# DGR-001 — performance contract baseline

## Files changed

- `packages/node/meshnet_node/performance_contract.py`
- `tests/test_performance_contract.py`
- `.scratch/distributed-gguf-runtime/issues/01-lock-the-safetensors-versus-gguf-performance-contract.md`
- `.scratch/distributed-gguf-runtime/evidence/DGR-001/performance-contract.json`

## What this slice does

- Locks the DGR-001 benchmark contract in code.
- Pins the architecture-aligned baseline to **DeepSeek-V2-Lite-Chat** (`deepseek2`).
- Uses the same model on both sides of the comparison:
  - **safetensors:** `deepseek-ai/DeepSeek-V2-Lite-Chat` in **BF16**
  - **GGUF:** `second-state/DeepSeek-V2-Lite-Chat-GGUF` in **Q2_K**
- Exposes a machine-readable JSON contract with:
  - benchmark lanes for `transformers` safetensors and `llama.cpp` GGUF on **CPU** and **GPU**
  - concurrency levels `1` and `4`
  - the required metrics list
  - an explicit stop condition for “no meaningful speed or fit benefit”
- Adds a deterministic stub benchmark report so the contract now has an executable report shape end to end.

## Recent benchmark runner slice

The runner currently uses a deterministic stub backend to exercise the comparison matrix without downloading a model. It emits:

- `.scratch/distributed-gguf-runtime/evidence/DGR-001/performance-contract.json`
- `.scratch/distributed-gguf-runtime/evidence/DGR-001/stub-benchmark-report.json`

The report includes per-device comparisons for:

- `transformers-safetensors-cpu` vs `llama-cpp-gguf-cpu`
- `transformers-safetensors-gpu` vs `llama-cpp-gguf-gpu`

and records the memory metric (`rss_bytes` on CPU, `vram_bytes` on GPU), decode speedup, artifact ratio, and output drift.

## Live endpoint CLI wiring

The contract CLI can now drive the live endpoint runner. Passing one `--live-endpoint LANE_ID=URL` mapping per contract lane (plus `--live-benchmark-out`) invokes `run_real_model_endpoint_benchmark` against already-running OpenAI-compatible servers and writes the report using the same schema as the stub:

```bash
PYTHONPATH=packages/node python -m meshnet_node.performance_contract \
  --live-endpoint transformers-safetensors-cpu=http://127.0.0.1:8001 \
  --live-endpoint llama-cpp-gguf-cpu=http://127.0.0.1:8002 \
  --live-endpoint transformers-safetensors-gpu=http://127.0.0.1:8003 \
  --live-endpoint llama-cpp-gguf-gpu=http://127.0.0.1:8004 \
  --live-benchmark-out .scratch/distributed-gguf-runtime/evidence/DGR-001/live-benchmark-report.json
```

`--live-model` overrides the model name sent in requests (defaults to the contract's safetensors repo). Without any `--live-endpoint` flags the CLI behaves exactly as before: it writes the contract JSON and, with `--benchmark-out`, the deterministic stub report.

## Exact commands and real results

### Targeted tests

```bash
PYTHONPATH=packages/node pytest -q tests/test_performance_contract.py tests/test_route_session_benchmark.py
```

Result: `19 passed in 0.11s`

### Contract artifact generation

```bash
PYTHONPATH=packages/node python -m meshnet_node.performance_contract --json-out .scratch/distributed-gguf-runtime/evidence/DGR-001/performance-contract.json
```

Result: wrote `.scratch/distributed-gguf-runtime/evidence/DGR-001/performance-contract.json`

### Python compile check

```bash
python -m compileall packages/node/meshnet_node/performance_contract.py tests/test_performance_contract.py
```

Result: passed

## Public relay smoke benchmark (2026-07-15)

A real streamed request was run through the public tracker — **not** by connecting directly to the private node address:

```text
https://meshnet.2.d-popov.com/v1/chat/completions
  -> wss://meshnet.2.d-popov.com/ws
  -> wss://meshnet.2.d-popov.com/rpc/7j77FsPY1evV8tuf-7000
  -> local CUDA node, Qwen/Qwen2.5-0.5B-Instruct layers 0-23
```

The local public-tracker node had an expired proof and a wedged HTTP server. A graceful restart refreshed its CUDA capability proof in `336 ms`, restored `admitted`/`routable` status, and reconnected its relay endpoint.

Measured streaming results after recovery:

| metric | result |
| --- | ---: |
| warm-up TTFT | 420.80 ms |
| warm-up elapsed | 610.23 ms |
| p50 TTFT (3 runs) | 288.26 ms |
| p50 elapsed (3 runs) | 363.20 ms |
| tracker-recorded relay throughput | 58.18-65.25 tok/s |
| HTTP status | 200 for all runs |

The tracker recorded `relay: true` and the local node ID `7j77FsPY-b32476219492` for each completion. Full redacted evidence is in `public-relay-smoke-benchmark.json`.

The other connected node is still alive but **not routable** because its capability proof is stale. It must revalidate before a multi-node shard/relay test can run.

## Limitations

- This slice still uses a deterministic stub backend for the core comparison matrix.
- It now also includes a live endpoint runner, reachable from the CLI via `--live-endpoint`/`--live-benchmark-out`, that fans out one OpenAI-compatible request per lane when the caller provides endpoints; the CLI does not start those servers.
- It does **not** download or run a real model from within the repo.
- Real safetensors vs GGUF execution, TTFT/prefill/decode measurements, RSS/VRAM capture, and output-drift comparison are still to be implemented against the contract.

## Compatibility notes

- The contract stays on the DeepSeek2 family to remain close to the DeepSeek-V4-Flash end goal.
- A smaller non-DeepSeek model can still be used later for loader-plumbing smoke tests, but it does not replace this baseline.
- Model artifacts must stay on the mounted drive and not under `/home`.

## Dependent-story handoff

Next implementation work should attach to this contract and add the live benchmark runner that actually compares:

1. current Transformers/safetensors recipe
2. whole-model llama.cpp GGUF recipe

using the same model architecture/revision and the same prompt/context/concurrency settings.