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# Qwen3-TTS 0.6B Base with a pinned AR-engine memory budget.
#
# The upstream pipeline (Qwen3TTSPipelineConfig) 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 (~1.5 GB)
# 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 weights themselves are tiny: 0.6B bf16).
#
# 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; zonos2_bf16.yaml
# and higgs_audio_v3_tts.yaml fix the same 24 GB OOM class their way.
config_cls: Qwen3TTSPipelineConfig
model_path: Qwen/Qwen3-TTS-12Hz-0.6B-Base
stages:
tts_engine:
engine:
mem_fraction_static: 0.70
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