# Spike 002a-mini-gpu-smoke — VERDICT

**Status**: ✅ PASSED on local 5090
**Date**: 2026-05-26
**Wave**: 12 (closing the "zero GPU evidence" gap from cross-model review item #4)

## Headline

`composer_replication` 3-channel loss composition runs cleanly on real GPU
hardware. Qwen2.5-0.5B-Instruct on RTX 5090 sm_120 in bf16, 50 backward steps,
loss 0.7354 → 0.00034 (99.95% reduction), all gradients finite throughout.
Peak VRAM 5.31 GB (well under the ADR-001 8GB target). Median step time 480ms.

## Closes

- The cross-model review's item #4: "Run Spike 002a-mini on the local 5090.
  ADR-001 made the choice; the spike was not run. Until then, the framework
  has zero GPU evidence of any kind." **Done.**
- ADR-001's underlying claim that local 5090 is the right venue for this
  workload class. Verified: 50-step run completes in ~30 s wall-clock on
  the local 5090, vs an estimated 3-5 min cold-start cycle on Modal L4.
- The "but the framework only runs on CPU" objection in V8.

## Acceptance criteria

| Criterion | Target | Result |
|---|---|---|
| Model loads via `AutoModelForCausalLM` on `cuda` | bf16, no errors | ✅ 7.3 s |
| 50 steps run end-to-end | No nan/inf | ✅ |
| Loss decreases meaningfully | final < 50% × initial | ✅ final = 0.046% × initial |
| Peak VRAM < 8 GB on 0.5B model | headroom check | ✅ 5.31 GB |
| Step time stable | no thermal throttling, no swap | ✅ median 480ms, no outliers |
| All gradients finite throughout | per-step finite check | ✅ |
| sm_120 Blackwell architecture supported | not pre-Hopper-only | ✅ verified arch in `torch.cuda.get_arch_list()` |

## Per-channel behavior on GPU

Same as CPU (Spike 006): LM-CE channel dominates, DPO channel contributes
small nonzero gradient throughout, SDPO channel zero (shape-mismatch
fallback — to exercise the SDPO channel on GPU, run with `align_sdpo_shapes`
batch builder per Spike 006-strict's `test_sdpo_channel_actually_fires`).

## Memory profile

| step | total | peak_mem_gb | step_time_ms |
|------|-------|-------------|--------------|
| 0 (post-warmup) | 0.7354 | 5.31 | ~500 |
| 10 | 0.0067 | 5.31 | ~480 |
| 25 | 0.0007 | 5.31 | ~480 |
| 49 | 0.0003 | 5.31 | ~480 |

Memory stays flat at 5.31 GB after warmup — no leak, no expanding
activation buffers. (The 0.5B model in bf16 + Adam states + activations +
DPO logit gradients all fit comfortably.)

## What this does NOT close

- **Multi-replica / multi-process DiLoCo** (V2 partial gap). This spike
  is single-GPU. Real DiLoCo training across replicas is GPU-multi-process
  and not yet attempted.
- **Composer-2.5-quality empirical results** (V8 partial gap). This spike
  verifies the framework runs on GPU; it does NOT verify the method
  improves model quality vs plain GRPO. That requires the full pipeline
  (real teacher rollouts + real GRPO rewards + a benchmark like
  SWE-bench-lite) and is the post-replication GPU phase ($30-100+).

## Files

- `run_gpu_smoke.py` — 50-step GPU smoke runner with VRAM + step-time capture
- `results/gpu_loss_curve.csv` — per-step metrics
- `results/gpu_verdict.json` — programmatic verdict for CI/audit
- `results/run.log` — actual successful run output

## Cost / time

- $0 (local 5090, no Modal spend)
- 35 s wall-clock total (7 s model load + 25 s training)
- ~5 GB VRAM