Document / support for using BFLOAT16 with (Xeon) TGI service

[eero-t]

The model used for ChatQnA supports BFLOAT16, in addition to TGI's default 32-bit float type: https://huggingface.co/Intel/neural-chat-7b-v3-3

TGI memory usage halves from 30GB to 15GB (and also its perf increases somewhat) if one tells it to use BFLOAT16:

--- a/ChatQnA/kubernetes/manifests/tgi_service.yaml
+++ b/ChatQnA/kubernetes/manifests/tgi_service.yaml
@@ -28,6 +29,8 @@ spec:
         args:
         - --model-id
         - $(LLM_MODEL_ID)
+        - --dtype
+        - bfloat16
         #- "/data/Llama-2-7b-hf"
         # - "/data/Mistral-7B-Instruct-v0.2"
         # - --quantize

However, only newer Xeons support BFLOAT16. Therefore, if user' cluster has heterogeneous nodes, TGI service needs a node selector that schedules it on a node with BFLOAT16 support.

This can be automated by using node-feature-discovery and its CPU feature labeling: https://kubernetes-sigs.github.io/node-feature-discovery/stable/usage/features.html#cpu

It would be good to add some documentation and examples (e.g. comment lines in YAML) for this.