ead09c45c7
Consumer Blackwell halves tensor-core throughput on FP32 accumulate (fp8 419 vs 838 TFLOPS with FP16 accumulate; bf16 209), so: - fp8 config locks use_fast_accum=True (Float8MMConfig). torchao already defaults it on; pinning it guards consumer cards against a default change. On B200 it is identical speed and slightly better quality (LPIPS 0.050 vs 0.091). - the Blackwell auto ladder prefers fp8 over mxfp8 (measured faster + more accurate). 2:4 semi-structured sparsity evaluated and rejected (scripts/sparse_accum_probe.py): 2:4 magnitude-prune + fp8 gives LPIPS 0.858 (broken image) with no fine-tune, the cuSPARSELt kernel errors on torch 2.9, and it does not compose with torch.compile (our main ~2x). Documented as a dead end, not shipped. |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||