Update vLLM documentation with additional model details

Expanded performance comparison of vLLM and llama-cpp for different models and workloads.

Author: Amanda-dong

docs/hpc/08_ml_ai_hpc/08_LLM inference/03_vLLM.md

@@ -4,18 +4,18 @@
 [`vLLM`](https://docs.vllm.ai/en/latest/) is an easy-to-use library for LLM inference and serving which support a wide variety of models with optimized kernels ensuring optimal utilization of GPUs.
 
 ## Why `vLLM`?
-We tested `vLLM` and `llama-cpp` on Torch, and found `vLLM` performs better on Torch for `Qwen2.5-0.5B-Instruct` with `256` input and `256` output tokens.
+We tested `vLLM` and `llama-cpp` on Torch, and found `vLLM` performs better on Torch for `Qwen2.5-0.5B-Instruct` and `Qwen2.5-7B-Instruct` with `256` input and `256` output tokens. In terms of practical usage, vLLM is better suited for high-concurrency and batch workloads, while llama-cpp is more appropriate for lightweight or single-user scenarios.
 
-|Inference Server|Total Throughput (tok/s)|Median Latency (ms)|Recommendation
+|Model|Inference Server|Generated Throughput (tok/s)|Median Latency (ms)|
 |-----|-----|-----|-----|
-|`vLLM`|~2273|~890|Best for Batch/Research|
-|`llama-cpp`|~1312|~1440 |Best for Single User|
+|0.5B|`vLLM`|~2273|~890|
+|0.5B|`llama-cpp`|~1312|~1440 |
+|7B|`vLLM`|~354|~5780 |
+|7B|`llama-cpp`|~277|~7200 |
 
 ### Test Environment
 GPU: NVIDIA L40S
 
-Model: `Qwen2.5-0.5B-Instruct`
-
 Precision: FP16
 
 Workload: 256 input / 256 output tokens