Block Unveils Dessa, a Full-Duplex Speech Model Trained Only on Speech

Block’s AI research lab has released Dessa, a full-duplex speech model trained only on human conversation. Block announced it on October 23, 2026 as a research preview. Dessa is a speech-to-speech model from Block that listens and talks at the same time. Block says it used no text and no pretrained models. That is a different design from the text-first stacks behind most voice agents today.
TL;DR

Runs on: A MacBook Pro M4 Max or better, locally, faster than real time.
Performance: Demo-only so far. Live recordings show backchannels, mid-sentence laughs and natural interruptions.
Best: Trained on 1 NVIDIA GB200 NVL72 rack (72 GPUs). Moshi used 1,016 H100 GPUs.
Worst: 0 published benchmark scores. No weights, paper, API or price.

What is Block’s Dessa?
Dessa is a true speech-to-speech model: audio in, audio out. According to Block’s project page, everything it knows came from human speech.
It is full-duplex, so it listens while it talks. Block says it learned from real conversation when to cut in. It also learned when to backchannel with short cues like “yeah” or “got it”.
Block’s demo clips show Dessa laughing mid-sentence and getting louder when asked to speak up. One clip shows it talking with OpenAI’s GPT-Live-1. Block says nobody steered any clip.
How does speech-only training work?
Most voice systems chain 3 parts: speech recognition, a text model, then speech synthesis. Block team argues that chain throws away emotion, sarcasm, laughter and sighs. Dessa hears and produces those signals directly.
Block has not published the architecture. Here is what it has disclosed:

Training data: human conversation audio only. No text and no pretrained models.
Compute: 1 NVIDIA GB200 NVL72, which links 72 Blackwell GPUs and 36 Grace CPUs.
Data efficiency: Block says Dessa learned language from hundreds of times fewer words than LLMs need.
Inference: faster than real time, on a MacBook Pro M4 Max or better.

The lab also explains why this is hard. Speech never repeats exactly. A spoken word takes thousands of times more bytes than the same word in text.
Kyutai’s Moshi, the best-known open full-duplex model, takes another route. Moshi starts from a text language model backbone and predicts time-aligned text tokens before audio tokens. Its model card lists 7 million hours of pretraining audio and 1,016 H100 GPUs.

How does Dessa compare with Moshi, PersonaPlex and GPT-Live-1?

Feature
Dessa (Block)
Moshi (Kyutai)
PersonaPlex-7B-v1 (NVIDIA)
GPT-Live-1 (OpenAI)

Starting point
Speech only, no text, no pretrained model
Helium text LM backbone plus audio
Fine-tuned from Moshi (Moshiko) weights
Not disclosed; delegates reasoning to a backend agent

Parameters
Not disclosed
8B (Hugging Face listing)
7B (model card)
Not disclosed

Training compute
1 GB200 NVL72 (72 GPUs)
1,016 H100 GPUs
Not disclosed
Not disclosed

Training audio
Not disclosed
7M h unsupervised, 2,000 h Fisher, 20,000 h synthetic
Fisher English, under 10,000 h
Not disclosed

Latency
Not disclosed (“faster than real time”)
200 ms in practice, 160 ms theoretical
0.170 s turn-taking, 0.240 s interruption (FullDuplexBench)
Not disclosed on model page

Weights and license
Not released
Open, CC-BY 4.0
Open, NVIDIA Open Model License
Closed

Runs on
MacBook Pro M4 Max or better
GPU; MLX, Candle and PyTorch checkpoints
NVIDIA A100 / H100, Linux
OpenAI cloud

Access and price
Research preview, waitlist
Free download
Free download (gated)
API, $0.05 per minute voice layer

Key Takeaways

Dessa is full-duplex and speech-to-speech, trained only on human conversation audio.
Block trained it on 1 GB200 NVL72 rack; Moshi used 1,016 H100s.
It runs locally on a MacBook Pro M4 Max, faster than real time.
Block published 0 benchmarks, no parameter count and no weights.
Access is a waitlist.

Check out the Dessa project page. Compare it with the Moshi paper, the PersonaPlex model card and the GPT-Live-1 docs. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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