StableLM Zephyr 3B is a 3-billion parameter language model developed by Stability AI, designed for efficient local deployment. This model excels in generating coherent and contextually relevant text, making it suitable for a wide range of applications such as content creation, chatbots, and natural language understanding tasks. With a context length of 4096 tokens, it can handle longer sequences of text, which is beneficial for tasks requiring a deeper understanding of context, such as summarization or dialogue generation.
In its size class, StableLM Zephyr 3B holds its own, offering a balance between performance and resource efficiency. It is particularly notable for its ability to run on hardware with limited VRAM, requiring only 2.1–3.3 GB, which makes it accessible for users with mid-range GPUs. The available quantizations, Q4_K_M and Q8_0, further enhance its efficiency, allowing it to run smoothly on less powerful systems without significant loss of quality. This model is ideal for developers and enthusiasts who need a capable language model but have constraints on computational resources. Realistic hardware for running this model includes modern laptops and desktops with integrated or entry-level dedicated GPUs, making it a versatile choice for both personal and small-scale professional projects.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 1.591 GB | 2.09 GB | 2.59 GB | 85% |
| Q8_0 | 8 | 2.769 GB | 3.27 GB | 3.77 GB | 98% |
Context window & KV cache
Adds 0.33 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 4K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run StableLM Zephyr 3B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull stablelm-zephyr - 2
Chat
ollama run stablelm-zephyr - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"stablelm-zephyr","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running StableLM Zephyr 3B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host StableLM Zephyr 3Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.0 GB
2.1 GB weights + 0.4 GB KV
Aggregate tok/s
83
across 1 user
Per-user tok/s
83
3 B dense
✅ Fits in 24 GB VRAM with 21.0 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run StableLM Zephyr 3B?
StableLM Zephyr 3B requires 2.09 GB VRAM minimum with Q4_K_M quantization. For full precision you need 3.27 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 StableLM Zephyr 3B?
To run StableLM Zephyr 3B, you need a GPU with at least 2.1 GB of VRAM, but 3.3 GB is recommended for better performance, especially with higher quantization levels.
Is StableLM Zephyr 3B good for coding?
StableLM Zephyr 3B is suitable for coding tasks due to its compact size and good chat quality, making it a viable option for code generation and assistance.
StableLM Zephyr 3B vs Llama 3.1 8B?
StableLM Zephyr 3B has fewer parameters (3B) compared to Llama 3.1 8B (8B), which means it requires less VRAM and computational power, but may have slightly lower performance in complex tasks.
Can I run StableLM Zephyr 3B on a Mac?
Yes, you can run StableLM Zephyr 3B on a Mac with an M1 or M2 chip, as these processors provide sufficient computational power and VRAM to handle the model efficiently.
How much VRAM does StableLM Zephyr 3B need?
StableLM Zephyr 3B requires between 2.1 GB and 3.3 GB of VRAM, depending on the quantization level used.
Is StableLM Zephyr 3B censored?
StableLM Zephyr 3B is not explicitly censored, but it includes content filters to prevent the generation of harmful or inappropriate content.
Is StableLM Zephyr 3B commercial-use allowed?
The license for StableLM Zephyr 3B allows for commercial use, but you should review the specific terms to ensure compliance with any usage restrictions.
StableLM Zephyr 3B context length?
StableLM Zephyr 3B supports a context length of up to 4096 tokens, allowing for longer conversations and more detailed inputs.
Does StableLM Zephyr 3B support function calling?
StableLM Zephyr 3B does not natively support function calling, but you can implement custom solutions to integrate function calls into your application.
StableLM Zephyr 3B quantization options?
StableLM Zephyr 3B supports various quantization options, including 8-bit, 4-bit, and 2-bit, which can reduce VRAM usage and improve inference speed.
Can StableLM Zephyr 3B run on CPU?
Yes, StableLM Zephyr 3B can run on a CPU, but performance will be significantly slower compared to running on a GPU with adequate VRAM.
StableLM Zephyr 3B fine-tuning?
StableLM Zephyr 3B can be fine-tuned on your own data to improve performance on specific tasks, but this requires additional computational resources and expertise.
StableLM Zephyr 3B system requirements?
To run StableLM Zephyr 3B, you need a system with at least 8 GB of RAM, a modern CPU, and a GPU with 2.1 GB to 3.3 GB of VRAM, depending on the quantization level.
StableLM Zephyr 3B performance benchmark?
StableLM Zephyr 3B can process around 50-100 tokens per second on a mid-range GPU, with performance varying based on the quantization level and hardware specifications.
StableLM Zephyr 3B for RAG?
StableLM Zephyr 3B can be used for Retrieval-Augmented Generation (RAG) tasks, but its effectiveness will depend on the quality and relevance of the retrieved information.
StableLM Zephyr 3B for agents?
StableLM Zephyr 3B is suitable for creating conversational agents due to its good chat quality and compact size, making it efficient for deployment in various applications.
StableLM Zephyr 3B for coding vs general?
StableLM Zephyr 3B performs well in both coding and general tasks, but its smaller size may result in slightly less specialized performance compared to larger models dedicated to specific domains.
StableLM Zephyr 3B vs ChatGPT?
StableLM Zephyr 3B is a smaller, more lightweight model compared to ChatGPT, which offers more parameters and potentially better performance in complex tasks, but requires more computational resources.
StableLM Zephyr 3B download size?
The download size of StableLM Zephyr 3B varies depending on the quantization level, ranging from approximately 1.5 GB to 3 GB.
Best quant for StableLM Zephyr 3B?
The best quantization level for StableLM Zephyr 3B depends on your hardware and performance needs. 4-bit quantization is often a good balance between VRAM efficiency and inference speed.