To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
The technical storage or access that is used exclusively for statistical purposes.
The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
BC
October 1, 2025The technical skills plateau is hitting me harder than I expected. I’ve been benchmarking hardware across multiple systems – testing everything from 8B models on a 3060 12GB up to 32B models on my 4070 Ti Super and 4080 Super setups. Once you’ve optimized GPU offload parameters, dialed in quantization settings, and mapped out VRAM bottlenecks, you’re just running variations on known configurations. The troubleshooting process itself taught me more than the successful runs – discovering that Qwen 32B models have architecture-specific incompatibilities with certain GGUF implementations wasn’t in any documentation, it came from systematic testing across different model families.
The “AI-proofing through corporate complexity” argument underestimates how quickly technical moats erode. I’m running local models that generate production-quality code and comprehensive analyses without specialized knowledge. The gap between “freelancer solving discrete technical problems” and “irrelevant” is narrower than she suggests. What matters is whether you’re building genuine expertise or just executing repeatable workflows. My hardware testing lab generates real insights into performance characteristics, compatibility issues, and optimization strategies that better models cannot replicate. But if I were just refreshing the same dashboards or writing similar white papers, I’d be in the same vulnerable position regardless of employment structure.
The RAM bottleneck I hit with 32GB trying to run 22B models mirrors her technical plateau. You can’t learn what your hardware physically can’t run. Upgrading to 64GB or 128GB unlocks entirely different capability tiers – suddenly 70B models at F16 precision become testable rather than theoretical. Infrastructure investment drives learning opportunities, whether that’s corporate training budgets or personal hardware spending. The difference is control over the investment direction.