# Qwen3-30B-A3B Community vLLM Harness Ablation, 2026-05-02

## Goal

Run a fresh dash0 experiment on the community vLLM latest release with the local community model:

`/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B`

The comparison is:

| Variant | Spec | Harness |
| --- | --- | --- |
| no-harness | `configs/examples/dash0_qwen30b_a3b_community_vllm020_noharness.json` | disabled via `llm.use_harness=false` |
| harness | `configs/examples/dash0_qwen30b_a3b_community_vllm020_harness.json` | enabled, including deterministic stop proposal |

Both specs start from the same base vLLM configuration. The base contains only serving access fields: `host`, `port`, and `served-model-name`. It does not set performance flags such as TP, DP, EP, max model length, prefix cache, chunked prefill, max-num-seqs, max-num-batched-tokens, or gpu-memory-utilization. The first trial therefore measures community vLLM defaults for this model.

## vLLM Install

PyPI reports `vllm==0.20.0` as the current community release checked on 2026-05-02. The dash0 runtime venv is on local rootfs rather than CPFS, because installing torch/CUDA wheels into CPFS was I/O-bound:

`/tmp/wjh/venvs/vllm-0.20.0-auto`

The first plain `pip install vllm==0.20.0` smoke pulled `torch 2.11.0+cu130` and failed on dash0's driver (`570.133.20`, CUDA 12.9). The active install uses the vLLM-documented `uv pip install vllm==0.20.0 --torch-backend=auto` path so uv selects a CUDA backend compatible with the installed driver.

Install log:

`/home/admin/cpfs/wjh/aituner/aituner/logs/install_vllm_0.20.0_20260502.log`

## Workload

The experiment reuses the 0-8k chat window that has already been used for qwen27b harness work:

| Field | Value |
| --- | --- |
| window | `chat_w20260311_1000` |
| source rows | 32606 |
| input filter | 0 to 8192 tokens |
| max requests per probe | 2048 |
| target pass rate | 0.95 |
| TTFT SLO | 2s up to 4k, 4s up to 32k, 6s above |
| TPOT SLO | 50ms |
| search high | 0.125 sampling_u |
| max probes per trial | 6 |

The `max_requests_per_probe=2048` cap keeps the fresh community-vLLM ablation practical while preserving a real trace-shaped replay, SLO scoring, and binary-search threshold probe.

## Harness Update Under Test

This run tests a stricter early-stop harness:

- The harness still injects L-C-A workload features, recent trial diagnostics, active bottleneck, legal topology candidates, tested signatures, and knob-family rules.
- A strong incumbent no longer means immediate stop. It means "validate nearby alternatives".
- Deterministic stop is allowed only after completed validation evidence says continuing is unlikely to be useful:
  - the incumbent beats baseline by a generic large-gain ratio,
  - at least two post-incumbent validation trials have run,
  - those validation trials did not produce a feasible per-GPU improvement,
  - the validation covered topology and runtime families, or accumulated at least three post-incumbent validation attempts.
- If the stop guard fires, `study tune` writes `harness-stop-XXXX` and exits without spending another GPU trial or asking the LLM for another proposal.

This is a generic harness rule, not a testcase-specific threshold. It does not depend on qwen27b, qwen235b, qwen30b, a fixed TP/DP value, or a hardcoded SLO number.

## Unit Tests

Local test command:

```bash
PYTHONPATH=src python3 -m unittest tests.test_core_flow -q
```

Result: passed, 74 tests.

The added coverage checks:

| Test | Purpose |
| --- | --- |
| `test_harness_does_not_stop_immediately_after_strong_incumbent` | strong incumbent requires validation first |
| `test_harness_stop_after_post_incumbent_validation_is_exhausted` | deterministic stop after validation exhaustion |
| `test_cli_tune_uses_harness_stop_before_llm` | `study tune` can stop without calling the LLM or launching another GPU trial |
| `test_prompt_can_disable_harness_for_ablation` | no-harness prompt removes structured harness context |

## Experiment Tracking

Pending dash0 runs:

| Variant | tmux session | Log | Study root |
| --- | --- | --- | --- |
| no-harness | `qwen30b_vllm020_noharness_20260502` | `logs/qwen30b_vllm020_noharness_20260502.log` | `.aituner-community-vllm020/studies/dash0-qwen30b-a3b-community-vllm020-chat-0-8k-noharness` |
| harness | `qwen30b_vllm020_harness_20260502` | `logs/qwen30b_vllm020_harness_20260502.log` | `.aituner-community-vllm020/studies/dash0-qwen30b-a3b-community-vllm020-chat-0-8k-harness` |

The harness run should be judged by best-so-far `request_rate_per_gpu` per tuning iteration, plus whether it stops only after validation evidence. The no-harness run should use the same trial budget so the ablation exposes whether the early-stop harness saves iterations without hiding a later better point.

## Results

Pending. This section will be filled after the dash0 experiments finish.