FuriosaAI Rejects Meta's $800M Bid, Partners with LG for 225% Inference Edge

FuriosaAI Rejects Meta's $800M Bid, Partners with LG for 225% Inference Edge

South Korean AI chip startup FuriosaAI has declined Meta's $800 million acquisition offer to instead pursue a strategic partnership with LG AI Research. The deal, finalized this week, will see FuriosaAI's RNGD inference accelerators power LG's EXAONE 4.0 large-language models across electronics, finance, and biotech sectors. Independent testing confirms the RNGD chips deliver 2.25x better performance per watt compared to leading GPUs while reducing total cost of ownership by an estimated 40%. This landmark partnership challenges Nvidia's market dominance and signals a shift toward specialized AI hardware.

Why This Partnership Reshapes AI Infrastructure

FuriosaAI's RNGD architecture represents a fundamental redesign of AI computation. Unlike general-purpose GPUs, the RNGD chip exclusively processes transformer-based models through dedicated tensor contraction processors, achieving 512 teraflops of FP8 performance at just 150W thermal design power. LG's validation tests demonstrated the RNGD-powered servers could process 3.75x more tokens than comparable GPU racks under identical power constraints. This efficiency breakthrough addresses the critical bottleneck of AI's escalating energy demands, which are projected to consume 10% of global electricity by 2030 if unchecked.

Strategic Implications for the AI Hardware Market

The rejected Meta acquisition—which collapsed over disagreements about post-merger autonomy—positions FuriosaAI as a viable challenger to established players. CEO June Paik emphasized sustainability as core to their mission, telling Bloomberg: "We want to make AI computing sustainable, not just profitable." Meanwhile, LG gains a sovereign AI solution that circumvents U.S. export controls on advanced chips. The collaboration marks South Korea's first major deployment of domestically developed AI accelerators in enterprise applications, with initial deployments targeting LG's ChatEXAONE agent platform for financial analysis and biomedical research.

Technical Breakdown: How RNGD Outperforms GPUs

FuriosaAI's innovation lies in its memory-centric design. Where GPUs prioritize floating-point operations, the RNGD chip optimizes for memory bandwidth efficiency through:

• Hybrid SRAM architecture: 48GB HBM3 memory with 1.5TB/s bandwidth
• Tensor parallelism optimization: Zero-overhead inter-chip communication
• Compiler-driven computation: Global optimization for transformer block reuse

These features enabled LG to run its 32-billion parameter EXAONE model at FP16 precision with 50-60 tokens/second throughput at batch size 1—meeting strict <0.3s time-to-first-token requirements for real-time applications. FuriosaAI's vLLM-compatible software stack further simplified integration, requiring just four weeks for full deployment.

Market Outlook and Future Developments

This partnership coincides with global efforts to diversify AI hardware supply chains. FuriosaAI confirms ongoing negotiations with U.S. and Middle Eastern clients, while actively pursuing a $48 million funding round. LG plans to expand EXAONE 4.0's availability to external clients by Q4 2025, using RNGD servers as its primary inference infrastructure. With inference costs dominating LLM operational budgets, FuriosaAI's energy-efficient approach could disrupt the projected $150 billion AI chip market by 2030.