docs: Clean up incomplete recipes and clarify Kubernetes-only focus (#4159)

Signed-off-by: Ben Hamm <ben.hamm@gmail.com>
Signed-off-by: Tanmay Verma <tanmay2592@gmail.com>
Signed-off-by: atchernych <atchernych@nvidia.com>
Co-authored-by: Biswa Panda <biswa.panda@gmail.com>
Co-authored-by: tanmayv25 <tanmay2592@gmail.com>
Co-authored-by: Tanmay Verma <tanmayv@nvidia.com>
Co-authored-by: Anant Sharma <anants@nvidia.com>
Co-authored-by: atchernych <atchernych@nvidia.com>

benchmarks/router/run_engines.sh

@@ -7,7 +7,7 @@
 export DYNAMO_HOME=${DYNAMO_HOME:-"/workspace"}
 NUM_WORKERS=8
 MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
-RECIPE_PATH="$DYNAMO_HOME/recipes/deepseek-r1-distill-llama-8b/trtllm"
+ENGINE_CONFIG_PATH="$DYNAMO_HOME/examples/backends/trtllm/engine_configs/deepseek-r1-distill-llama-8b"
 TENSOR_PARALLEL_SIZE=1
 DATA_PARALLEL_SIZE=1
 USE_MOCKERS=false
@@ -86,13 +86,13 @@ if [ ${#EXTRA_ARGS[@]} -eq 0 ]; then
         )
     elif [ "$USE_TRTLLM" = true ]; then
         # Default args for TensorRT-LLM engine using predefined YAML configs
-        # Config files located at: $RECIPE_PATH/{agg,decode,prefill}.yaml
+        # Config files located at: $ENGINE_CONFIG_PATH/{agg,decode,prefill}.yaml
         if [ "$MODE" = "prefill" ]; then
-            ENGINE_CONFIG="$RECIPE_PATH/prefill.yaml"
+            ENGINE_CONFIG="$ENGINE_CONFIG_PATH/prefill.yaml"
         elif [ "$MODE" = "decode" ]; then
-            ENGINE_CONFIG="$RECIPE_PATH/decode.yaml"
+            ENGINE_CONFIG="$ENGINE_CONFIG_PATH/decode.yaml"
         else
-            ENGINE_CONFIG="$RECIPE_PATH/agg.yaml"
+            ENGINE_CONFIG="$ENGINE_CONFIG_PATH/agg.yaml"
         fi
 
         EXTRA_ARGS=(

docs/backends/trtllm/README.md

@@ -158,7 +158,7 @@ cd $DYNAMO_HOME/examples/backends/trtllm
 ```bash
 cd $DYNAMO_HOME/examples/backends/trtllm
 
-export AGG_ENGINE_ARGS=./recipes/deepseek-r1/trtllm/agg/mtp/mtp_agg.yaml
+export AGG_ENGINE_ARGS=./engine_configs/deepseek-r1/agg/mtp/mtp_agg.yaml
 export SERVED_MODEL_NAME="nvidia/DeepSeek-R1-FP4"
 # nvidia/DeepSeek-R1-FP4 is a large model
 export MODEL_PATH="nvidia/DeepSeek-R1-FP4"

docs/backends/trtllm/gemma3_sliding_window_attention.md

@@ -30,7 +30,7 @@ VSWA is a mechanism in which a model’s layers alternate between multiple slidi
 cd $DYNAMO_HOME/examples/backends/trtllm
 export MODEL_PATH=google/gemma-3-1b-it
 export SERVED_MODEL_NAME=$MODEL_PATH
-export AGG_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_agg.yaml
+export AGG_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_agg.yaml
 ./launch/agg.sh
 ```
 
@@ -39,7 +39,7 @@ export AGG_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_agg.yaml
 cd $DYNAMO_HOME/examples/backends/trtllm
 export MODEL_PATH=google/gemma-3-1b-it
 export SERVED_MODEL_NAME=$MODEL_PATH
-export AGG_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_agg.yaml
+export AGG_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_agg.yaml
 ./launch/agg_router.sh
 ```
 
@@ -48,8 +48,8 @@ export AGG_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_agg.yaml
 cd $DYNAMO_HOME/examples/backends/trtllm
 export MODEL_PATH=google/gemma-3-1b-it
 export SERVED_MODEL_NAME=$MODEL_PATH
-export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_prefill.yaml
-export DECODE_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_decode.yaml
+export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_prefill.yaml
+export DECODE_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_decode.yaml
 ./launch/disagg.sh
 ```
 
