GiGantic v2.4
Autonomous Multi-Agent BenchmarkRun 1 - Run 5 (Active)▼
GiGantic Pipeline & Benchmark Telemetry
Empirical evaluation of GiGantic autonomous multi-agent pipeline vs conventional engineering workflow
Best Solution Accuracy
Pending▼0 runs
Autonomous Latency
Pending▼0s recorded
NVIDIA GPU Utilization
Pending▼Standby
Accuracy Learning Curve
Progressive multi-agent refinement vs 80% baseline
Awaiting benchmark experiment trials to establish learning curve.(Launch a GiG to record live accuracy points)
Single-prompt control baseline: 80%GiGantic multi-agent refinement: +15 pts gain
Control vs GiGantic Verification
Pending
GiGantic AI
Pending
Control Baseline
Autonomous Delta
Pending RunsPipeline Insights
Awaiting completed benchmark trials to measure autonomous acceleration vs manual baseline.
Computation & Token Distribution
All Subsystems
Awaiting Execution Telemetry
Tokens0
Machining Swarm
35%NVIDIA NIM Inference
25%BDS Vector RAG
20%Safety & Compliance QA
12%CAD Topology & 3D
8%Model execution layerNVIDIA NIM Active
Swarm Agent Telemetry Heatmap
Per-agent token intensity across benchmark runs
<300 tok
>400 tok
>500 tok
No Active Swarm Telemetry (Pending)
ToolingMachiningCost QASafetyNIM GPUVector RAGReview
R1R2R3R4R5R6R7
Dynamic agent delegation7 runs logged
Solution Precision Score
End-to-end CAD fabrication tolerance pass rate
Tolerance threshold: ±0.05 mmPending