Top Stories
How Nota AI Efficiently Optimized and Compressed Solar Open 2 with Backend.AI FastTrack 3
By LablupWithin the Sovereign AI Foundation Model Project, Nota optimized large-scale LLMs to run on fewer GPUs through model compression, while using Backend.AI and FastTrack to streamline experimentation and resource management.31 July 2026
Lablup - FuriosaAI RNGD Whitepaper Released
By LablupLablup and FuriosaAI have published "RNGD meets Backend.AI," a whitepaper documenting the performance and operational efficiency of the RNGD and Backend.AI combination under real LLM workloads.29 July 2026
Serving Solar Open 2 with Two DGX Spark Systems
By Kyujin Cho, Jinho HeoWe share how we enabled Solar Open 2 NVFP4 on dual DGX Sparks with vLLM by resolving FlashInfer b12x Expert Parallelism and checkpoint limitations.24 July 2026
News
Lablup - FuriosaAI RNGD Whitepaper Released
By LablupLablup and FuriosaAI have published "RNGD meets Backend.AI," a whitepaper documenting the performance and operational efficiency of the RNGD and Backend.AI combination under real LLM workloads.29 July 2026
Lablup adds Intel Arc Pro B70 support to Backend.AI
By LablupBackend.AI now officially supports the Intel Arc Pro B70 workstation GPU, expanding its hardware coverage beyond Intel Gaudi 2 and 3 AI accelerators to include the Arc graphics lineup. This enables unified management of Intel AI hardware across environments, from data center Gaudi to workstation-class Arc Pro, within a single platform. Backend.AI supports diverse GPUs and accelerators with an intuitive interface and session-based resource management, simplifying AI model development, training, and deployment. With 32GB memory and high throughput, Arc Pro B70 is optimized for agentic AI workloads, improving concurrency and KV cache efficiency.12 June 2026
Lablup at AI EXPO KOREA 2026: Booth Highlights
By LablupLablup wrapped up AI EXPO KOREA 2026 at booth F04. A three-day recap, from Backend.AI's AI infrastructure orchestration to AI:GO running models and autonomous agents on a laptop.15 May 2026
Releases
Lablup Releases 'mlxcel,' an Open-Source AI Inference Engine Optimized for Apple Silicon
By LablupLablup open-sources mlxcel, an AI inference engine optimized for Apple Silicon (M1 to M5) and NVIDIA CUDA. Built in pure Rust with no Python runtime, it delivers 119% of mlx-lm's decode throughput and supports 80+ model architectures.18 May 2026
Release: Backend.AI FastTrack 3 25.18
By LablupA rundown of the major changes in Backend.AI FastTrack 3 25.18.5 January 2026
Release: Backend.AI 25.15 (LTS)
By LablupBackend.AI 25.15 LTS is here, with broad system and UX optimization that strengthens reliability and scalability for large-scale AI training and deployment.2 October 2025
Engineering
Serving Solar Open 2 with Two DGX Spark Systems
By Kyujin Cho, Jinho HeoWe share how we enabled Solar Open 2 NVFP4 on dual DGX Sparks with vLLM by resolving FlashInfer b12x Expert Parallelism and checkpoint limitations.24 July 2026
Is Korean really a low-resource language?
By Wonik Cho, Youngsook SongThe claim that Korean is "low-resource" is only half true: the data is not missing, just scattered, closed off, and poorly known. Drawing on the Open Korean Corpora report, this article sorts 100 open Korean corpora into 10 categories and marks each by three criteria—documentation, usage, and redistribution. The result is a single map that lets anyone see, at a glance, what Korean data exists and how it can actually be used.1 July 2026
Backend.AI on DGX Spark: Open source installation guide
By Kyujin Cho and 3 othersWe provide a step-by-step tutorial on how to install the open-source version of Backend.AI on NVIDIA DGX Spark and easily launch a model.22 June 2026
Customer Story
How Nota AI Efficiently Optimized and Compressed Solar Open 2 with Backend.AI FastTrack 3
By LablupWithin the Sovereign AI Foundation Model Project, Nota optimized large-scale LLMs to run on fewer GPUs through model compression, while using Backend.AI and FastTrack to streamline experimentation and resource management.31 July 2026
Let researchers focus on research with Backend.AI
By LablupML research scientist Eunseong Choi explains how using Backend.AI on Sungkyunkwan University’s on‑premise supercomputing center reduced the overhead of managing GPU infrastructure and gave his lab a consistent multi‑GPU environment to focus on research.25 June 2026
Behind Lablup x Upstage's Phase 1 Win for the Sovereign AI Foundation Model Project
By LablupThe Upstage consortium, with Lablup as infrastructure partner, passed Phase 1 of Korea's Sovereign AI Foundation Model program. Team members share the behind-the-scenes story.6 February 2026
