{"id":10317,"date":"2026-05-22T18:30:00","date_gmt":"2026-05-22T16:30:00","guid":{"rendered":"http:\/\/stocks-future.com\/?guid=ed612e2e81505bb16d26b8072e18c04f"},"modified":"2026-05-22T18:30:00","modified_gmt":"2026-05-22T16:30:00","slug":"zflow-ais-simulation-guided-optimization-identifies-a-1-54x-higher-throughput-serving-configuration-for-deepseek-v4-pro-on-8xb300","status":"publish","type":"post","link":"https:\/\/stocks-future.com\/?p=10317","title":{"rendered":"ZFLOW AI’s Simulation-Guided Optimization Identifies a 1.54\u00d7 Higher-Throughput Serving Configuration for DeepSeek V4-Pro on 8\u00d7B300"},"content":{"rendered":"

\nWorking 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.<\/i><\/p><\/a><\/a>

SANTA CLARA, Calif.--(BUSINESS WIRE)--ZFLOW AI today announced a performance optimization milestone on PaleBlueDot AI's 8\u00d7NVIDIA 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\u2019s B300 production platform.<\/p>

\nZFLOW 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.<\/p>

\nZFLOW 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 \u2014 profiling real workload behavior and using hardware-aware simulation to guide deployment and tuning decisions for a specific workload on specific hardware.<\/p>

\nIn this milestone, ZFLOW AI evaluated DeepSeek V4-Pro serving with SGLang and EAGLE speculative decoding, analyzing serving-architecture tradeoffs, high-concurrency throughput and latency, and next-step multi-node deployment. Under higher-concurrency traffic, the prefill-decode disaggregated configuration reached peak throughput of 826 tokens\/second \u2014 approximately 1.54\u00d7 the non-disaggregated (monolithic) peak \u2014 with tail latency 2\u20133\u00d7 better. The monolithic path remained favorable for single-stream, low-concurrency, and long-context workloads, including full 1M-token context.<\/p>

\nZFLOW AI also observed that MTP\/EAGLE speculative decoding improved throughput with no measured quality regression in this test run: GSM8K accuracy across EAGLE 3\/1\/4, EAGLE 1\/1\/2, and no-MTP configurations stayed within approximately \u00b11 percentage point. Broader evaluation is ongoing.<\/p>

\nZFLOW AI's simulation further indicates that a two-node B300 configuration is a promising direction for production deployment, which the team plans to validate on hardware as a next step.<\/p>

\n\u201cModern inference optimization is moving beyond manual tuning of individual runtime knobs,\u201d said Dr. Zhibin Xiao, Founder and CEO of ZFLOW AI. \u201cThe next layer is a closed-loop workflow connecting real workload execution, hardware simulation, and optimization strategy. Our work on PaleBlueDot AI's B300 platform shows how ZFLOW AI helps infrastructure teams turn raw hardware capability into a workload-specific deployment strategy.\u201d<\/p>

\nFull closed-loop auto-optimization for DeepSeek V4-Pro on B300 remains under active development. ZFLOW AI plans to publish a Technical Insights blog detailing the serving-architecture tradeoffs, MTP\/EAGLE optimization, and multi-node deployment work.<\/p>

\nTeams evaluating DeepSeek V4-Pro or other frontier models on B300 or other next-generation GPU platforms can contact ZFLOW AI at contact@zflow.ai<\/a> to discuss optimization for their own workloads.<\/p>

\nAbout ZFLOW AI<\/b><\/p>

\nZFLOW AI is building a neutral optimization and control layer for AI infrastructure. Sitting above serving runtimes (vLLM, SGLang, TensorRT-LLM, Dynamo) and below the business decision, ZFLOW AI finds the lowest-cost, highest-performance way to run a given workload on a given cluster \u2014 across heterogeneous GPU, LPU, NPU, and CPU systems, without locking teams into any single vendor or stack. Learn more at zflow.ai<\/a>.<\/p>

\nAbout PaleBlueDot AI<\/b><\/p>

\nPaleBlueDot AI is a Silicon Valley-based AI compute platform with a growing global footprint, delivering high-performance AI compute through a unified platform for enterprise-scale deployment. Guided by its mission to make intelligence universally accessible, PaleBlueDot AI helps organizations build, deploy, and scale AI faster, better, and cheaper.<\/p> Contacts<\/b>

\nSandy Shen\nsandy.shen@zflow.ai<\/a><\/p>","protected":false},"excerpt":{"rendered":"

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.SANTA CLARA, Calif.–(BUSINESS WIRE)–ZFLOW AI today announced a…<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-10317","post","type-post","status-publish","format-standard","hentry","category-infos-businesswire"],"_links":{"self":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/10317","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10317"}],"version-history":[{"count":1,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/10317\/revisions"}],"predecessor-version":[{"id":10318,"href":"https:\/\/stocks-future.com\/index.php?rest_route=\/wp\/v2\/posts\/10317\/revisions\/10318"}],"wp:attachment":[{"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10317"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10317"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stocks-future.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10317"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}