Enterprise LLMs in the wild: performance bottlenecks, licensing, and legal risk

Enterprise LLMs are moving from hype to hard-won practice: throughput, backend choices, and risk governance are now front and center.

Throughput and backend choices IBM Granite 4.0 is marketed as hyper-efficient, high-performance hybrid models for enterprise. Yet a real-world user reports only about 8 tokens per second when running llama.cpp with the Vulkan backend on a 24GB GPU, suggesting a backend bottleneck. The chatter hints that ROCm could be faster, underscoring how runtime stack matters as much as model design ^[1].

Licensing and model collection Granite 4.0 Language Models - a ibm-granite Collection bundles 32B-A9B, 7B-A1B, and 3B dense models, with GGUFs in the same repo. We’re told weights are available to everyone, highlighting a posture toward local deployment and broad accessibility ^[2]. The suite is lauded for fast on-device parsing and even for coding-focused directions, reflecting a broader move to practical, self-contained deployments ^[2].

Legal risk and liability On the legality front, LegalDeep AI promises to scan contracts offline, flag risks, and offer plain-English explanations, with SOC 2–compliant infrastructure and 10K+ contracts used in training. It claims to identify risks in 12 minutes (vs. 2 hours for a human) and highlights a central question: can you sue for damages if a review is flawed, and who bears liability as enterprises adopt such tools? These questions echo the broader tension between efficiency gains and enforceable responsibility ^[3].

Bottom line: throughput, licensing, and liability shape enterprise adoption more than hype alone—watch governance and data rights as closely as the models themselves.

Post IDs referenced: 1, 2, 3

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