RaiderChip Hardware NPU adds Falcon-3 LLM to its supported AI models
The model, launched by Abu Dhabi’s Technology Innovation Institute (TII), runs seamlessly in RaiderChip’s AI accelerator and its FPGA based demonstrator.
Spain, December 19th, 2024 -- On December 17th, the Technology Innovation Institute (TII) of Abu Dhabi (United Arab Emirates) launched Falcon 3, a new open-source generative AI model available in four sizes: 1B, 3B, 7B, and 10B parameters. Categorized as a small model by its size, Falcon 3 delivers outstanding performance when compared to competing LLMs in its category.
Thanks to RaiderChip technology’s ease of use, implementing support for new LLM AI models is straight-forward and transparent. Only a firmware update is needed to accelerate Falcon-3 inference fully inside its hardware NPU.
RaiderChip NPU running, fully standalone and offline, the Falcon-3 1B LLM with BF16 weights
Falcon 3’s design, optimized for lightweight infrastructure efficiency, is particularly appealing to RaiderChip as it aligns with the company’s strategy to offer generative AI solutions that are embeddable on low-cost hardware with limited resources. According to Victor Lopez, RaiderChip’s CTO, “The fact that Falcon models are available in various sizes, including quantized versions, allows us to provide our customers with fast and efficient inference across a wide range of edge and embedded devices using our state-of-the-art NPU, which provides Generative AI in fully offline and standalone devices.”
The introduction of small models, capable of delivering extraordinary reasoning, comprehension, and generation capabilities across multiple languages, enhances the scope and quality of generative AI applications based on FPGAs. These include conversational assistants, recommendation systems, and data analysis platforms, among others —an expanding list expected to grow significantly in the coming months and years.
RaiderChip’s commitment to low-cost FPGAs contributes to the diversification and democratization of access to advanced artificial intelligence. This aligns with Falcon 3’s philosophy of offering powerful and accessible models to all users. Thanks to RaiderChip’s NPU, Falcon 3 can now run locally, offline, and on-premises across a greater number of new devices, unlocking new opportunities for generative AI at the edge.
Companies interested in trying the Generative AI NPU may reach out to RaiderChip for access to our FPGA demo or a consultation on how our IP cores can accelerate their AI workloads.
More information at https://raiderchip.ai/technology/hardware-ai-accelerators
|
Related News
- RaiderChip launches its Generative AI hardware accelerator for LLM models on low-cost FPGAs
- RaiderChip NPU for LLM at the Edge supports DeepSeek-R1 reasoning models
- RaiderChip unveils its fully Hardware-Based Generative AI Accelerator: The GenAI NPU
- Ceva and Edge Impulse Join Forces to Enable Faster, Easier Development of Edge AI Applications
- AMD Completes Acquisition of Silo AI to Accelerate Development and Deployment of AI Models on AMD Hardware
Breaking News
- Europe takes a major step towards digital autonomy in supercomputing and AI with the launch of DARE project
- Infineon brings RISC-V to the automotive industry and is first to announce an automotive RISC-V microcontroller family
- EnSilica Secures €2.13 Million European Space Agency Development Contract
- indie Semiconductor and GlobalFoundries Announce Strategic Collaboration to Accelerate Automotive Radar Adoption
- Silvaco Expands Product Offering with Acquisition of Cadence's Process Proximity Compensation Product Line
Most Popular
- Pragmatic Semiconductor launches next-generation platform for mixed-signal flexible ASIC design with early-access programme
- Semiconductor Industry Faces a Seismic Shift
- Arm vs. Qualcomm: The Legal Tussle Continues
- Quintauris launches the first RISC-V profile for today's real-time automotive applications
- eMemory and PUFsecurity Launch World's First PUF-Based Post-Quantum Cryptography Solution to Secure the Future of Computing
![]() |
E-mail This Article | ![]() |
![]() |
Printer-Friendly Page |