LaoZhang AI Team writes that the best choice for a 16GB GPU for local LLM coding depends on whether one prioritizes cost, speed, or balance, with the RTX 5070 Ti recommended as the strongest all-around option. The article emphasizes that buyers should prioritize "accepted work"—the ability to complete tasks within their specific repository context—rather than chasing theoretical peak performance metrics like TOPS. If a model's required context exceeds 16GB of VRAM, upgrading to a faster card will not solve the problem; in such cases, moving to a 24GB card or using hosted services is necessary.
- A true "VRAM fit" must account for KV cache and runtime buffers, which can exceed advertised capacities during long context tasks.
- The RTX 5060 Ti (180W) is noted as an efficient budget choice if latency requirements are met.
- Testing should follow a three-step ladder: smallest functional context, normal multi-file task, and one extreme stress case.
- "Accepted work" is defined by the ability to produce patches that pass all unit tests and static checks without manual core repairs.
- AMD's Radeon RX 9060 XT offers strong capacity-per-watt but requires verifying OS/driver compatibility (ROCm) before purchase.
- A true "VRAM fit" must account for KV cache and runtime buffers, which can exceed advertised capacities during long context tasks.
- The RTX 5060 Ti (180W) is noted as an efficient budget choice if latency requirements are met.
- Testing should follow a three-step ladder: smallest functional context, normal multi-file task, and one extreme stress case.
- "Accepted work" is defined by the ability to produce patches that pass all unit tests and static checks without manual core repairs.
- AMD's Radeon RX 9060 XT offers strong capacity-per-watt but requires verifying OS/driver compatibility (ROCm) before purchase.
