TensorRT-LLM Explainer Notebook

LLM InferenceTensorRT-LLMGPU Performance

A clean, intuitive walkthrough of how LLM inference pipelines behave under PyTorch eager-mode semantics, why decoding becomes memory-bound, and how TensorRT-LLM mitigates these bottlenecks through kernel fusion, paged KV cache, Tensor Core–optimized kernels, and FP8 execution. The explainer uses toy CPU simulations and diagrams to illustrate these behaviors, along with a cost-per-million-token model.

This explainer distills GPU inference bottlenecks into an intuitive visual guide, bridging eager-mode execution behavior with TensorRT-LLM’s fused-kernel, high-throughput runtime model.

Key Skills Demonstrated

LLM inference architecture and decode behavior
GPU systems performance reasoning
Memory bandwidth vs compute analysis
Tensor Core optimization concepts
Clear, intuitive technical communication

Core Concept Snippet

decode_loop.py

# naive eager-mode decode loop
for _ in range(num_tokens):
    attn = q @ k.T                # memory-bound dot product
    probs = softmax(attn)
    out = probs @ v               # second memory-bound step
    update_kv_cache(out)          # expensive tensor inserts

What This Snippet Shows

This snippet illustrates the fundamental bottleneck in eager-mode LLM decoding. Each generated token triggers multiple small, memory-bound operations that repeatedly load Q, K, and V tensors from global memory, compute attention, and update the KV cache. The result is high memory traffic, frequent kernel launches, and poor Tensor Core utilization.

Key Takeaways

LLM decoding is inherently memory-bound due to KV cache reads and writes that scale with sequence length.
Eager-mode execution amplifies overhead through many small kernel launches and inefficient memory access patterns.
Longer context increases KV traffic, making decode latency grow as sequences get longer.
TensorRT-LLM improves performance by fusing kernels, optimizing memory access, and reducing framework overhead — mitigating, though not eliminating, memory-bound behavior.
With FP8 and Tensor Core–optimized kernels, TensorRT-LLM can achieve 2–4× higher effective throughput in real-world inference workloads.

Conclusion