Can RTX 3060 12GB run Nomic Embed Text v1.5?
Yes — runs locally
~84 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3060 12GB (12 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 84 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 3060 12GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Nomic Embed Text v1.5 runs at Grade S on an NVIDIA GeForce RTX 3060 12GB with FP16 quantization, achieving ~716 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.3GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.7 installed.
Expected performance
With the FP16 quantization, you can expect the model to achieve ~716 tok/sec, using approximately 0.8GB of VRAM. This leaves 11.2GB of VRAM available for context, allowing for a practical context window of up to 8192 tokens without running into memory constraints.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the FP16 quantized model (0.3GB) 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 interactive nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for RTX 3060 12GB
For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers flag to offload layers to the GPU. Given the 12GB VRAM, you can set --n-gpu-layers to 128 to maximize speed while ensuring stability. Additionally, enable flash attention (--flash-attn) to further enhance performance. Tensor parallelism is not necessary for this model size but can be explored for larger models.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers or decrease the batch size.
Slow inference times
Ensure CUDA is properly installed and the --flash-attn flag is enabled.
Model fails to load
Check the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used on this GPU. LM Studio is suitable for a more graphical interface, llama.cpp offers fine-grained control over model parameters, and Jan provides a lightweight option for quick testing. However, Ollama is recommended for its ease of use and optimized performance on CUDA-enabled GPUs.
Other models that run great on RTX 3060 12GB
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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