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Qwen3-TTS benchmarks on RTX PRO 6000 Blackwell

Two studies live in this repo — note the model size for every table:

study	model	date	report	raw data
TRT vs native vocoder A/B (FastAPI + Silero path)	1.7B `Qwen3-TTS-RU-CallCenter-stressed`	2026-07-14	this README (below) + `VOCODER_RUNTIME_BENCH.md`	`metrics/native-eager-fastapi-silero-c10-c60/`, `metrics/trt-fastapi-silero-c10-c60/`
Load test + 1.7B comparison + TRT-vs-eager A/B (direct vLLM path)	0.6B `Qwen/Qwen3-TTS-12Hz-0.6B-Base`	2026-07-16	`QWEN06B_BENCH.md`	`metrics/qwen06b-trt-c1-c50/`, `metrics/qwen06b-trt-high-c30-c64/`, `metrics/qwen06b-eager-c1-c50/`

[1.7B] TensorRT Vocoder vs Native PyTorch Vocoder with FastAPI and Silero Stress

Exact A/B measurement date: 2026-07-14. Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition. Model: Qwen3-TTS-RU-CallCenter-stressed, Base voice clone with 10_ekaterina.wav, PCM16 mono at 24 kHz.

Result

The TensorRT vocoder sustained every tested level from 10 through 60 concurrent sessions within SLO. The native PyTorch/eager vocoder passed through concurrency 30 and failed at 40, 50, and 60.

Both runs used the complete public service path, including Russian text normalization and automatic stress placement:

DVC replay -> FastAPI :8000 -> normalization -> Silero Stress -> vLLM -> vocoder

The only intentional inference-runtime variable was:

TRT run: OMNI_TRT_VOCODER=1, code2wav decoded by TensorRT in Triton.
Native run: OMNI_TRT_VOCODER=0, code2wav decoded by the original in-process PyTorch/eager implementation.

Exact workload controls

Both runs used:

the same 100 private-HF call scenarios and seed 42;
the same 10_ekaterina.wav and transcript reference;
the same qwen3_tts_bigmem.yaml deployment configuration;
the same sentence chunker and LLM simulator;
two in-flight TTS chunks per session;
real recorded user-pause pace (user_time_scale: 1.0);
90-second sustained windows at concurrency 10, 20, 30, 40, 50, and 60;
the same SLO thresholds and 200 ms audible-gap boundary.

TRT-path verification: the Triton code2wav inference counter increased during the TRT run. Native-path verification: it remained exactly 30674 -> 30674 through the complete native run. After the experiment, the TRT service was restored and the counter increased again on a control request.

Gap definitions

Micro-gap: a playback-buffer underrun shorter than 200 ms. It is measured, written into the WAV timeline, and logged, but does not count as an audible SLO stutter.
Audible gap: a playback-buffer underrun of at least 200 ms.
Total gaps: micro-gaps plus audible gaps.
Gap frequency: gap events per 100 completed turns. Turns can contain more than one gap, so this is an event rate rather than a percentage of turns.
Audible-gap turns: percentage of turns containing at least one gap of 200 ms or longer.
Initial silence before the first PCM bytes is represented by TTFA/E2E and is not counted as a mid-phrase gap.

Primary comparison: gap scaling

Concurrency	TRT turns	TRT total gaps	TRT gaps / 100 turns	Native turns	Native total gaps	Native gaps / 100 turns	Native audible gaps	Native audible-gap turns
10	60	0	0.00	61	0	0.00	0	0.00%
20	119	0	0.00	119	2	1.68	0	0.00%
30	185	0	0.00	178	11	6.18	6	3.37%
40	246	2	0.81	246	36	14.63	14	5.69%
50	310	7	2.26	305	72	23.61	28	8.52%
60	374	18	4.81	358	157	43.85	54	12.57%

At concurrency 60, the native vocoder produced one playback gap per 2.28 turns on average. The TRT vocoder produced one short micro-gap per 20.78 turns and no audible gaps.

Gap duration detail

Concurrency	TRT micro-gaps	TRT micro p95, ms	TRT max gap, ms	Native micro-gaps	Native micro p95, ms	Native audible gaps	Native audible p95, ms	Native max gap, ms
10	0	-	-	0	-	0	-	-
20	0	-	-	2	195.6	0	-	195.6
30	0	-	-	5	188.9	6	360.2	360.2
40	2	33.8	33.8	22	166.0	14	910.8	911.6
50	7	53.8	53.8	44	193.2	28	1,217.8	1,846.1
60	18	71.0	110.3	103	172.4	54	1,235.6	1,242.0

The TRT run's worst gap across all levels was 110.3 ms. The native run's worst gap was 1,846.1 ms at concurrency 50.

Latency and throughput

Concurrency	TRT TTFA p95, ms	Native TTFA p95, ms	TRT E2E p95, ms	Native E2E p95, ms	TRT audio x	Native audio x	TRT SLO	Native SLO
10	173.5	202.5	1,120.7	1,126.1	7.64	7.79	PASS	PASS
20	259.9	440.8	1,210.3	1,280.7	16.87	16.21	PASS	PASS
30	278.6	489.6	1,179.3	1,242.6	23.51	23.82	PASS	PASS
40	504.6	1,151.0	1,243.8	2,004.0	31.13	31.01	PASS	FAIL
50	522.4	1,720.7	1,258.9	2,315.6	39.05	39.02	PASS	FAIL
60	578.2	2,816.9	1,319.6	3,658.6	47.40	45.16	PASS	FAIL

Throughput remains similar through concurrency 50, but the native vocoder's queueing latency and playback discontinuities grow sharply. At concurrency 60, native TTFA p95 is 4.87 times the TRT value.

