Can RTX 5060 run Whisper Large v3 Turbo?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 (8 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 114 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Optimized large Whisper model. Near-best accuracy with faster inference.
Setup tutorial: Whisper Large v3 Turbo on RTX 5060
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on the NVIDIA GeForce RTX 5060 with Q8_0 quantization, achieving ~238 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~238 tok/sec performance using approximately 2.0GB of VRAM, leaving 6.0GB of VRAM for context. This should allow for a practical context window of several minutes of audio, depending on the complexity of the input.
1. Install runtimeOllama
curl -sSL https://ollama.com/install.sh | sh
ollama install2. Download the model
Download the Q8_0 quantized model (1.5GB) from the Hugging Face repository.
ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda
ollama chat ggerganov/whisper.cpp:ggml-large-v3-turbo.bin4. Optimize for RTX 5060
For optimal performance on the NVIDIA GeForce RTX 5060 with 8GB VRAM, use the --n-gpu-layers parameter to offload some layers to the CPU if needed. The --flash-attn flag can also be used to reduce memory usage and improve speed. Given the 8GB VRAM, you can set --n-gpu-layers to 24 to balance between speed and memory usage. Tensor parallelism is not necessary for this model but can be explored if you have multiple GPUs.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using the --n-gpu-layers parameter, e.g., --n-gpu-layers 24.
Slow inference speed
Enable flash attention with the --flash-attn flag to optimize memory access patterns.
Model not loading
Ensure the model file is correctly downloaded and the Ollama runtime is properly installed. Try reinstalling Ollama with 'ollama install'.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio offers a GUI interface and is suitable for users who prefer a visual setup. llama.cpp provides more control over quantization and optimization settings, ideal for fine-tuning performance. Jan is a lightweight runtime that can be useful for deployment in resource-constrained environments. For the NVIDIA GeForce RTX 5060, Ollama is recommended for its ease of use and robust performance out-of-the-box.
Other models that run great on RTX 5060
FAQ (20)
What GPU do I need to run Whisper Large v3 Turbo?
To run Whisper Large v3 Turbo, you need a GPU with at least 2.0 GB of VRAM. The exact VRAM requirement can vary slightly depending on the quantization level used.
Is Whisper Large v3 Turbo good for coding?
Whisper Large v3 Turbo is primarily designed for speech recognition tasks and may not be optimized for coding-related tasks. For coding, models like Codex or CodeLLaMa might be more suitable.
Whisper Large v3 Turbo vs Llama 3.1 8B?
Whisper Large v3 Turbo has 0.81 billion parameters and is optimized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is more versatile for general language tasks. Choose based on your specific needs.
Can I run Whisper Large v3 Turbo on a Mac?
Yes, you can run Whisper Large v3 Turbo on a Mac as long as your Mac has a compatible GPU with at least 2.0 GB of VRAM. Ensure you have the necessary drivers and libraries installed.
How much VRAM does Whisper Large v3 Turbo need?
Whisper Large v3 Turbo requires at least 2.0 GB of VRAM. The exact amount can vary slightly depending on the quantization level used.
Is Whisper Large v3 Turbo censored?
Whisper Large v3 Turbo is not censored. It is an open-source model released under the MIT license, allowing for broad usage without content restrictions.
Is Whisper Large v3 Turbo commercial-use allowed?
Yes, Whisper Large v3 Turbo is licensed under the MIT license, which allows for commercial use without additional restrictions.
Whisper Large v3 Turbo context length?
The context length for Whisper Large v3 Turbo is currently unknown. Refer to the official documentation or model repository for the most accurate information.
Want personalized recommendations for your exact setup? Detect my hardware →