Can RTX 4070 Ti SUPER run Nomic Embed Text v1.5?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 144 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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 4070 Ti SUPER with FP16 quantization for Grade S performance at ~955 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.3GB of disk space available. Your system should be running Windows or Linux with the latest NVIDIA drivers (version 525.60.13 or later) and CUDA 11.8 or later installed.
Expected performance
With the FP16 quantization, you can expect to achieve ~955 tok/sec, using approximately 0.8GB of VRAM. This leaves 15.2GB of VRAM available for context, enabling you to handle large documents or multiple queries efficiently within the 8192 token context window.
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 --context-length 8192
ollama interactive nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use the FP16 quantization. Set `--n-gpu-layers` to 32 to fully utilize the GPU's memory. Enable flash attention (`--flash-attn`) to speed up inference. With 0.8GB VRAM used by the model, you have 15.2GB of VRAM available for context, allowing for a practical context window close to the maximum 8192 tokens.
Troubleshooting
Out of memory error during inference
Reduce the context length using `--context-length <new_value>` or decrease `--n-gpu-layers` to free up VRAM.
Slow inference times
Ensure that flash attention is enabled with `--flash-attn`. If still slow, try increasing the batch size with `--batch-size <value>`.
Model not found
Verify the model path and ensure the model is correctly downloaded. Use `ollama list` to check available models.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a graphical interface for easier management, while llama.cpp provides low-level control over inference parameters. Jan is suitable for distributed inference across multiple GPUs. However, Ollama is recommended for its ease of use and efficient performance on the NVIDIA GeForce RTX 4070 Ti SUPER.
Other models that run great on RTX 4070 Ti SUPER
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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