Search and recommendation (S&R) are two integral components of modern online platforms, both aiming to model and satisfy user information needs. This shared objective motivates a unified modeling paradigm that enables richer user modeling and improves the effectiveness of both tasks. Recent attempts to unify S&R formulate item ranking in both tasks as conditional generation. While this paradigm is promising, existing methods rely on full fine-tuning, which is computationally expensive and limits scalability. Parameter-efficient fine-tuning (PEFT) offers a more practical alternative but faces two critical challenges in unifying S&R: (1) gradient conflicts across tasks due to divergent optimization objectives, and (2) shifts in user intent understanding caused by overfitting to fine-tuning data, which distort general-domain knowledge and weaken LLM reasoning. To address these issues, we propose Gradient Multi-Subspace Tuning (GEMS), a novel framework that unifies S&R with LLMs while alleviating gradient conflicts and preserving general-domain knowledge. GEMS introduces (1) Multi-Subspace Decomposition, which disentangles shared and task-specific optimization signals into complementary low-rank subspaces, thereby reducing destructive gradient interference, and (2) Null-Space Projection, which constrains parameter updates to a subspace orthogonal to the general-domain knowledge space, mitigating shifts in user intent understanding. Extensive experiments on benchmark datasets show that GEMS consistently outperforms the state-of-the-art baselines across both search and recommendation tasks, and the gains remain consistent when scaling to billion-parameter LLMs.