Holistic Optimization of AI Inference Systems

As the axis of AI competition shifts from "bigger" to "more efficient," the core challenge has become how to run large-scale AI inference reliably at the lowest possible cost. Simply using the latest GPUs isn't enough—model, compilation, memory, communication, and scheduling must all be addressed together.

The starting point for efficiency optimization is model compression. Models are mainly trained in BF16 or FP32, but for inference, lower precisions like FP8 or INT8 can be used without significant loss. The article explains that FP8 operations can use up to 10x less energy than FP32, INT8 can be about 2x more efficient than FP8, and FP8/INT8 weights take up 4x less memory compared to FP32. It also notes that moving a 32-bit float from HBM can cost up to 50x more than an FP32 multiplication, making it important to have a structure that reduces memory movement rather than just computation.

The following compression techniques are highlighted:

• Pruning: Removes unimportant channels or weights to shrink the model. Structured pruning removes entire channels or layers, while unstructured pruning zeros out individual weights to increase sparsity.
• Knowledge distillation: Trains a smaller student model based on the outputs of a larger teacher model. In some cases, size can be cut roughly in half while largely suppressing accuracy loss.
• Quantization: Converts BF16/FP32 operations to INT8/FP8 to lower memory and compute costs. Quantization during training is more sophisticated, while post-training quantization is advantageous when retraining is difficult.

The second axis is Parallelism and disaggregated execution. If a model doesn't fit on a single device, it must be split across multiple devices—and even if it does fit, distributed execution may be better for throughput or latency. Representative approaches are pipeline parallelism and tensor parallelism, and recently, approaches like E-P-D (encode-prefill-decode), which separate specific stages of an LLM to run on different devices, have also drawn attention.

• Pipeline parallelism sequentially assigns layers to different devices. Communication overhead can increase latency, but it makes it easier to increase batch size, which can improve overall throughput.
• Tensor parallelism splits a single operation itself across multiple devices. For operations that parallelize well, such as large matrix multiplications, it increases throughput and lowers the memory burden per device.
• However, operations that require gathering the entire input, such as reduction or softmax, incur synchronization costs.

The third key element is the KV-cache. Since it's both essential and a bottleneck in LLM inference, new techniques for distributing or compressing it continue to emerge. The fourth is batching—instead of processing requests one by one, batching them together significantly raises throughput and hardware utilization.

The fifth is code generation. AI compilers translate models into hardware-specific optimal instructions to boost execution efficiency. Last is scheduling, where the key question is how to distribute actual user traffic across multiple models and multiple devices. Serving everything from foundation models to less popular models and fine-tuned versions together requires scheduling that finely manages available resources.

The core message is simple: there is no single solution for AI inference optimization. Model precision, execution method, compilation, memory movement, batching, and scheduling must all be designed together across every layer. Ultimately, the winner isn't determined by the fastest GPU, but by holistic system design that makes the smartest use of constrained resources.