Can M4 Pro run Nomic Embed Text v1.5?
Yes — runs locally
~90 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The M4 Pro (48 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 90 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality text embedding model. 137M params. Good for RAG and search.
Setup tutorial: Nomic Embed Text v1.5 on M4 Pro
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an Apple M4 Pro with FP16 quantization for Grade S performance at ~1228 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.8GB of free disk space, macOS 12.3 or later, and Xcode Command Line Tools installed. You can install Xcode CLT by running `xcode-select --install` in your terminal.
Expected performance
With the FP16 quantization, you can expect the model to run at approximately 1228 tokens per second, using around 0.8GB of VRAM. This leaves 47.2GB of VRAM available for context, allowing for a practical context window of up to 8192 tokens without significant performance degradation.
1. Install runtimeOllama (preferred on Apple Silicon)
brew install ollama
ollama init2. Download the model
Download the FP16 quantized model (0.3GB file) from Hugging Face.
ollama pull nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf3. Run it
ollama run nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf
ollama chat --model nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for M4 Pro
For optimal performance on the Apple M4 Pro, leverage the Metal/MLX backend to utilize the 48GB of unified memory. Ensure that MPS layers are enabled to take full advantage of the GPU's capabilities. With 48GB of VRAM, you can maintain a large context window while keeping the model's VRAM usage at 0.8GB, leaving ample headroom for other tasks.
Troubleshooting
Model does not load due to insufficient VRAM.
Ensure you have at least 48GB of VRAM available. If running other applications, close them to free up memory.
Performance is below 1228 tok/sec.
Check if the Metal/MLX backend is enabled and if MPS layers are utilized. Ensure no other processes are heavily using the GPU.
Ollama commands fail with permission errors.
Run `sudo chown -R $(whoami) /usr/local/lib/ollama` to correct permissions.
Alternative runtimes
While Ollama is the preferred runtime for Apple Silicon, you can also use LM Studio for a more graphical interface, llama.cpp for command-line flexibility, or MLX for direct Metal integration. Jan is another option but may require additional setup for Apple M4 Pro. Choose based on your specific needs for interactivity, performance, and ease of use.
Other models that run great on M4 Pro
FAQ (20)
What GPU do I need to run Nomic Embed Text v1.5?
Nomic Embed Text v1.5 requires a GPU with at least 0.3 GB of VRAM for basic operation, but 0.8 GB is recommended for optimal performance, especially with higher quantization levels.
Is Nomic Embed Text v1.5 good for coding?
Nomic Embed Text v1.5 is primarily designed for text embedding tasks, which can be useful for code search and retrieval, but it may not be as specialized for coding as models specifically trained on programming data.
Nomic Embed Text v1.5 vs Llama 3.1 8B?
Nomic Embed Text v1.5 has 0.137 billion parameters, making it significantly smaller than Llama 3.1 8B, which has 8 billion parameters. This makes Nomic Embed Text v1.5 more lightweight and easier to run on less powerful hardware, but it may not perform as well on complex tasks.
Can I run Nomic Embed Text v1.5 on a Mac?
Yes, you can run Nomic Embed Text v1.5 on a Mac, provided your Mac meets the minimum VRAM requirements of 0.3 GB and has the necessary software dependencies installed.
How much VRAM does Nomic Embed Text v1.5 need?
Nomic Embed Text v1.5 requires between 0.3 GB and 0.8 GB of VRAM, depending on the quantization level used. Lower quantization levels require less VRAM but may impact performance.
Is Nomic Embed Text v1.5 censored?
Nomic Embed Text v1.5 is not explicitly censored. However, it adheres to ethical guidelines and best practices in AI development, which may influence its training data and output.
Is Nomic Embed Text v1.5 commercial-use allowed?
Yes, Nomic Embed Text v1.5 is licensed under the Apache-2.0 license, which allows for both commercial and non-commercial use without restriction.
Nomic Embed Text v1.5 context length?
Nomic Embed Text v1.5 supports a context length of up to 8192 tokens, which is quite generous for most text embedding tasks.
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