What GPU do I need to run livekit/turn-detector?
A 135M-parameter speech model. 135M parameters, published in F32. View on Hugging Face
turn-detector is published by livekit on Hugging Face, with 696,553 downloads and 142 likes to date. It's a LlamaForCausalLM model built for text-classification, published natively in F32.
What turn-detector is
turn-detector is a 135M-parameter model published by LiveKit on Hugging Face that classifies audio/text signals, released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from turn-detector's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Speech synthesis
- Voice agent pipelines
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 0.5 GB | 0.6 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.1 GB | 0.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 0.1 GB | 0.1 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run turn-detector at its published (F32) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
turn-detector: common questions
How much VRAM does turn-detector need?
0.6 GB at FP32, 0.2 GB at FP8 (quantized), 0.1 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 0.5 GB of weights plus inference overhead is the whole requirement.
Can turn-detector run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 0.5 GB, or 0.6 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 0.3 GB, or 0.3 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of turn-detector fit on one V100?
26, by VRAM alone. That card carries 16.0 GB and one copy needs 0.6 GB at FP32, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 26 copies is not 26 times the requests served.
How to run turn-detector
Run turn-detector with Transformers (Python)
Generic example using Hugging Face's transformers library, not from the model's own docs.
from transformers import pipeline
clf = pipeline("text-classification", model="livekit/turn-detector", device="cuda")
print(clf("example input"))Deploy turn-detector on Aquanode
Aquanode has no one-click deploy template for turn-detector; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× V100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2.5 models
- Qwen2.5-0.5B-Instruct (494M, BF16)
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- jina-code-embeddings-0.5b (494M, BF16)
- Qwen2.5-Coder-0.5B-Instruct (494M, BF16)
- Qwen2.5-0.5B-Instruct (494M, BF16)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
