of any proof-of-concept will meet customer expectations or commercial requirements, or that such activities will lead to further business opportunities, order volume, or deploy-at-scale implementations. Additional risks and uncertainties that could cause actual results to differ materially from those expected or implied include, among others: the preliminary and limited nature of benchmark results; differences in workloads, configurations, measurement boundaries, and methodologies that can materially affect TTFT and power measurements; variability in model architectures, versions and toolchains that may impact performance; the pace and extent of adoption of “physical AI” at the edge and the impact of safety, privacy, and security requirements; supply-chain constraints affecting semiconductors, components, or manufacturing partners; GSI Technology’s historical dependence on sales to a limited number of customers and fluctuations in the mix of customers and products in any period; global public health crises that reduce economic activity; the rapidly evolving markets for its products and uncertainty regarding the development of these markets; the need to develop and introduce new products to offset the historical decline in the average unit selling price of its products; intensive competition; the continued availability of government funding opportunities; delays or unanticipated costs that may be encountered in the development of new products based on its in-place associative computing technology and the establishment of new markets and customer and partner relationships for the sale of such products; and delays or unexpected challenges related to the establishment of customer relationships and orders for its radiation-hardened and tolerant SRAM products. Many of these risks are currently amplified by and will continue to be amplified by, or in the future may be amplified by, economic and geopolitical conditions, such as changing interest rates, worldwide inflationary pressures, policy unpredictability, the imposition of tariffs, export controls and other trade barriers, military conflicts, particularly in relation to Taiwan, and a challenging global economic environment. These risks are discussed in more detail in GSI Technology’s most recently-filed Annual Report on Form 10-K, its Quarterly Reports on Form 10-Q and its other reports filed from time to time with the SEC. You are urged to review carefully and consider GSI Technology’s various disclosures in this press release and in its reports publicly disclosed or filed with the SEC that attempt to advise you of the risks and factors that may affect its business.
Source: GSI Technology, Inc.
Contacts:
Investor Relations
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Kim Rogers
541-904-5075
Kim@HaydenIR.com
Media Relations
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Ricca Silverio
415-348-2724
gsi@finnpartners.com
Company
GSI Technology, Inc.
Douglas M. Schirle
Chief Financial Officer
408-331-9802
FAQ**
How does GSI Technology Inc. (GSIT) plan to capitalize on the low power usage and fast TTFT performance of the Gemini-II processor to attract potential customers in the "physical AI" market segments?
Given the preliminary nature of the benchmark results announced by GSI Technology Inc. (GSIT), what steps will the company take to ensure the reliability and consistency of performance metrics moving forward?
Considering the competitive landscape highlighted by GSI Technology Inc. (GSIT), how does the company intend to maintain its technological edge against larger players like Qualcomm and NVIDIA in the edge computing space?
What are the specific risks identified by GSI Technology Inc. (GSIT) related to supply chain constraints that could affect future production and delivery of the Gemini-II processor?
**MWN-AI FAQ is based on asking OpenAI questions about GSI Technology Inc. (NASDAQ: GSIT).
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