feat: gplang-omni reserves 70% of gpu 0 memory, preventing oom errors on consumer cards

1 files changed, 22 insertions, 0 deletions

diff --git a/app/backends/sglomni/configs/moss_tts.yaml b/app/backends/sglomni/configs/moss_tts.yaml
index ef8d8fa..41bddd5 100644
--- a/app/backends/sglomni/configs/moss_tts.yaml
+++ b/app/backends/sglomni/configs/moss_tts.yaml
@@ -1,2 +1,24 @@
+# MOSS-TTS v1.5 with a pinned AR-engine memory budget.
+#
+# The upstream pipeline (MossTTSPipelineConfig) colocates its three stages
+# (preprocessing -> tts_engine -> vocoder) in one process on GPU 0 and leaves
+# the engine's sglang mem_fraction_static unset: the static pool (weights +
+# KV cache) is auto-sized to nearly all free VRAM at boot. On a 24 GB card
+# that leaves only tens of MiB free once the engine's CUDA graphs and the
+# colocated vocoder are resident — the first /v1/audio/speech request aborts
+# with "CUDA out of memory. Tried to allocate ~100 MiB" and every retry fails
+# identically (the same failure the qwen3_tts/higgs/zonos2 vendored configs
+# fix; note moss_tts_local does NOT need this — MossTTSLocalPipelineConfig
+# already budgets its colocated stages explicitly: 0.15/0.67/0.18).
+#
+# 0.70 pins the static pool at ~70% of the card (~16.5 GB on a 24 GB GPU —
+# a KV pool far larger than any narration request needs) and leaves ~7 GB
+# for the vocoder, CUDA graphs, transient allocations, and other GPU
+# processes. Precedent: dots_tts.yaml pins this same knob.
 config_cls: MossTTSPipelineConfig
 model_path: OpenMOSS-Team/MOSS-TTS-v1.5
+
+stages:
+  tts_engine:
+    engine:
+      mem_fraction_static: 0.70