Local vs. Cloud Transcription for Desktop Recording

TL; DR:

For a desktop recording app, cloud-hosted transcription is almost always the right default, because the GPU on your user's laptop is already fully claimed by the meeting itself. A video call consumes GPU for decode, encode, background segmentation, and compositing. Screen capture consumes more. Local ASR is a fourth tenant competing for the same silicon, and it is the only one of the four whose failure mode is a degraded meeting for the user.

Cloud transcription trades that GPU cost for roughly 32 KB/s of upstream bandwidth — about 6-10% of a 1080p video call — which is the cheaper resource to spend by a wide margin. Cloud transcription also offers more flexibility and customization in terms of transcription. For example, a Desktop Recording SDK allows teams to test out and switch between transcription providers with a single integration.

This article breaks down the GPU budget concretely: what each workload actually costs, what the models need, and how to decide. We'll be primarily discussing Whisper, as that's the most popular model.

Note: Throughout this article, "Whisper" refers to OpenAI's open-source model weights, which are MIT-licensed and free to run on your own hardware — not to OpenAI's hosted Whisper API or the various third-party services that use the name commercially.

What do local transcription models require?

The table below gives parameter count, download size, and accuracy for each Whisper model size. The last column is the one that matters for a meeting recorder: whether the model can realistically run while your user is on a call.

Model	Parameters	Weights on disk	Approx. WER	Viable during a live call?
`tiny`	39M	~75 MB	~13%	Yes, but accuracy is too low for meeting notes
`base`	74M	~142 MB	~10%	Marginal
`small`	244M	~466 MB	~7%	Yes on 16 GB machines, contentious on 8 GB
`medium`	769M	~1.5 GB	~5%	Risky — thermal ceiling lands mid-meeting
`large-v3`	1550M	~2.9 GB	~3.5%	No, not on a laptop during a call

The models accurate enough for usable meeting transcripts are the ones a laptop cannot spare the memory or thermal headroom for during a call, and the models that run comfortably are the ones producing double-digit word error rates.

That squeeze does not close by picking a better model, because it isn't really about the models. large-v3 runs fine on a laptop that isn't doing anything else. The constraint is that your user's machine is simultaneously decoding participant video, encoding a webcam feed, running background segmentation, and encoding your screen capture — and it has to keep doing all of that for the full length of the meeting without slowing down the user.

In practice, most developers ship something in the base-to-small range in order to accommodate all devices their users might be on. That means the customer with a 64 GB M4 Max and the customer with an 8 GB M1 Air both get a transcript built by the same mediocre model. Meanwhile, cloud transcription gives every user large-v3-class accuracy regardless of what they bought, for about $0.15 to $0.45 per audio hour.

What cloud transcription costs instead

Cloud transcription doesn’t require GPU spend, but it does require bandwidth. Streaming ASR providers overwhelmingly want 16 kHz mono 16-bit PCM.

16,000 samples/s × 2 bytes = 32 KB/s = 256 kbps
Over a 60-minute meeting: ~115 MB uploaded
A 1080p video conference typically consumes 1.5–4 Mbps

So uncompressed streaming ASR adds roughly 6–10% on top of the video call's own bandwidth. Encode to Opus at 24–32 kbps first and it drops under 2%. On any connection that can sustain the video call at all, this is not the constraint.

More arguments against local transcription for a desktop recorder

Extra software to download. When you base your application’s transcription on a local transcription model, your users need to have that model installed on their device if they want to use your desktop app. Forcing users to install additional third-party software, even it's bundled into your application, has implications for compliance and creates extra friction for your customers.

No speaker names. Even when running a version of Whisper with diarized transcripts enabled, you will just receive generic speaker labels like Speaker 1 and Speaker 2. Speaker names do not come from the audio, they come from the meeting platform's participant metadata. For that reason, it’s essential to work with a provider like Recall.ai that offers that metadata in the transcripts.

Without speaker labels, diarized transcripts are not useful for any product built on meeting data, as the platform won’t know the identities of the people speaking. This impacts the ability to create artifacts like action items, CRM updates, and more.

Issues with custom vocabulary. Accurately transcribing things like company or product names and industry jargon is an essential part of usable meeting transcripts. Many cloud providers have extensive support for the vocabulary used by various companies and situations. For example, AssemblyAI's “keyterms prompting” feature allows you to provide a list of up to 1,000 words or phrases to improve transcription accuracy.

Whisper's equivalent is initial_prompt, which is capped at roughly 224 tokens and, critically, only biases the first window of audio.

No provider optionality. With a cloud-based desktop recording SDK, switching transcription providers is a configuration change. This lets you A/B test two providers on real traffic, measure accuracy on your own audio, and try out different providers easily. With a local stack, the provider is your architecture, and it is much more labor-intensive to test and switch to a new one.

A Desktop Recording SDK offers flexible, high-quality transcription

Local ASR can work well, but making it work consistently across the wide range of hardware your customers use introduces compute, thermal, battery, model-distribution, accuracy, diarization, and maintenance tradeoffs. A cloud-backed transcription layer lets you move those concerns off the user's device while delivering a more consistent experience.

Recall.ai's Desktop Recording SDK is a cloud-backed layer that allows desktop applications to receive high-quality meeting audio and transcripts. The SDK supports Zoom, Microsoft Teams, and Google Meet on Windows and Apple Silicon Macs and can deliver transcripts, participant events, raw audio, and video data to an application in real time.

Here are a few of the Desktop Recording SDK's benefits for transcription:

Swap transcription providers easily. The Desktop Recording SDK supports Recall.ai's own transcription service plus AssemblyAI Deepgram, ElevenLabs, AWS Transcribe, Rev, and Speechmatics. Developers can switch between these transcription providers with a single field in the Create Desktop SDK Upload request:

recording_config: {
  transcript: {
    provider: {
      recallai_streaming: {}       // or deepgram_streaming, assembly_ai_v3_streaming, elevenlabs_streaming
    }
  },
  realtime_endpoints: [
    { type: 'desktop_sdk_callback', events: ['transcript.data', 'transcript.partial_data'] }
  ]
}

With the Desktop Recording SDK, changing transcription providers is a small change in the configuration, not a full rewrite of the architecture.

Offers speaker names. The Desktop Recording SDK collects participant metadata from the meeting platform itself, so utterances are attributed to real people.

Supports privacy without the GPU cost. Most teams reaching for local ASR are chasing a compliance requirement. Unlike other providers, Recall.ai offers zero data retention. If this is enabled, transcript data will never touch Recall.ai's servers, and all data will be processed on-device.