Optimizing large language models for modern AI accelerators requires a fundamental shift from pure algorithmic design to hardware-aware co-design. As generative AI workloads scale, deployment success hinges on balancing three critical dimensions: model accuracy, system throughput, and user interactivity. Prioritizing one at the expense of others degrades practical utility, making it essential to architect models that maximize the performance frontier across varying compute regimes. Performance is fundamentally governed by arithmetic intensity, which dictates whether a workload is constrained by memory bandwidth or peak compute throughput. Throughput-heavy deployments must push operations into the compute-bound region, while latency-sensitive decoding operates in a memory-bound regime where minimizing data movement is paramount. This dynamic makes model dimensioning a strategic priority. Developers are advised to favor wider architectures over deeper ones, as wider models improve weight reuse, shorten sequential execution paths, and raise arithmetic intensity. Additionally, linear layer dimensions must align precisely with underlying GPU tile geometries. Avoiding disproportionately small projection or reduction dimensions prevents inefficient data transfers, while selecting dimensions that are multiples of hardware tile sizes maximizes tensor core utilization and eliminates padding waste. Quantization further amplifies performance gains without compromising fidelity. Low-precision formats leverage hierarchical scaling to maintain high accuracy while drastically reducing memory traffic. By designing linear layers to efficiently execute in reduced precision, developers can unlock substantial throughput improvements for matrix-heavy operations. Parallelism strategy must also evolve alongside model architecture. For Mixture-of-Experts systems, expert parallelism serves as the primary lever for throughput-oriented serving. Distributing sparse experts across multiple GPUs increases effective concurrency, improves memory bandwidth utilization, and reduces per-GPU weight storage, directly accelerating feed-forward network execution. Conversely, latency-driven workloads benefit from disaggregated prefill and decode phases. Techniques like chunked pipeline parallelism split sequences and layers to minimize first-token latency without sacrificing per-GPU throughput. For highly interactive applications, decoupling attention and feed-forward parallelization allows systems to target distinct bottlenecks independently, leveraging advanced key-value cache sharding and hybrid tensor-expert strategies to sustain responsiveness under heavy load. These co-design principles signal a broader industry transition toward systems where model topology, precision, and parallelization are engineered in tandem with silicon capabilities. By adhering to structured dimensioning, precision-aware deployment, and regime-specific parallelization, developers can significantly reduce infrastructure costs, accelerate deployment cycles, and deliver consistently responsive AI services at scale.