NVIDIA Introduces DynoSim for Optimizing LLM Serving Parameters

NVIDIA introduced DynoSim, a tool for automatic optimization of large language model serving system configurations. The solution helps engineers find an optimal combination of dozens of parameters through Pareto frontier simulation—a set of configurations where improving one metric inevitably leads to the degradation of another.

The Problem: Hundreds of Variables

Configuring LLM serving is not a single variable, but an entire system of interconnected parameters. Each choice affects others, and local optimization often shifts the bottleneck to another part of the system. For example, adding more workers for parallel processing can increase latency due to memory shortage. Choosing a different backend requires reconfiguring the scheduler.

Key parameters that must be considered simultaneously:

• Selection of model backend (vLLM, TensorRT, TensorRT-LLM, others)
• Tensor parallelism configuration (how to distribute computation across multiple GPUs)
• Balance between prefill (context preparation) and decode (response generation) phases
• Number of worker processes and threads on the host
• Scheduler strategy (batch size, dynamic batching)
• Traffic routing policy between nodes
• KV cache behavior and memory management
• Auto-scaling thresholds and horizontal scaling parameters

Previously, engineers found optimal configuration through trial and error. This meant weeks of testing on expensive GPU equipment, high costs, and inability to check all combinations.

The Solution: Pareto Frontier Simulation

DynoSim automatically simulates the parameter space and builds a performance map. Instead of testing on real hardware, the tool uses a physical model of hardware and software—predicting latency, throughput, and memory consumption.

As output, DynoSim produces a Pareto frontier—a set of non-dominated configurations. For example, one configuration may achieve 50ms latency at 1000 req/sec throughput, while another reaches 100ms at 2000 req/sec. Engineers select configurations based on priorities: if low latency is required, they choose the first option; if maximum throughput is needed, the second; if balance is desired, they look for an intermediate option.

The process typically takes hours of computation rather than weeks of experiments on real hardware. This accelerates the development cycle and allows engineers to test hundreds of parameter combinations.

What This Means

Tools like DynoSim translate LLM serving optimization from pure experimentation into a scientific discipline. Companies can now make informed configuration choices instead of blind trial-and-error. For large cloud services, even small efficiency improvements reduce costs by hundreds of millions of dollars per year, which is why tools like DynoSim are quickly becoming an industry standard.