Open Model Lab
Open-Model Systems Bottlenecks
Which latency, throughput, batching, KV-cache, quantization, and profiling bottlenecks matter most for small experiments?
Status
- Status
- planned
Status: Planned. This page is a report scaffold. It does not contain model scores, charts, or completed run results.
Research question
Which latency, throughput, batching, KV-cache, quantization, and profiling bottlenecks matter most for small experiments?
Planned setup
- Profile representative training and inference paths.
- Compare practical bottlenecks before and after targeted changes.
- Keep model-quality claims separate from systems measurements.
Planned measurements
- Latency.
- Throughput.
- Batching behavior.
- KV-cache and quantization trade-offs.
- Cost/latency/quality trade-offs.
Planned sections
- Research question and claim boundary
- Setup, model variants, data versions, and config hashes
- Eval suite or task design
- Measurements and failure modes
- Limitations, caveats, and next decision
Expected artifacts
- systems_efficiency module.
- Profiling and optimization comparison table.
- Systems bottleneck report.
Claim boundary
This report focuses on practical experiment velocity, not production-scale serving.