Can RTX 4060 Ti run Nomic Embed Text v1.5?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti (8 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 114 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 RTX 4060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 4060 Ti with FP16 quantization for Grade S performance at ~477 tok/sec.
Prerequisites
Before starting, ensure you have at least 1GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed with CUDA 11.8 or higher.
Expected performance
With the FP16 quantization, you can expect the model to run at ~477 tok/sec, using approximately 0.8GB of VRAM. The remaining 7.2GB of VRAM provides ample headroom for handling large context windows, making it suitable for tasks requiring extensive context such as RAG and search.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the FP16 quantized version of Nomic Embed Text v1.5 (0.3GB file size) 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 --context-length 8192
ollama interactive4. Optimize for RTX 4060 Ti
For optimal performance on the NVIDIA GeForce RTX 4060 Ti with 8GB VRAM, use the FP16 quantization and set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 0.8GB VRAM used by the model, you will have 7.2GB of VRAM available for context, allowing for a practical context window of up to 8192 tokens.
Troubleshooting
Insufficient VRAM during model loading
Reduce the --n-gpu-layers parameter to 16 or lower to decrease VRAM usage.
Slow tokenization speed
Ensure that the latest CUDA drivers are installed and that the GPU is not under heavy load from other processes.
Model fails to load
Verify that the model file was downloaded correctly and try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more advanced features or better integration with specific workflows. For example, llama.cpp offers more fine-grained control over quantization and performance tuning, while LM Studio provides a more user-friendly interface for model management and deployment. However, Ollama is generally the easiest to set up and use for this specific model and GPU combination.
Other models that run great on RTX 4060 Ti
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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