Falcon 3B, developed by TII, is a robust language model with 3 billion parameters, 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 summarization tasks. With a context length of 8192 tokens, Falcon 3B can handle longer inputs and maintain context over extended sequences, which is particularly useful for tasks requiring deep understanding and continuity. The model is licensed under Apache-2.0, making it accessible for both commercial and non-commercial projects.
In its size class, Falcon 3B stands out for its balance between performance and resource efficiency. It punches above its weight in terms of output quality, often delivering results comparable to larger models while requiring significantly less computational power. The available quantizations, Q4_K_M and Q8_0, further enhance its efficiency, allowing it to run smoothly on hardware with as little as 2.4 GB of VRAM. This makes it an ideal choice for users with mid-range GPUs or those looking to deploy powerful text generation capabilities on more modest hardware. Developers and hobbyists who need a versatile and efficient language model for local use will find Falcon 3B to be a valuable asset.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 1.868 GB | 2.37 GB | 2.87 GB | 85% |
| Q8_0 | 8 | 3.2 GB | 3.8 GB | 5 GB | 98% |
Context window & KV cache
Adds 0.66 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 8K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Falcon 3 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 falcon3:3b - 2
Chat
ollama run falcon3:3b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"falcon3:3b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Falcon 3 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 Falcon 3 3Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.3 GB
2.4 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 20.7 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 Falcon 3 3B?
Falcon 3 3B requires 2.37 GB VRAM minimum with Q4_K_M quantization. For full precision you need 3.8 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 Falcon 3 3B?
To run Falcon 3 3B, you need a GPU with at least 2.4 GB of VRAM, but 3.8 GB is recommended for better performance, especially with higher quantization levels.
Is Falcon 3 3B good for coding?
Falcon 3 3B is well-suited for coding tasks due to its compact size and good performance, making it efficient for generating code snippets and providing programming assistance.
Falcon 3 3B vs Llama 3.1 8B?
Falcon 3 3B has fewer parameters (3B vs 8B) and requires less VRAM, making it more lightweight and faster to run, but Llama 3.1 8B may offer better performance in complex tasks due to its larger size.
Can I run Falcon 3 3B on a Mac?
Yes, you can run Falcon 3 3B on a Mac, provided your Mac has a compatible GPU with at least 2.4 GB of VRAM and you have the necessary software environment set up.
How much VRAM does Falcon 3 3B need?
Falcon 3 3B requires between 2.4 GB and 3.8 GB of VRAM, depending on the quantization level used. Higher quantization reduces VRAM usage but may slightly impact performance.
Is Falcon 3 3B censored?
Falcon 3 3B is not inherently censored, but its responses can be filtered or moderated based on the configuration and settings used during deployment.
Is Falcon 3 3B commercial-use allowed?
Yes, Falcon 3 3B is licensed under Apache-2.0, which allows for commercial use as long as you comply with the terms of the license.
Falcon 3 3B context length?
Falcon 3 3B supports a context length of up to 8192 tokens, allowing it to handle longer inputs and maintain context over extended conversations.
Does Falcon 3 3B support function calling?
Falcon 3 3B does not natively support function calling, but you can implement custom logic to handle function calls in your application layer.
Falcon 3 3B quantization options?
Falcon 3 3B supports various quantization options, including 4-bit, 8-bit, and 16-bit, which can reduce VRAM usage and improve inference speed while maintaining acceptable performance.
Can Falcon 3 3B run on CPU?
Yes, Falcon 3 3B can run on a CPU, but it will be significantly slower compared to running on a GPU. A powerful multi-core CPU is recommended for better performance.
Falcon 3 3B fine-tuning?
Falcon 3 3B can be fine-tuned on specific datasets to improve its performance on particular tasks. Fine-tuning requires a suitable dataset and training infrastructure, such as a GPU with sufficient VRAM.
Falcon 3 3B system requirements?
To run Falcon 3 3B, you need a system with at least 8 GB of RAM, a compatible GPU with 2.4 GB to 3.8 GB of VRAM, and a multi-core CPU. Additional storage is required for model files and data.
Falcon 3 3B performance benchmark?
Falcon 3 3B typically processes around 50-100 tokens per second on a mid-range GPU, with performance varying based on the specific hardware and quantization level used.
Falcon 3 3B for RAG?
Falcon 3 3B can be used for Retrieval-Augmented Generation (RAG) by integrating it with a retrieval system to fetch relevant documents, enhancing its ability to generate accurate and contextually rich responses.
Falcon 3 3B for agents?
Falcon 3 3B is suitable for creating conversational agents and chatbots due to its compact size and good performance, making it efficient for real-time interactions.
Falcon 3 3B for coding vs general?
Falcon 3 3B performs well in both coding and general tasks, but its efficiency and smaller size make it particularly useful for coding, where quick responses and low resource usage are important.
Falcon 3 3B vs ChatGPT?
Falcon 3 3B is smaller and more lightweight than ChatGPT, making it easier to run on less powerful hardware. However, ChatGPT may offer more advanced features and better performance in complex conversational tasks.
Falcon 3 3B download size?
The download size of Falcon 3 3B varies depending on the quantization level, but it typically ranges from 1.5 GB to 3 GB.
Best quant for Falcon 3 3B?
The best quantization for Falcon 3 3B depends on your hardware and performance needs. 8-bit quantization offers a good balance between VRAM usage and performance, while 4-bit is more efficient but may slightly reduce accuracy.