MANGO AI Software
Agent Serve Benchmark
Model your stack. Buy smarter.
Standardized harness · Reproducible runs · Vendor neutral · Measured + Modeled
Get a clear view of what your stack can do.
See what runs today. Model what you don't have yet. Prove it on one board.
Proof on real hardware.
We run real agents on real GPUs with the MangoBoost SGLang engine, so every result is measured on actual hardware and ranked, never estimated.
Model what you haven't built.
An auto tuning agent searches the config space, parallelism, batching, caching, before you touch a GPU.
Compare on the frontier.
Filter by benchmark, model, and precision, the winning config rises. Open and vendor neutral.
Token speed doesn't equal task performance.
Benchmarks often score the model alone. But agents operate in full loops of calls, tools, and memory, so performance lives at the system level.
What most tools measure.
Tokens per second, TTFT, TPOT, LLM latency. Real signal, blind to the agent around it.
What your service actually feels.
Concurrent users, task throughput, task latency. What your users and your bill live by.
From workload to winning config.
Tell us your domain and usage. We benchmark, model the cluster, and hand you a deploy ready config.
01. Input
Your workload.
Domain, models, and usage: concurrency, context, latency.
02. Measured
Benchmark it.
Every candidate on one harness, ranked the same way.
03. Modeled
Simulate it.
Model configs you haven't built yet in software, across the serving design space, before you size the cluster.
04. Consulting
Recommended config.
Mango engineers turn it into a deployable setup.
05. Dashboard
Track it live.
Compare on the public board, run again anytime.
01. Input
Your workload.
Domain, models, and usage: concurrency, context, latency.
02. Measured
Benchmark it.
Every candidate on one harness, ranked the same way.
03. Modeled
Simulate it.
Model configs you haven't built yet in software, across the serving design space, before you size the cluster.
04. Consulting
Recommended config.
Mango engineers turn it into a deployable setup.
05. Dashboard
Track it live.
Compare on the public board, run again anytime.
The whole agent stack, in four layers.
Benchmark for accuracy and efficiency. Tested end to end. Reproducible.
01Accuracy
Task accuracy.
How often the agent finishes the job, measured as resolve rate on credible benchmarks.
02Performance
Task performance.
Throughput per GPU and end to end latency, the efficiency that sets cost.
03Profiling
System profiling.
KV cache, device utilization, and hit rate, captured live.
04Reproducibility
Replayable runs.
Every run is saved step by step and can be replayed, so every result holds up.
Vendor neutral board. Our engine underneath.
Every config runs on MangoBoost SGLang, the -mb build.
The board stays neutral across models, agents, and hardware. The winning engine is ours.
Supported today: SWE-Bench · GLM-5 · FP8 · AMD MI300X · SGLang · OpenCode
Task Throughput per GPU vs. Task End-to-end LatencySWE-BENCH · MI300X
↑ Task throughput per GPU (Tasks/s/gpu)Task end-to-end latency (s) →
Mango AI Agent OS, Memory as a Service (expected)
MI300X, Mango AI Agent OS
MI300X, SGLang-MB-Opt
MI300X, SGLang (baseline)
Pareto Frontier. SWE-Bench, GLM-5 FP8, AMD MI300X, SGLang.
Latest from MangoBoost.
Benchmark milestones, engineering deep-dives and company announcements.
Related news and blog posts will appear here as they are published.
Optimize your agent serving cluster.
Bring your domain and workload. We benchmark, model, and optimize a config that performs.