NCP-GENL Model Optimization Practice Question
An engineer is using TensorRT-LLM to serve a chatbot model. They observe that the time to first token (TTFT) is high, but subsequent tokens are generated quickly. Which optimization should they prioritize to reduce TTFT?
⚠ Common exam trap
The trap here is conflating throughput optimizations like in-flight batching with latency reductions for the first token, which are distinct performance metrics.
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
✓
Optimize the prefill phase with kernel fusion and faster attention.
Time to first token is primarily determined by the prefill phase, which processes the input prompt. Optimizing this phase with fused kernels, efficient attention mechanisms, and reduced memory latency directly reduces TTFT. TensorRT-LLM includes highly optimized prefill kernels and supports FlashAttention, which significantly speeds up the prefill computation. Other options target throughput or cache management, not TTFT.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable in-flight batching to increase GPU utilization.
Why it's wrong here
In-flight batching improves overall throughput by processing multiple requests concurrently, but it does not specifically reduce the time to first token for a single request. In fact, it can increase TTFT under high load because the prefill phase competes with other requests for compute resources.
- ✓
Optimize the prefill phase with kernel fusion and faster attention.
Why this is correct
TTFT is dominated by the prefill phase, where the entire prompt is processed. Optimizing this phase with fused kernels, faster attention implementations like FlashAttention, and reducing memory overhead directly cuts TTFT. TensorRT-LLM provides optimized prefill kernels and supports FlashAttention, making this the most effective approach.
- ✗
Increase the KV cache size to avoid evictions.
Why it's wrong here
KV cache size affects the ability to handle long sequences and batch sizes, but it does not directly impact TTFT unless evictions cause recomputation. In a typical scenario with sufficient cache, increasing it further yields no TTFT benefit and wastes memory.
- ✗
Use a larger batch size for the prefill phase.
Why it's wrong here
Increasing prefill batch size processes more prompts at once, improving throughput, but it does not reduce TTFT for an individual request. Larger batches can actually increase latency for each request because the prefill computation is shared and takes longer.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.