Baseten Framework Maps the 'Efficient Frontier' of LLM Inference
A new technical guide distinguishes between techniques that balance latency and throughput versus those that fundamentally increase system efficiency.
Baseten has released a technical guide detailing the "efficient frontier" of LLM inference engineering to help developers optimize model deployments. The framework provides a structured approach to managing the inherent tension between system speed and total capacity.
According to Baseten, inference engineering is divided into two categories: techniques that manage tradeoffs between existing performance metrics and those that push the entire frontier forward to achieve universal efficiency gains. The primary conflict in these deployments is typically between latency—the speed at which a single user receives a response—and throughput, which is the total number of tokens a system can generate per second. This balance directly dictates the operational cost of running large-scale models.
Managing the Tradeoffs
For engineers moving along the existing frontier, Baseten identifies several key levers. Batch sizing is a primary tool for balancing per-user latency against total throughput. Parallelism strategies also play a critical role; Tensor Parallelism is recommended for reducing latency, while Expert Parallelism and Attention Data Parallelism are better suited for maximizing throughput.
Quantization is highlighted as a "jagged frontier," where engineers can often secure significant efficiency gains with minimal impact on model quality. The guide specifically points to formats such as MXFP4 and NVFP4 as effective means of achieving these gains.
Pushing the Frontier
While some techniques merely shift the balance, others fundamentally increase the system's ceiling. Baseten categorizes kernel optimization, P/D disaggregation—the process of separating prefill and decode workers—and speculative decoding as methods that move the frontier forward. The guide specifically cites EAGLE-3, DSpark, and DFlash as examples of speculative decoding techniques that provide universal performance improvements.
Why Efficiency Matters
As large language models scale toward trillions of parameters, inference costs and latency have become the primary bottlenecks for production environments. By distinguishing between tradeoff management and frontier-moving gains, engineers can make more precise architectural decisions. For example, a company might deploy Tensor Parallelism for high-value, latency-sensitive users while utilizing high batch sizes for low-cost background processing pipelines.
The Path Forward
As the industry moves toward more complex model architectures, the ability to disaggregate workloads and optimize kernels will remain central to reducing the cost of intelligence. Engineers are encouraged to evaluate their specific workload needs—whether real-time interaction or bulk processing—before selecting a combination of these frontier-shifting and tradeoff-managing techniques.