The Snowflake Arctic Embed S is a compact BERT-based model designed for efficient feature extraction and embedding generation. With only 33 million parameters, it offers a lightweight solution for generating embeddings from text inputs, making it particularly suitable for applications where resource constraints are a concern. The model supports a context length of up to 512 tokens, which is standard for many NLP tasks, ensuring it can handle a wide range of input sizes without significant performance degradation. Licensed under the Apache 2.0 license, it is freely available for both commercial and non-commercial use.
In its size class, the Snowflake Arctic Embed S stands out for its efficiency and performance. Despite its small parameter count, it manages to deliver embeddings that are useful for downstream tasks such as text classification, clustering, and similarity search. This makes it a strong contender for scenarios where computational resources are limited, but high-quality embeddings are still required. The model’s low VRAM requirement of just 0.1 GB means it can run smoothly on a wide range of hardware, including older or less powerful machines. Users looking for a balance between performance and resource efficiency will find this model particularly appealing. Ideal use cases include developers working on edge devices, small-scale projects, or those who need to deploy multiple models simultaneously with limited GPU memory.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 0.036 GB | 0.1 GB | 0.2 GB | 88% |
How to run Snowflake Arctic Embed S
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Local embedding server with OpenAI-compat /v1/embeddings.
Ollama home →- 1
Pull
ollama pull snowflake-arctic-embed:s - 2
Use
curl http://localhost:11434/api/embed -d '{"model":"snowflake-arctic-embed:s","input":"hello world"}'
Community benchmarks
Real tokens/sec reports from people running Snowflake Arctic Embed S on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
how much VRAM do I need to run Snowflake Arctic Embed S?
Snowflake Arctic Embed S requires 0.1 GB VRAM minimum with Q8_0 quantization. For full precision you need 0.1 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Snowflake Arctic Embed S?
Snowflake Arctic Embed S requires a GPU with at least 0.1 GB of VRAM, depending on the quantization level used.
Is Snowflake Arctic Embed S good for coding?
While Snowflake Arctic Embed S is primarily an embedding model, it can be useful for generating code embeddings but may not be as specialized for coding tasks as models designed specifically for that purpose.
Snowflake Arctic Embed S vs Llama 3.1 8B?
Snowflake Arctic Embed S has only 0.033 billion parameters, making it much smaller and more lightweight compared to Llama 3.1 8B, which has 8 billion parameters. This makes Snowflake Arctic Embed S easier to run on lower-end hardware.
Can I run Snowflake Arctic Embed S on a Mac?
Yes, you can run Snowflake Arctic Embed S on a Mac, provided your Mac has a compatible GPU with at least 0.1 GB of VRAM or sufficient CPU resources.
How much VRAM does Snowflake Arctic Embed S need?
Snowflake Arctic Embed S requires 0.1 GB of VRAM, depending on the quantization level used.
Is Snowflake Arctic Embed S censored?
Snowflake Arctic Embed S is not explicitly censored, but it adheres to the Apache-2.0 license, which generally ensures open and permissive use.
Is Snowflake Arctic Embed S commercial-use allowed?
Yes, Snowflake Arctic Embed S is licensed under Apache-2.0, which allows for commercial use without restrictions.
Snowflake Arctic Embed S context length?
The context length for Snowflake Arctic Embed S is 512 tokens.
Does Snowflake Arctic Embed S support function calling?
Snowflake Arctic Embed S is an embedding model and does not natively support function calling, but it can be integrated into systems that do.
Snowflake Arctic Embed S quantization options?
Snowflake Arctic Embed S supports various quantization levels, typically ranging from 8-bit to 4-bit, which can reduce VRAM usage while maintaining performance.
Can Snowflake Arctic Embed S run on CPU?
Yes, Snowflake Arctic Embed S can run on CPU, although it will be slower than on GPU. The small model size makes it feasible for CPU inference.
Snowflake Arctic Embed S fine-tuning?
Snowflake Arctic Embed S can be fine-tuned for specific tasks, but the process may require additional data and computational resources.
Snowflake Arctic Embed S system requirements?
To run Snowflake Arctic Embed S, you need a system with at least 0.1 GB of VRAM (GPU) or sufficient CPU resources, and enough RAM to handle the model's context length of 512 tokens.
Snowflake Arctic Embed S performance benchmark?
Performance benchmarks for Snowflake Arctic Embed S vary, but it typically processes around 100-200 tokens per second on a mid-range GPU, depending on the quantization level.
Snowflake Arctic Embed S for RAG?
Snowflake Arctic Embed S can be used in Retrieval-Augmented Generation (RAG) systems to generate high-quality embeddings for document retrieval and context generation.
Snowflake Arctic Embed S for agents?
Snowflake Arctic Embed S can be integrated into agent systems to provide compact and efficient embeddings for decision-making and interaction.
Snowflake Arctic Embed S for coding vs general?
Snowflake Arctic Embed S is more suited for general embedding tasks due to its compact size and multilingual support, rather than specialized coding tasks.
Snowflake Arctic Embed S vs ChatGPT?
Snowflake Arctic Embed S is a compact embedding model with 0.033 billion parameters, while ChatGPT is a large language model with billions of parameters, making it more powerful for text generation but requiring more resources to run.
Snowflake Arctic Embed S download size?
The download size of Snowflake Arctic Embed S is relatively small, typically around 30-50 MB, depending on the quantization level.
Best quant for Snowflake Arctic Embed S?
The best quantization level for Snowflake Arctic Embed S depends on your specific needs. 8-bit quantization offers a good balance between performance and resource efficiency, while 4-bit can further reduce VRAM usage with a slight trade-off in accuracy.