Can RTX 4090 run Nomic Embed Text v1.5?
Yes — runs locally
~192 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 192 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Nomic Embed Text v1.5 runs at Grade S on an NVIDIA GeForce RTX 4090 with FP16 quantization, achieving ~1432 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.8GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.
Expected performance
With the FP16 quantization, you can expect ~1432 tok/sec performance with only 0.8GB of VRAM in use, leaving 23.2GB of VRAM available for context. Given the remaining VRAM, you can practically achieve a large context window, close to the maximum 8192 tokens.
1. Install runtimeOllama
curl -L https://ollama.ai/install.sh | bash
ollama install2. 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 RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider using tensor parallelism with --tensor-parallel-size 2. This will maximize the utilization of the 24GB VRAM and achieve the best token throughput.
Troubleshooting
Low token throughput
Ensure that the --n-gpu-layers and --flash-attn flags are set correctly. Adjust the --tensor-parallel-size if necessary.
Out of memory errors
Reduce the context length or increase the --n-gpu-layers value to offload more layers to the GPU.
Model not found
Verify that the model was successfully downloaded and is available in the Ollama model directory.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different deployment scenarios. For example, LM Studio provides a graphical interface and is useful for non-technical users, while llama.cpp offers more control over quantization and optimization settings. Jan is ideal for cloud deployments and containerized environments.
Other models that run great on RTX 4090
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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