docs: add Tesla T4/Turing hardware notes (attn-impl, dtype, quantization)

[moduvoice]

Motivation

Ran the GPA-v1.5 native infer Quick Start (GPA_1.5/docs/infer.md) verbatim on a real Tesla T4 16GB (Turing, sm_75; driver 550.163.01 / CUDA 12.4, torch 2.6.0+cu124, transformers 5.13.0) and hit a crash the docs don't mention, plus found a couple of undocumented behaviors worth calling out for anyone on Turing-class GPUs (T4, similarly L4).

Changes

Doc-only additions to GPA_1.5/docs/infer.md — no code changes:

1. --attn-impl auto crashes on Turing GPUs. inference/model_loader.py::normalize_attn_impl returns flash_attention_2 for any cuda* device with no torch.cuda.get_device_capability() check and no check that flash-attn is even installed (it's commented out as an optional extra in pyproject.toml, not in requirements.txt). Following the Quick Start as-is on T4 fails at model load with:

   ImportError: FlashAttention2 has been toggled on, but it cannot be used due to the following error:
   the package for FlashAttention2 doesn't seem to be installed.
   

   Documented the fix: pass --attn-impl sdpa (already a supported CLI value, no code change) — both ASR and TTS complete normally. Also corrected the "Fast Troubleshooting Data preparation files are missing #5" section, which previously only suggested falling back to --device cpu; now it points to --attn-impl sdpa first so users don't need to give up the GPU.

2. No --dtype CLI flag. --device and --attn-impl are exposed as CLI args, but dtype is hardcoded to torch.bfloat16 on CUDA in model_loader.py. Documented that this is measurably suboptimal on T4: PyTorch's SDPA EFFICIENT_ATTENTION kernel rejects bfloat16 and silently falls back to the slower MATH path, while fp16 reaches EFFICIENT_ATTENTION directly (per-token latency was within noise in our runs, but only fp16 hits the faster kernel on this GPU class).

3. bitsandbytes int8 quantization trade-off. Not mentioned anywhere in the docs today. Measured BitsAndBytesConfig(load_in_8bit=True) reducing peak VRAM by ~43% (2317 MB → 1327 MB, reproduced twice) but making batch=1 autoregressive decode ~3.2x slower (≈36–39 ms/token → ≈121–125 ms/token, reproduced twice). Documented as a real trade-off rather than a free win.

4. VRAM headroom note. The native infer path uses a 0.6B backbone; full-pipeline peak VRAM measured ≈4.0–4.2 GB (bf16/fp16) / ≈3.0–3.1 GB (int8) — roughly 74–81% of a T4's 16GB unused. Noted this so T4/L4 users know OOM isn't a practical concern here.

Testing

• Ran GPA_1.5/infer.py --task asr and --task tts end-to-end on a Tesla T4 16GB with --attn-impl sdpa: both complete successfully (ASR transcript correct, TTS produces a valid wav).
• Reproduced the flash_attention_2 ImportError with the default --attn-impl auto on the same box (2 runs, deterministic).
• Measured peak VRAM and per-token decode time for bf16/fp16/int8 via the repo's own AutoModelForCausalLM/BitsAndBytesConfig APIs (no repo code modified), each reproduced twice.
• This PR only touches GPA_1.5/docs/infer.md; no source files changed.