Mistral Small — edge deployment

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-mistral-small-edge-builders

1. What does the term 'edge deployment' refer to in the context of AI models?

   1. Running AI models locally on devices like laptops, phones, or custom hardware
   2. Deploying AI models exclusively through cloud APIs
   3. Running AI models on servers in distant data centers
   4. Training AI models at the network edge

2. What is quantization in the context of AI model deployment?

   1. A way to combine multiple models into one
   2. A process for converting text into numerical representations
   3. A technique to increase model accuracy by adding more parameters
   4. A method to reduce model size by using fewer bits to represent each weight

3. What file format is mentioned in the lesson for distributing quantized Mistral Small models?

   1. ONNX (.onnx)
   2. PyTorch (.pt)
   3. GGUF
   4. TensorFlow Lite (.tflite)

4. What is Ollama, as referenced in the lesson's key terms?

   1. A quantization algorithm for reducing model size
   2. A dataset for training instruction-following models
   3. A cloud computing service for hosting AI models
   4. A tool for running language models locally on your own machine

5. Why might a healthcare clinic choose to run Mistral Small locally instead of using a cloud API?

   1. To ensure patient data never leaves the premises
   2. Because the clinic has unlimited internet bandwidth
   3. To achieve faster inference speeds than cloud services
   4. To reduce electricity costs during peak hours

6. Approximately how much RAM is required to run the 4-bit quantized version of Mistral Small?

   1. Around 4GB
   2. Around 40GB
   3. Around 14GB
   4. Around 100GB

7. Which of the following is NOT listed as a good use case for Mistral Small in the lesson?

   1. Large-scale data centers
   2. Field sales tablets
   3. Factory floor terminals
   4. Privacy-first consumer apps

8. What is the primary advantage of an open-weights license for a model like Mistral Small?

   1. It automatically makes the model run faster on all hardware
   2. It guarantees the model will be free forever
   3. It ensures the model cannot be quantized
   4. It allows commercial use without paying per-token fees

9. What does the lesson warn about before deploying the 4-bit version of Mistral Small?

   1. It lacks instruction-following capabilities
   2. It cannot run on Apple Silicon devices
   3. It requires internet connectivity to function
   4. It may not perform the same as the full-precision version

10. Compared to using a cloud API, what is the ongoing cost structure of running Mistral Small on local hardware?

    1. Pay-per-month subscription
    2. Free (no cost at all)
    3. Electricity costs only
    4. Metered per token used

11. What hardware platforms does the lesson say Mistral Small can run on at usable speed?

    1. Server-grade CPUs only
    2. Only expensive data center GPUs
    3. Apple Silicon, consumer NVIDIA, and AMD GPUs
    4. Cloud virtual machines exclusively

12. Why might a factory floor terminal be a good fit for Mistral Small?

    1. Because industrial environments may lack internet connectivity
    2. Because factories have unlimited electrical power
    3. Because the model needs frequent retraining on factory data
    4. Because factories typically have fast, reliable internet

13. In the comparison table, how much RAM does Llama 4 Scout require compared to Mistral Small?

    1. About the same RAM
    2. Significantly more RAM (40GB or more)
    3. Less RAM
    4. Less RAM when quantized

14. What does 'open-weights' mean for an AI model?

    1. The model must be connected to the internet to function
    2. The model can only be used in outdoor environments
    3. The model's parameters are publicly available to download and run
    4. The model automatically weights data by openness

15. What capability does Mistral Small maintain despite its small size?

    1. Image generation
    2. Voice synthesis
    3. Strong instruction following
    4. Real-time translation