The challenge of scaling Large Language Models (LLMs) in production is rarely a simple matter of compute; it is an intricate exercise in identifying bottlenecks across a distributed architecture. Raw profiling data—spans, traces, memory allocation logs—presents a high-noise, low-signal environment for infrastructure engineers attempting to optimize serving latency and throughput.
The objective for the ZFLOW AI Performance Dashboard was straightforward yet computationally and aesthetically demanding: synthesize millions of raw profiling events into a decision-useful interface that adheres to the precision and clarity expected in high-end technical tooling.
Distilling Complexity
Initial iterations of internal tools relied on fragmented Grafana dashboards and raw log dumps. Engineers were forced to context-switch constantly to correlate an anomalous spike in Time-To-First-Token (TTFT) with underlying hardware metrics or batch-processing inefficiencies.
By introducing a centralized telemetry plane utilizing a Next.js front-end mapped against an optimized SQL backend, the architecture was redesigned to prioritize declarative, query-driven views. The interface relies on aggressive whitespace and stark, monochromatic visual cues to ensure that critical deviations in performance immediately draw the operator's eye, devoid of arbitrary color-coding that often obfuscates true priority.
Hardware Utilization Matrix
A critical requirement was comparing divergent hardware profiles (e.g., A100 vs. H100 arrays) under identical workload simulations. The resulting table component eschews traditional heavy bordering for a typographic approach, relying on alignment and negative space to define the grid structure.
| Architecture | Throughput (tok/s) | P99 Latency (ms) | VRAM Util. |
|---|---|---|---|
| NVIDIA A100 (80GB) | 2,450 | 142.5 | 88% |
| NVIDIA H100 (80GB) | 4,120 | 86.2 | 74% |
| AWS Inferentia2 | Profiling... | Profiling... | - |