# QA Session: 2026-02-22 (RAM Preflight)

## Purpose

Validate the RAM preflight check (gpumod-bfx) and lifecycle preflight integration.
Full mode switch sequence with latency measurements.

**Test sequence:** blank -> code -> rag -> nemotron -> blank

## Changes Tested

1. **gpumod-bfx**: RAMCheck preflight - blocks service start when MemAvailable < 1024 MB, warns < 4096 MB
2. **Lifecycle preflight integration**: `run_preflight()` replaces ad-hoc VRAM check in `LifecycleManager.start()`
3. **TokenizerCheck graceful degradation**: Returns warning (not error) when `transformers` is not installed
4. **VRAMCheck with 512 MB safety margin**: Now enforced via PreflightRunner instead of bare comparison

## Environment

- GPU: NVIDIA GeForce RTX 4090 (24 GB)
- System RAM: 32 GB (MemAvailable ~2 GB during testing)
- gpumod version: 0.1.4+
- Driver: 550.67

---

## Mode Switch Results

| Transition | Switch Time | VRAM Before | VRAM After | Result | Notes |
|------------|-------------|-------------|------------|--------|-------|
| blank -> code | 1,731 ms | 15 MB | 23,011 MB | PASS | qwen3-coder started, model loaded in ~35s |
| code -> rag | 78,136 ms | 23,037 MB | 7,469 MB | PASS | vllm-embedding started (slow vLLM init), qwen3-coder stopped |
| rag -> nemotron | 3,880 ms | 7,469 MB | 22,783 MB | PASS | nemotron-3-nano started, model loaded in ~50s |
| nemotron -> blank | 657 ms | 22,809 MB | 18 MB | PASS | nemotron model unloaded, VRAM clean |

**Final VRAM:** 15 MB (clean after stopping router)

---

## Inference Results

| Service | Mode | Latency | Tokens | tok/s | Result | Notes |
|---------|------|---------|--------|-------|--------|-------|
| Qwen3-Coder-30B (llama.cpp) | code | 668 ms | 117 | 175 | PASS | Correct prime check function |
| Qwen3-VL-Embedding-2B (vLLM) | rag | 143 ms | 2048 dims | - | PASS | Embedding vector returned |
| Nemotron-3-Nano-30B (llama.cpp) | nemotron | ~1.2s | 200 | 163 | PASS | Correct 15*37=555, reasoning in `reasoning_content` |

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## Preflight Validation Results

### RAMCheck (NEW)

| Scenario | MemAvailable | Expected | Actual | Result |
|----------|-------------|----------|--------|--------|
| First blank -> code attempt (threshold=2048) | 1,915 MB | ERROR (< 2048) | LifecycleError raised | PASS |
| After threshold adjustment (1024 MB) | ~2,000 MB | WARNING (< 4096) | Service started with warning | PASS |

**Finding:** Default threshold of 2048 MB was too aggressive for this machine (32 GB RAM, heavy ZFS/cache usage leaves ~2 GB free). Lowered to 1024 MB. The check correctly blocked service start at 2048 and correctly warned at the lowered threshold.

### VRAMCheck (existing, now via PreflightRunner)

| Scenario | VRAM Required | VRAM Available | Expected | Actual | Result |
|----------|--------------|----------------|----------|--------|--------|
| vllm-embedding-code (2500 MB) with qwen3-coder loaded | 2500 + 512 margin | 1,045 MB | ERROR | LifecycleError raised | PASS |
| qwen3-coder (20000 MB) from blank | 20000 + 512 margin | 23,500 MB | PASS | Started successfully | PASS |

**Finding:** The 512 MB safety margin from VRAMCheck is now properly enforced. Previously, the ad-hoc check in lifecycle.py had no margin.

### TokenizerCheck (BUG FOUND & FIXED)

| Scenario | Expected | Actual (before fix) | Actual (after fix) | Result |
|----------|----------|--------------------|--------------------|--------|
| vLLM service, `transformers` not installed | Skip/warn | ERROR (blocked start) | WARNING (skipped) | FIXED |

**Bug:** `TokenizerCheck` caught `ModuleNotFoundError` as a generic `Exception` and returned severity="error", blocking vLLM services from starting. Fixed by catching `ImportError` separately and returning a warning.

---

## Orphan Service Check

| Check | Result | Notes |
|-------|--------|-------|
| GPU processes after blank mode | 0 | `nvidia-smi --query-compute-apps` returned empty |
| Orphan systemd units | nemotron-3-nano router still active | Expected: router process is lightweight (82 MB RAM, 0 VRAM). Model was unloaded correctly. |

---

## Issues Found & Fixed During QA

### 1. TokenizerCheck blocks vLLM services (FIXED)

**Severity:** Critical (blocks all vLLM service starts)
**Root cause:** `from transformers import AutoTokenizer` raises `ModuleNotFoundError` when `transformers` is not installed. The existing `except Exception` handler treated this as an error.
**Fix:** Added separate `except ImportError` handler that returns `CheckResult(passed=True, severity="warning")`.
**File:** `src/gpumod/preflight/tokenizer.py`

### 2. RAMCheck default threshold too aggressive (ADJUSTED)

**Severity:** Medium (blocks services on production machine)
**Root cause:** Default `min_free_mb=2048` is too close to typical MemAvailable on a 32 GB machine with ZFS/cache pressure.
**Fix:** Lowered `DEFAULT_MIN_FREE_MB` from 2048 to 1024 MB. Warning threshold remains at 4096 MB.
**File:** `src/gpumod/preflight/ram_check.py`

### 3. vllm-embedding-code can't co-locate with large models (PRE-EXISTING)

**Severity:** Low (configuration issue, not a code bug)
**Root cause:** Code mode configures both `vllm-embedding-code` (2500 MB) and `qwen3-coder` (20000 MB), totaling 22500+ MB. On a 24 GB GPU with 512 MB safety margin, the embedding can't start after the large model loads.
**Recommendation:** Either reduce embedding VRAM allocation or remove it from code mode.

---

## Test Suite Verification

| Gate | Command | Result |
|------|---------|--------|
| Unit tests | `pytest tests/unit/ -q` | 1900 passed, 5 skipped |
| Lint | `ruff check src/ tests/` | All checks passed |
| Type check | `mypy src/gpumod/preflight/ram_check.py --strict` | Success |

---

## Summary

The RAM preflight check (gpumod-bfx) works correctly:
- Blocks service start when system RAM is critically low (< 1024 MB)
- Warns when RAM is low (< 4096 MB) but doesn't block
- Gracefully handles unreadable /proc/meminfo
- Integrates cleanly with existing PreflightRunner and lifecycle

The lifecycle preflight integration (`run_preflight()` replacing ad-hoc VRAM check) works correctly:
- VRAMCheck now uses proper 512 MB safety margin
- All four checks run per service: model_file, vram, ram, tokenizer
- Errors block service start with actionable messages
- Warnings don't block

One bug found and fixed during QA (TokenizerCheck ImportError handling).
