There is no proven “near-GPU” latency number for CPU-only TTS. A voice agent can avoid a local GPU by sending synthesis to a cloud service, or by running a streaming TTS model on the host CPU. Those are different architectures, and neither can be judged by TTS speed alone: measure the interval from the end of the caller’s speech to the first playable reply audio, then test it under the concurrency and network conditions you expect in production.
What “near-GPU” means for a voice agent
“Near-GPU” is not a standardized benchmark category. Here it means responsive enough for a production voice interaction without depending on a GPU installed on the user’s or application’s local machine. That does not necessarily mean the system uses no GPUs: remote services may run speech recognition, the language model, or TTS on GPU-backed infrastructure.
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Cloud-hosted inference
The client has no local GPU, while some or all model inference happens remotely. The perceived delay includes network travel, service queueing, model work, audio transport, and playback—not just the TTS engine’s startup time.
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Local CPU inference
TTS runs on the host CPU, either alongside a remote or local language model. Piper’s usage documentation shows how to download an ONNX voice model and run it locally; its streaming example sends raw audio to standard output as it is generated. That establishes a workable implementation path, not a latency guarantee or a capacity claim for any particular CPU. Piper usage documentation | agent-cli TTS server documentation
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Local GPU inference
A local GPU can be a useful comparison point, but only when the model, hardware, workload, concurrency, and measurement method match. A vendor result on a particular GPU is not evidence of equivalent CPU performance.
Measure the pause the user hears—not just TTS startup
For a voice agent, define end-to-end response latency as the time from the end of the user’s speech to the first playable synthesized audio. NVIDIA recommends targeting less than one second for a conversational agent; treat that as NVIDIA guidance, not a universal human-factors standard. NVIDIA’s end-to-end latency guidance
Break that interval into stages so a slow reply has an identifiable cause:
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- ASR finalization: recognition may continue after the user stops speaking. NVIDIA’s example assigns about 80–160 ms from utterance end to final transcript with its 80 ms ASR chunk setting. This is a configuration-specific example, not a universal ASR delay.
- LLM time to first token (TTFT): the time until the model starts producing its response. NVIDIA’s FAQ gives typically 400–600 ms for its stated Nano 30B configuration; do not generalize that figure to other models or deployments.
- TTS time to first byte or chunk (TTFB): the wait from a synthesis request until the first audio data is available. Check that the metric actually represents audio that can be played, not merely a response or data chunk received by the server.
- Inter-chunk delay: the gap between successive audio chunks. A quick first chunk can still be followed by pauses that make speech sound broken.
- Transport and playback: media-session setup, routing, buffering, and the client’s ability to begin playback all affect what the caller experiences.
RTFX, used in NVIDIA Riva’s performance documentation, is generated audio duration divided by computation time. It describes synthesis throughput relative to audio duration; it is not a substitute for measuring when a caller first hears a response. NVIDIA Riva TTS performance methodology
What published GPU figures can—and cannot—tell you
NVIDIA’s Magpie FAQ reports 78 ms TTS TTFB for Magpie TTS Multilingual 357M on an A100 at one stream. That is a vendor figure for one model, one GPU, and single-stream load—not a CPU result or an end-to-end voice-agent measurement. NVIDIA Magpie TTFB FAQ, updated July 10, 2026
NVIDIA’s Nemotron Voice Agent reference table reports 0.93 seconds of end-to-end latency at both 1 and 64 streams on a dedicated four-B200 setup. In that table, TTS TTFB is 0.08 seconds at one stream and 0.10 seconds at 64 streams. NVIDIA notes that results can vary with CPU/GPU configuration and load balancing. These measurements describe the listed reference system; they do not establish how a CPU-only or cloud-only deployment will perform. NVIDIA Nemotron Voice Agent evaluation and performance
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| Published measurement | Reported result | Scope |
|---|---|---|
| Magpie TTS TTFB | 78 ms | Magpie TTS Multilingual 357M on an A100, one stream; NVIDIA FAQ updated July 10, 2026. |
| Nemotron Voice Agent end-to-end latency | 0.93 s at 1 stream; 0.93 s at 64 streams | Dedicated four-B200 setup in NVIDIA’s reference table. |
| Nemotron Voice Agent TTS TTFB | 0.08 s at 1 stream; 0.10 s at 64 streams | Same reference table and setup as the end-to-end figures above. |
These figures are useful for understanding what a specified GPU setup can report, not for predicting a different stack’s production p95. NVIDIA’s TTS NIM methodology uses 20 iterations over 10 LJSpeech strings per stream, waits for all chunks of a request before sending the next request on that stream, and averages three trials. Riva also documents controlled strings, iterations, and three-trial averages. Such methods make vendor tests more interpretable, but they do not reproduce every language, text length, network path, or traffic pattern. NVIDIA TTS NIM performance methodology | NVIDIA Riva TTS performance methodology
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The available figures do not provide a controlled, apples-to-apples CPU-versus-GPU production latency comparison. Do not use a GPU TTFB, RTFX result, or vendor reference pipeline to promise CPU performance.