Detailed run tables

TensorRT vocoder

Concurrency	Turns	Micro-gaps	Gap events / 100 turns	TTFA p50/p95/p99, ms	E2E p95, ms	SLO
10	60	0	0.00	130.2 / 173.5 / 194.9	1,120.7	PASS
20	119	0	0.00	148.0 / 259.9 / 299.0	1,210.3	PASS
30	185	0	0.00	153.9 / 278.6 / 331.1	1,179.3	PASS
40	246	2	0.81	167.1 / 504.6 / 642.4	1,243.8	PASS
50	310	7	2.26	176.1 / 522.4 / 694.0	1,258.9	PASS
60	374	18	4.81	197.6 / 578.2 / 845.8	1,319.6	PASS

Measured TRT capacity lower bound: at least 60 concurrent sessions. A level above 60 was not included in this exact A/B run.

Native PyTorch/eager vocoder

Concurrency	Turns	Micro-gaps	Audible gaps	Gap events / 100 turns	TTFA p50/p95/p99, ms	E2E p95, ms	SLO
10	61	0	0	0.00	143.9 / 202.5 / 253.9	1,126.1	PASS
20	119	2	0	1.68	181.3 / 440.8 / 537.0	1,280.7	PASS
30	178	5	6	6.18	202.6 / 489.6 / 706.6	1,242.6	PASS
40	246	22	14	14.63	235.7 / 1,151.0 / 3,334.2	2,004.0	FAIL
50	305	44	28	23.61	322.3 / 1,720.7 / 3,542.4	2,315.6	FAIL
60	358	103	54	43.85	460.9 / 2,816.9 / 5,477.3	3,658.6	FAIL

Measured native capacity under the configured SLO: 30 concurrent sessions.

Conclusions

Silero Stress is compatible with both vocoder implementations: neither run produced HTTP or TTS errors, and both returned valid streaming PCM.
The original native vocoder remains acceptable through concurrency 30, but playback gaps begin at 20 and audible gaps begin at 30.
Native latency crosses the SLO at concurrency 40 and degrades rapidly at 50/60 despite similar aggregate audio throughput.
TensorRT eliminates all audible gaps in the tested 10-to-60 range and keeps TTFA/E2E p95 within SLO at every level.
The measured SLO capacity improvement is 30 -> at least 60 concurrent sessions.

Data provenance

TRT aggregate artifacts:

experiments/fastapi-stress-c10-c60-90s-20260714/metrics.json
experiments/fastapi-stress-c10-c60-90s-20260714/env.json
experiments/fastapi-stress-c10-c60-90s-20260714/params_used.yaml
experiments/fastapi-stress-c10-c60-90s-20260714/gpu_dmon.log

Native aggregate artifacts:

experiments/fastapi-stress-native-eager-c10-c60-90s-20260714/metrics.json
experiments/fastapi-stress-native-eager-c10-c60-90s-20260714/env.json
experiments/fastapi-stress-native-eager-c10-c60-90s-20260714/params_used.yaml
experiments/fastapi-stress-native-eager-c10-c60-90s-20260714/gpu_dmon.log

Private-corpus artifacts (events.jsonl, turns.jsonl, WAV recordings, and the HTML report containing turn text) are intentionally kept local and are not published to the public Hugging Face repository.

Scenario SHA-256: 627261a0ada620c562449c53ffc6ee546eb29720d68ba3327c95a22d506b77cb
Reference WAV SHA-256: d913ffab4a16c5a017a36ad2d00a8352fcdb190396d64f37d5a5d91466dec673

SLO thresholds: TTS TTFA p95 <= 800 ms, E2E first-audio p95 <= 2,000 ms, audible underruns <= 0.2 per turn, and errors <= 1%.

[0.6B] Update 2026-07-16: Qwen3-TTS-12Hz-0.6B-Base study

Everything in this section was measured on the 0.6B model (Qwen/Qwen3-TTS-12Hz-0.6B-Base), not the 1.7B production finetune.

Full report: QWEN06B_BENCH.md. Highlights (same GPU, same rig, shipped qwen3_tts_gb10.yaml config):

0.6B swap is weight-only (identical codec dims, engines auto-rebuild).
Capacity within SLO: >= 50 concurrent calls (same as 1.7B) at 25-40 % lower TTFA: p95 at c50 350 ms vs 594 ms (1.7B).
TRT-vs-eager A/B on 0.6B: SLO capacity 50 vs 30; at c50 eager TTFA p95 2344 ms vs 350 ms TRT (x6.7) - the TRT vocoder wins regardless of talker size (the vocoder is the same in both checkpoints).
Raw data: metrics/qwen06b-trt-c1-c50/, metrics/qwen06b-trt-high-c30-c64/, metrics/qwen06b-eager-c1-c50/.

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