Updated note about latency, added note about mem usage with dynamic shapes (#11126)

docs/OV_Runtime_UG/supported_plugins/CPU.md

@@ -90,7 +90,7 @@ Each stream is pinned to its own group of physical cores with respect to NUMA no
 See [optimization guide](@ref openvino_docs_deployment_optimization_guide_dldt_optimization_guide) for more details.
 
 > **NOTE**: When it comes to latency, one needs to keep in mind that running only one stream on multi-socket platform may introduce additional overheads on data transfer between NUMA nodes.
-> In that case it is better to run inference on one socket (please see [Optimizing for Throughput](../../optimization_guide/dldt_deployment_optimization_tput.md) for details).
+> In that case it is better to use ov::hint::PerformanceMode::LATENCY performance hint (please see [performance hints overview](@ref openvino_docs_OV_UG_Performance_Hints) for details).
 
 ### Dynamic shapes
 CPU plugin provides full functional support for models with dynamic shapes in terms of the opset coverage.
@@ -105,6 +105,9 @@ We can reduce memory consumption through memory reuse, and as a result achieve b
 
 @snippet snippets/cpu/dynamic_shape.cpp defined_upper_bound
 
+> **NOTE**: Using fully undefined shapes may result in significantly higher memory consumption compared to inferring the same model with static shapes.
+> If the memory consumption is unacceptable but dynamic shapes are still required, one can reshape the model using shapes with defined upper bound to reduce memory footprint.
+
 Some runtime optimizations works better if the model shapes are known in advance.
 Therefore, if the input data shape is not changed between inference calls, it is recommended to use a model with static shapes or reshape the existing model with the static input shape to get the best performance.