ZFLOW AI's Simulation-Guided Optimization Identifies a 1.54× Higher-Throughput Serving Configuration for DeepSeek V4-Pro on 8×B300
Working on PaleBlueDot AI's NVIDIA B300 platform, ZFLOW AI used hardware-aware simulation to find an optimized SGLang serving configuration for high-concurrency DeepSeek V4-Pro inference.
ZFLOW AI today announced a performance optimization milestone on PaleBlueDot AI's 8×NVIDIA B300 bare-metal platform, using simulation to identify an optimized DeepSeek V4-Pro serving configuration on an SGLang stack. To our knowledge, this is the first publicly documented simulation-guided serving optimization of a frontier open-source model on NVIDIA’s B300 production platform.
ZFLOW AI is building a neutral optimization and control layer for AI infrastructure. Sitting above serving runtimes and below the business decision, ZFLOW AI helps infrastructure teams find the lowest-cost, highest-performance way to run a given workload on a given cluster.
ZFLOW AI's role is complementary to the serving runtime. Building on the high-performance DeepSeek V4 foundation provided by the SGLang ecosystem, ZFLOW AI applies an optimization intelligence layer on top of the runtime — profiling real workload behavior and using hardware-aware simulation to guide deployment and tuning decisions for a specific workload on specific hardware.
