Update front-page readme with link to XLA flag doc

README.md

@@ -270,22 +270,29 @@ We currently enable training and evaluation for the following models:
 We will update this table as new models become available, so stay tuned.
 
 ## Environment Variables
-
-The [JAX image](https://github.com/NVIDIA/JAX-Toolbox/pkgs/container/jax) is embedded with the following flags and environment variables for performance tuning:
-
-| XLA Flags | Value | Explanation |
-| --------- | ----- | ----------- |
-| `--xla_gpu_enable_latency_hiding_scheduler` | `true`  | allows XLA to move communication collectives to increase overlap with compute kernels |
-| `--xla_gpu_enable_async_all_gather` | `true` | allows XLA to run NCCL [AllGather](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/operations.html#allgather) kernels on a separate CUDA stream to allow overlap with compute kernels |
-| `--xla_gpu_enable_async_reduce_scatter` | `true` | allows XLA to run NCCL [ReduceScatter](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/operations.html#reducescatter) kernels on a separate CUDA stream to allow overlap with compute kernels |
-| `--xla_gpu_enable_triton_gemm` | `false` | use cuBLAS instead of Trition GeMM kernels |
+The [JAX images](https://github.com/NVIDIA/JAX-Toolbox/pkgs/container/jax) are embedded with the following environment variables and XLA flags for performance tuning:
 
 | Environment Variable | Value | Explanation |
 | -------------------- | ----- | ----------- |
 | `CUDA_DEVICE_MAX_CONNECTIONS` | `1` | use a single queue for GPU work to lower latency of stream operations; OK since XLA already orders launches |
 | `NCCL_NVLS_ENABLE` | `0` | Disables NVLink SHARP ([1](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html#nccl-nvls-enable)). Future releases will re-enable this feature. |
 | `CUDA_MODULE_LOADING` | `EAGER` | Disables lazy-loading ([1](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#cuda-environment-variables)) which uses slightly more GPU memory. |
 
+XLA flags that tune performance are also set by default in the [JAX images](https://github.com/NVIDIA/JAX-Toolbox/pkgs/container/jax). To view the
+the flags currently set, you can inspect the container's environment variables:
+```sh
+# Update IMAGE to inspect a container of your choosing
+IMAGE=ghcr.io/nvidia/jax:jax
+
+docker run --rm quay.io/skopeo/stable inspect docker://$IMAGE | jq -r '.Env[]' | grep '^XLA_FLAGS='
+
+# which returns
+
+XLA_FLAGS= --xla_gpu_enable_latency_hiding_scheduler=true --xla_gpu_enable_async_all_gather=true --xla_gpu_enable_async_reduce_scatter=true --xla_gpu_enable_triton_gemm=false
+```
+
+See [GPU performance](./rosetta/docs/GPU_performance.md) for details about these, and other XLA flags, that enable high-performance for LLMs on NVIDIA GPUs.
+
 ## Profiling JAX programs on GPU
 See [this page](./docs/profiling.md) for more information about how to profile JAX programs on GPU.