Consolidate Llama3.1 and Llama3.3 recipes in a single recipe

inference/trillium/vLLM/Llama3.3/README.md → inference/trillium/vLLM/Llama3.x/README.md

@@ -1,25 +1,27 @@
-# Serve Llama3.3 with vLLM on TPU VMs
+# Serve Llama3.x with vLLM on TPU VMs
 
 In this guide, we show how to serve
+[Llama3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct), 
 [Llama3.3-70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
 
 > **Note:** Access to Llama models on Hugging Face requires accepting the Community License Agreement and awaiting approval before you can download and serve them.
 
-## Step 0: Install `gcloud cli`
+## Step 0: Install `gcloud CLI`
 
 You can reproduce this experiment from your dev environment
 (e.g. your laptop).
 You need to install `gcloud` locally to complete this tutorial.
 
-To install `gcloud cli` please follow this guide:
+To install `gcloud CLI` please follow this guide:
 [Install the gcloud CLI](https://cloud.google.com/sdk/docs/install#mac)
 
 Once it is installed, you can login to GCP from your terminal with this
 command: `gcloud auth login`.
 
 ## Step 1: Create a v6e TPU instance
 
-We create a single VM. For Llama3.3-70B, at least 8 chips are required. If you need a different number of
+We create a single VM. For Llama3.1-8B, 1 chip is sufficient and for the 70B
+models, at least 8 chips are required. If you need a different number of
 chips, you can set a different value for `--topology` such as `1x1`,
 `2x4`, etc.
 
@@ -53,7 +55,7 @@ export ZONE=your-tpu-zone
 export PROJECT=your-tpu-project
 export QR_ID=your-queued-resource-id # e.g. my-qr-request
 
-# This command requests a v6e-8 (8 chips). Adjust accelerator-type for different sizes. For 1 chip, use --accelerator-type v6e-1.
+# This command requests a v6e-8 (8 chips). Adjust accelerator-type for different sizes. For 1 chip (Llama3.1-8B), use --accelerator-type v6e-1.
 gcloud alpha compute tpus queued-resources create $QR_ID \
     --node-id $TPU_NAME \
     --project $PROJECT --zone $ZONE \
@@ -69,25 +71,25 @@ gcloud alpha compute tpus queued-resources list --project $PROJECT --zone $ZONE
 
 Once the state is `ACTIVE`, your TPU VM is ready and you can proceed to the next steps.
 
-## Step 2: ssh to the instance
+## Step 2: SSH to the instance
 
 ```bash
 gcloud compute tpus tpu-vm ssh $TPU_NAME --project $PROJECT --zone=$ZONE
 ```
 
-## Step 3: Use the vllm docker image for TPU
+## Step 3: Use the latest vLLM Docker image for TPU
 
 ```bash
 export DOCKER_URI=vllm/vllm-tpu:nightly-20251129-28607fc-39e63de
 ```
 
-The docker image is pinged here for users to reproduce the [results below](#section-benchmarking).
+The docker image is pinned here for users to reproduce the [results below](#section-benchmarking).
 
 To use the latest stable version, set `DOCKER_URI=vllm/vllm-tpu:latest`.
 
 To use the latest nightly built image that has more recent features/improvements, set `DOCKER_URI=vllm/vllm-tpu:nightly`.
 
-## Step 4: Run the docker container in the TPU instance
+## Step 4: Run the Docker container in the TPU instance
 
 ```bash
 sudo docker run -it --rm --name $USER-vllm --privileged --net=host \
@@ -96,7 +98,7 @@ sudo docker run -it --rm --name $USER-vllm --privileged --net=host \
     --entrypoint /bin/bash ${DOCKER_URI}
 ```
 
-> **Note:** 150GB should be sufficient for the 70B model. For the 8B model allocate at least 17GB for the weights.
+> **Note:** 150GB should be sufficient for the 70B models. For the 8B model allocate at least 17GB for the weights.
 
 > **Note:** See [this guide](https://cloud.google.com/tpu/docs/attach-durable-block-storage) for attaching durable block storage to TPUs.
 
@@ -115,6 +117,8 @@ export HF_TOKEN=<your HF token>
 Now we start the vllm server.
 Make sure you keep this terminal open for the entire duration of this experiment.
 
+Here is the serving command for the 70B model:
+
 ```bash
 export MAX_MODEL_LEN=2048
 export TP=8 # number of chips
@@ -131,6 +135,14 @@ vllm serve meta-llama/Llama-3.3-70B-Instruct \
     --max-model-len $MAX_MODEL_LEN
 ```
 
+| Model | Input/Output Scenario | max-num-batched-tokens | max-num-seqs | tensor-parallel-size |
+|:--- | :--- | :--- | :--- | :--- |
+| Llama-3.x-70B-Instruct | Prefill Heavy | 2048 | 256 | 8 |
+| Llama-3.x-70B-Instruct | Decode Heavy/ Balanced | 512 | 256 | 8 |
+| Llama3.1-8B-Instruct | Prefill Heavy | 1024 | 128 | 1 |
+
+
+
 It takes a few minutes depending on the model size to prepare the server.
 Once you see the below snippet in the logs, it means that the server is ready
 to serve requests or run benchmarks:
@@ -156,7 +168,7 @@ export PROJECT=your-tpu-project
 gcloud compute tpus tpu-vm ssh $TPU_NAME --project $PROJECT --zone=$ZONE
 ```
 
-## Step 8: access the running container
+## Step 8: Access the running container
 
 ```bash
 sudo docker exec -it $USER-vllm bash
@@ -177,7 +189,7 @@ curl http://localhost:8000/v1/completions \
     }'
 ```
 
-## Step 10: Preparing the test image
+## Step 10: Prepare the test image
 
 You will need to install datasets as it's not available in the base vllm
 image.
@@ -186,7 +198,7 @@ image.
 pip install datasets
 ```
 
-## <a id="section-benchmarking"></a>Step 11: Run the benchmarking
+## <a id="section-benchmarking"></a>Step 11: Run the benchmark
 
 Finally, we are ready to run the benchmark:
 
@@ -270,4 +282,4 @@ Mean ITL (ms):                           35.12
 Median ITL (ms):                         30.73
 P99 ITL (ms):                            47.03
 ==================================================
-```
+```