Falcon 3 10B, developed by TII, is a powerful language model with 10 billion parameters designed for advanced text generation tasks. It excels in generating coherent and contextually rich text, making it suitable for applications such as content creation, chatbots, and natural language understanding. With a context length of 8192 tokens, Falcon 3 10B can maintain a longer and more detailed context compared to many other models in its class, which is particularly useful for tasks requiring deep contextual understanding. The model is licensed under Apache-2.0, making it accessible for both commercial and non-commercial projects.
In terms of performance, Falcon 3 10B holds its own against other models of similar size. It offers a good balance between computational efficiency and output quality, making it a strong contender for those who need high-quality text generation without the resource demands of larger models. The available quantizations (Q4_K_M and Q8_0) further enhance its efficiency, allowing it to run on a variety of hardware setups. Users with GPUs ranging from 6.4 to 10.7 GB of VRAM can realistically deploy this model locally, making it a versatile choice for developers and researchers looking to integrate sophisticated text generation capabilities into their projects. Ideal users include those working on content generation, conversational agents, and any application where nuanced and context-aware text is crucial.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 5.856 GB | 6.36 GB | 6.86 GB | 85% |
| Q8_0 | 8 | 10.203 GB | 10.7 GB | 11.2 GB | 98% |
Context window & KV cache
Adds 1.25 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 10B
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 falcon3:10b - 2
Chat
ollama run falcon3:10b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"falcon3:10b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Falcon 3 10B 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 10Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
7.7 GB
6.4 GB weights + 0.8 GB KV
Aggregate tok/s
25
across 1 user
Per-user tok/s
25
10 B dense
✅ Fits in 24 GB VRAM with 16.3 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 10B?
Falcon 3 10B requires 6.36 GB VRAM minimum with Q4_K_M quantization. For full precision you need 10.7 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 10B?
To run Falcon 3 10B, you need a GPU with at least 6.4 GB of VRAM for quantized versions, and up to 10.7 GB for the full-precision model.
Is Falcon 3 10B good for coding?
Falcon 3 10B is well-suited for coding tasks, offering strong performance in generating code and understanding programming contexts.
Falcon 3 10B vs Llama 3.1 8B?
Falcon 3 10B has more parameters (10B vs 8B), which generally results in better performance and more nuanced outputs, but it requires more VRAM and computational resources.
Can I run Falcon 3 10B on a Mac?
Yes, you can run Falcon 3 10B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (6.4 GB to 10.7 GB).
How much VRAM does Falcon 3 10B need?
Falcon 3 10B requires 6.4 GB to 10.7 GB of VRAM, depending on the quantization level used.
Is Falcon 3 10B censored?
Falcon 3 10B is not inherently censored, but its responses can be filtered or moderated based on the implementation and settings used.
Is Falcon 3 10B commercial-use allowed?
Yes, Falcon 3 10B is licensed under Apache-2.0, allowing for commercial use without restrictions.
Falcon 3 10B context length?
Falcon 3 10B supports a context length of 8192 tokens, which is suitable for handling longer inputs and generating detailed outputs.
Does Falcon 3 10B support function calling?
Falcon 3 10B does not natively support function calling, but you can implement this functionality through custom scripts or integrations.
Falcon 3 10B quantization options?
Falcon 3 10B supports various quantization options, including 8-bit, 4-bit, and 2-bit, which reduce VRAM usage and improve inference speed.
Can Falcon 3 10B run on CPU?
While Falcon 3 10B can run on a CPU, it will be significantly slower compared to running on a GPU. Consider using a GPU for better performance.
Falcon 3 10B fine-tuning?
Falcon 3 10B can be fine-tuned on specific datasets to improve performance on particular tasks, but this requires significant computational resources and expertise.
Falcon 3 10B system requirements?
Falcon 3 10B requires a powerful GPU with 6.4 GB to 10.7 GB of VRAM, at least 16 GB of RAM, and a multi-core CPU for optimal performance.
Falcon 3 10B performance benchmark?
Falcon 3 10B typically processes around 50-100 tokens per second on a high-end GPU, with performance varying based on the specific hardware and quantization level.
Falcon 3 10B for RAG?
Falcon 3 10B can be used for Retrieval-Augmented Generation (RAG) tasks, combining its strong language capabilities with external data sources for enhanced outputs.
Falcon 3 10B for agents?
Falcon 3 10B can be integrated into conversational agents and chatbots, providing robust language generation and understanding capabilities.
Falcon 3 10B for coding vs general?
Falcon 3 10B performs well in both coding and general tasks, but it may require fine-tuning or specific prompts to optimize performance for coding-specific scenarios.
Falcon 3 10B vs ChatGPT?
Falcon 3 10B offers similar capabilities to ChatGPT but with a different architecture and training methodology, potentially leading to different strengths in specific tasks.
Falcon 3 10B download size?
The download size of Falcon 3 10B varies depending on the quantization level, ranging from approximately 5 GB for 8-bit quantized versions to 20 GB for the full-precision model.
Best quant for Falcon 3 10B?
The best quantization level for Falcon 3 10B depends on your hardware and use case. 8-bit quantization offers a good balance between performance and resource efficiency.