What is the Best Speech-to-Text Models Available and Which GPU Should I Deploy it on?

Vishnu Subramanian

Founder @JarvisLabs.ai

Deploy the latest speech-to-text models on the right GPU to maximize performance while minimizing costs. OpenAI's GPT-4o-transcribe leads in accuracy with a remarkable 2.46% WER, while Deepgram Nova-3 offers the best speed-to-accuracy balance for enterprise workloads. For production transcription services, A100 or H100 GPUs deliver the best balance of performance and cost-efficiency at scale.

Top Speech-to-Text Models in 2025

The speech-to-text landscape has evolved dramatically over the past year, with several models now offering near-human accuracy. After examining the major contenders across various scenarios (noisy environments, accented speech, and technical jargon), here's how they stack up:

When choosing the right model for your specific use case, consider these factors beyond just word error rate (WER):

• Diarization capabilities: Only Deepgram and AssemblyAI offer robust speaker identification
• Language support: GPT-4o-transcribe leads with 100+ languages, while specialized models may work better for specific languages
• Streaming vs. batch: Nova-3 and GPT-4o models support real-time transcription with low latency
• Deployment options: Self-hosted models give you more control but require infrastructure expertise

Cloud-hosted options like Deepgram make sense for rapid prototyping and lower volume workloads, while self-hosted Whisper becomes economical at scale (though requires significant engineering resources to maintain).

GPU Requirements for Speech-to-Text Models

Your GPU selection drastically impacts transcription speed and cost-effectiveness. Here's what the data shows about production environments:

Memory Requirements

Speech recognition models are memory-intensive but not as demanding as large language models:

• Whisper Large v3: Requires at least 10GB VRAM
• Faster-Whisper (8-bit quantized): Can run with 8GB VRAM
• Distilled models: Some can run with just 4-6GB VRAM

Performance Benchmarks

Benchmarks across various GPUs show substantial speed differences when running Whisper Large v3:

For production environments with high throughput requirements, the A100 or RTX 6000 Ada offers better value than the H100 in most cases, unless you're processing extremely large batches simultaneously.

Optimization Strategies

Several proven strategies can help maximize performance when deploying speech-to-text models:

1. Batch processing: Group short audio files together for higher GPU utilization
2. 8-bit quantization: Use with faster-whisper for up to 40% memory savings with minimal accuracy loss
3. Shorter segments: Process 30-second chunks in parallel rather than full files
4. Pre-processing: Remove silence and normalize audio before processing
5. Model selection: Use smaller models for initial passes, larger models for difficult segments

When scaling beyond a single GPU, distributing segments across multiple GPUs often yields better performance than tensor parallelism for speech recognition workloads.

Real-World Deployment Examples

A tiered approach works well for different workload scales:

• Development and testing: Cloud A5000 or RTX5000 instances
• Medium production: Multiple A100s in parallel for batch processing
• High-volume production: Load-balanced cluster of A100s with queuing system

For operations at extreme scale, dedicated H100 instances make sense, but most companies are better served with multiple A100 instances for better failure resilience and scheduling flexibility.

Recommendations By Use Case

For Startups and Small Teams

• Model: Self-hosted faster-whisper (distilled version) or Deepgram Nova-2 (pay-as-you-go)
• GPU: JarvisLabs A5000/RTX5000 instances ($0.39-0.49/hour)

For Enterprise Applications

• Model: Deepgram Nova-3 (for speed) or GPT-4o-transcribe (for accuracy)
• GPU: A100 for steady workloads, H100 for occasional bursts via JarvisLabs ($1.29-2.99/hour)

For AI Research

• Model: Whisper Large v3 (for customization)
• GPU: H100 for experimenting with foundation models and fine-tuning

Speech-to-text technology is moving incredibly fast, with new models releasing almost monthly. It's remarkable how much is possible even with modest hardware when proper optimizations are applied.

What's your specific use case? Are you building a real-time transcription system or processing batch recordings? Consider your throughput requirements, accuracy needs, and budget constraints when making your selection.