NCP-GENL Model Optimization Practice Question
Which strategy is most effective for optimizing an LLM that is too large to fit into a single GPU's VRAM?
⚠ Common exam trap
Students often select data parallelism or pipeline parallelism incorrectly, failing to recognize that single layers exceeding VRAM require splitting the actual weight matrices across multiple devices.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement tensor parallelism
Model parallelism, specifically tensor parallelism, is the standard approach for splitting a large model across multiple GPUs. By partitioning individual layers across different devices, the computation can be distributed, and the total memory requirement is spread proportionally. This allows for the deployment of models that exceed the capacity of a single GPU, enabling high-performance inference for massive parameter models that would otherwise be impossible to load.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the inference batch size
Why it's wrong here
Increasing the batch size increases the memory requirement per inference request. If the model already exceeds the VRAM of a single GPU, increasing the batch size will only worsen the memory overflow. It is the opposite of the strategy needed to address a memory capacity limitation.
- ✗
Apply dynamic quantization to the weights
Why it's wrong here
While quantization reduces the memory footprint of the weights, it may not be sufficient if the model size significantly exceeds the available GPU memory. Furthermore, dynamic quantization often incurs higher inference latency compared to static quantization, making it a secondary strategy compared to partitioning the model across multiple GPUs.
- ✓
Implement tensor parallelism
Why this is correct
Tensor parallelism involves splitting individual weight matrices of the model across multiple GPUs. This allows the model to reside on multiple devices, effectively pooling their VRAM and compute power. It is the primary method for scaling large models beyond the hardware limits of a single GPU device.
- ✗
Convert the model to a CPU-only format
Why it's wrong here
Moving the model to CPU execution would result in extreme latency, making it unsuitable for real-time LLM applications. The objective is to keep inference on the GPU for speed, so splitting the model across multiple GPUs is a much more performant solution than offloading to the host CPU.
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Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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