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How to evaluate CPU-only streaming TTS
Documentation establishes that local CPU streaming is implementable, but it does not state which processor, voice, or load will meet your latency target. Benchmark the machine and integration you intend to deploy rather than inferring performance from a model’s ability to run locally.
- Pin the test configuration. Record the voice model, runtime, CPU, thread settings, audio format, text normalization, and language. Keep them fixed when comparing runs.
- Separate cold start from steady state. Measure startup separately, then warm the service as it would be warmed in production. Report which measurements include model loading.
- Instrument the complete turn. Timestamp end of user speech, final transcript, LLM first token, TTS request, first playable audio, subsequent chunks, and playback start. Record end-to-end p50 and p95, TTS first-chunk time, inter-chunk delay, total synthesis duration, failures, and queue time.
- Use realistic replies and load. Test representative text lengths and languages at the expected concurrency. Include CPU contention from the agent, telephony or media stack, and other processes. A single-stream result does not establish tail latency under concurrent work.
- Test interruption and cancellation. Barge in while speech is playing and check whether generation stops and queued audio is discarded. Fast startup does not make the interaction responsive if old audio continues after the caller speaks.
- Compare actual deployment paths. Run the CPU-hosted path and any hosted alternative from the regions and network routes your users will use. Assess data handling, availability, operating cost, and maintenance alongside latency.
Choose a deployment by its trade-offs
| Question | Cloud-hosted inference | Local CPU inference |
|---|---|---|
| Where does TTS run? | On a remote service; verify which components use GPU-backed infrastructure. | On the host CPU; Piper documentation describes local ONNX model execution and streaming output. |
| What contributes to response time? | Model work plus network, queueing, media transport, and playback. | CPU generation plus local scheduling, contention, buffering, and playback. |
| What does the cited evidence establish? | NVIDIA publishes vendor GPU results and a sub-one-second target, but the cited four-GPU measurements do not prove cloud-only deployment performance. | Local streaming is documented, but no comparable CPU production latency benchmark is established. |
| What must your team validate? | Latency from real user regions, concurrency, availability, and the service’s data handling. | Target host capacity, tail latency under contention, voice and language suitability, and operational upkeep. |
Cost, service-level guarantees, and a controlled CPU/GPU voice-quality comparison are not established by the cited performance material. Treat them as requirements to evaluate for your own service and traffic, not as implications of a latency table.
Production details beyond synthesis speed
A voice agent can synthesize quickly and still feel sluggish or unreliable if it mishandles turn-taking. OpenAI’s account of its real-time voice stack describes work on awkward pauses, clipped interruptions, delayed barge-in, media-session termination, stable ownership for ICE/DTLS sessions, and global first-hop routing latency. These are system concerns that an isolated TTS benchmark cannot measure. OpenAI: How OpenAI delivers low-latency voice AI at scale
As OpenAI puts it, “Voice AI only feels natural if conversation moves at the speed of speech.”
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