MODEL PROFILE

Nemotron-3-Diarization-GGUF

audio-cpp · Nemotron-3-Diarization-GGUF

**Update 2026-09-26**: Since the initial release, Nemotron 3 Diarization has received improved NeMo parity for ragged batches and streaming tails, selectable flash/eager attention, self-describing speaker-probability output, ASR-compatible latency profiles, and integration with Nemotron’s speaker-tagged transcription pipeline. Existing published GGUFs remain compatible. GGUF weights for native inference with audio.cpp. Tested on CUDA, Vulkan, and Metal(Mac) backends. Nemotron 3 Diarization identifies who spoke when, with up to eight speakers. It supports offline, streaming, and server batch diarization. It returns speaker turns and timestamps, not transcribed words. This package was converted from the original `.nemo` checkpoint, not from an existing GGUF. Each file contains 362 tensors, frontend configuration, and an embedded model spec, with no separate weight or configuration files required. The BF16 file retains the original model weight precision, with the frontend mel filter stored in F32. BF16 is parity safe with Nemo with TF32 control.

license:openmdw-1.1
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/30 06:30:08
OVERVIEW

Model overview

**Update 2026-09-26**: Since the initial release, Nemotron 3 Diarization has received improved NeMo parity for ragged batches and streaming tails, selectable flash/eager attention, self-describing speaker-probability output, ASR-compatible latency profiles, and integration with Nemotron’s speaker-tagged transcription pipeline. Existing published GGUFs remain compatible. GGUF weights for native inference with audio.cpp. Tested on CUDA, Vulkan, and Metal(Mac) backends. Nemotron 3 Diarization identifies who spoke when, with up to eight speakers. It supports offline, streaming, and server batch diarization. It returns speaker turns and timestamps, not transcribed words. This package was converted from the original `.nemo` checkpoint, not from an existing GGUF. Each file contains 362 tensors, frontend configuration, and an embedded model spec, with no separate weight or configuration files required. The BF16 file retains the original model weight precision, with the frontend mel filter stored in F32. BF16 is parity safe with Nemo with TF32 control.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:openmdw-1.1。模型介绍与第三方评测均以原来源的具体版本和方法为准。