8efcc17f47* Studio: account for DeepSeek-V4 compute buffer in context auto-fit DeepSeek-V4-Flash's lightning indexer plus compressed sparse attention reserve a large context-scaling compute buffer that _compute_buffer_ctx_bytes did not model (the KQ-mask and dequant-scratch rates both miss it, even with an f16 cache). Measured on UD-Q4_K_XL at ub 512 it is about 65.5 GiB at 1M context, which the mask estimate puts near 1.5 GiB, so the auto-fit kept the full 1M train context and llama-server OOM'd allocating the ~70 GB buffer, then spilled to CPU (~4 tok/s). Add a deepseek4-gated flat plus per-token term so the fit caps the context (about 256k on a B200) and the model stays fully on GPU. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||