@@ -58,7 +58,7 @@ export DECODE_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_decode.yaml
 cd $DYNAMO_HOME/examples/backends/trtllm
 export MODEL_PATH=google/gemma-3-1b-it
 export SERVED_MODEL_NAME=$MODEL_PATH
-export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_prefill.yaml
-export DECODE_ENGINE_ARGS=$DYNAMO_HOME/recipes/gemma3/trtllm/vswa_decode.yaml
+export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_prefill.yaml
+export DECODE_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_decode.yaml
 ./launch/disagg_router.sh
 ```

docs/backends/trtllm/gpt-oss.md

@@ -90,14 +90,14 @@ The deployment uses configuration files and command-line arguments to control be
 
 #### Configuration Files
 
-**Prefill Configuration (`recipes/gpt-oss-120b/trtllm/disagg/prefill.yaml`)**:
+**Prefill Configuration (`examples/backends/trtllm/engine_configs/gpt-oss-120b/prefill.yaml`)**:
 - `enable_attention_dp: false` - Attention data parallelism disabled for prefill
 - `enable_chunked_prefill: true` - Enables efficient chunked prefill processing
 - `moe_config.backend: CUTLASS` - Uses optimized CUTLASS kernels for MoE layers
 - `cache_transceiver_config.backend: ucx` - Uses UCX for efficient KV cache transfer
 - `cuda_graph_config.max_batch_size: 32` - Maximum batch size for CUDA graphs
 
-**Decode Configuration (`recipes/gpt-oss-120b/trtllm/disagg/decode.yaml`)**:
+**Decode Configuration (`examples/backends/trtllm/engine_configs/gpt-oss-120b/decode.yaml`)**:
 - `enable_attention_dp: true` - Attention data parallelism enabled for decode
 - `disable_overlap_scheduler: false` - Enables overlapping for decode efficiency
 - `moe_config.backend: CUTLASS` - Uses optimized CUTLASS kernels for MoE layers
@@ -145,7 +145,7 @@ python3 -m dynamo.frontend --router-mode round-robin --http-port 8000 &
 CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m dynamo.trtllm \
   --model-path /model \
   --served-model-name openai/gpt-oss-120b \
-  --extra-engine-args recipes/gpt-oss-120b/trtllm/disagg/prefill.yaml \
+  --extra-engine-args examples/backends/trtllm/engine_configs/gpt-oss-120b/prefill.yaml \
   --dyn-reasoning-parser gpt_oss \
   --dyn-tool-call-parser harmony \
   --disaggregation-mode prefill \
@@ -161,7 +161,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m dynamo.trtllm \
 CUDA_VISIBLE_DEVICES=4,5,6,7 python3 -m dynamo.trtllm \
   --model-path /model \
   --served-model-name openai/gpt-oss-120b \
-  --extra-engine-args recipes/gpt-oss-120b/trtllm/disagg/decode.yaml \
+  --extra-engine-args examples/backends/trtllm/engine_configs/gpt-oss-120b/decode.yaml \
   --dyn-reasoning-parser gpt_oss \
   --dyn-tool-call-parser harmony \
   --disaggregation-mode decode \

docs/backends/trtllm/llama4_plus_eagle.md

@@ -28,7 +28,7 @@ This guide demonstrates how to deploy Llama 4 Maverick Instruct with Eagle Specu
     - The other node runs the prefill worker.
 
 ## Notes
-* Make sure the (`eagle3_one_model: true`) is set in the LLM API config inside the `recipes/llama4/trtllm/eagle` folder.
+* Make sure the (`eagle3_one_model: true`) is set in the LLM API config inside the `examples/backends/trtllm/engine_configs/llama4/eagle` folder.
 
 ## Setup
 
@@ -52,7 +52,7 @@ See [this](./multinode/multinode-examples.md#setup) section from multinode guide
 ## Aggregated Serving
 ```bash
 export NUM_NODES=1
-export ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/eagle/eagle_agg.yml"
+export ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/llama4/eagle/eagle_agg.yml"
 ./multinode/srun_aggregated.sh
 ```
 
@@ -60,9 +60,9 @@ export ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/eagle/eagle_agg.yml"
 
 ```bash
 export NUM_PREFILL_NODES=1
-export PREFILL_ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/eagle/eagle_prefill.yaml"
+export PREFILL_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/llama4/eagle/eagle_prefill.yml"
 export NUM_DECODE_NODES=1
-export DECODE_ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/eagle/eagle_decode.yaml"
+export DECODE_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/llama4/eagle/eagle_decode.yml"
 ./multinode/srun_disaggregated.sh
 ```
 

docs/backends/trtllm/multimodal_support.md

@@ -27,7 +27,7 @@ Here are quick steps to launch Llama-4 Maverick BF16 in aggregated mode
 ```bash
 cd $DYNAMO_HOME
 
-export AGG_ENGINE_ARGS=./recipes/llama4/trtllm/multimodal/agg.yaml
+export AGG_ENGINE_ARGS=./examples/backends/trtllm/engine_configs/llama4/multimodal/agg.yaml
 export SERVED_MODEL_NAME="meta-llama/Llama-4-Maverick-17B-128E-Instruct"
 export MODEL_PATH="meta-llama/Llama-4-Maverick-17B-128E-Instruct"
 ./launch/agg.sh
@@ -79,8 +79,8 @@ cd $DYNAMO_HOME
 
 export MODEL_PATH=${MODEL_PATH:-"Qwen/Qwen2-VL-7B-Instruct"}
 export SERVED_MODEL_NAME=${SERVED_MODEL_NAME:-"Qwen/Qwen2-VL-7B-Instruct"}
-export PREFILL_ENGINE_ARGS=${PREFILL_ENGINE_ARGS:-"recipes/qwen2-vl-7b-instruct/trtllm/prefill.yaml"}
-export DECODE_ENGINE_ARGS=${DECODE_ENGINE_ARGS:-"recipes/qwen2-vl-7b-instruct/trtllm/decode.yaml"}
+export PREFILL_ENGINE_ARGS=${PREFILL_ENGINE_ARGS:-"examples/backends/trtllm/engine_configs/qwen2-vl-7b-instruct/prefill.yaml"}
+export DECODE_ENGINE_ARGS=${DECODE_ENGINE_ARGS:-"examples/backends/trtllm/engine_configs/qwen2-vl-7b-instruct/decode.yaml"}
 export MODALITY=${MODALITY:-"multimodal"}
 
 ./launch/disagg.sh

docs/backends/trtllm/multinode/multinode-examples.md

@@ -17,6 +17,8 @@ limitations under the License.
 
 # Example: Multi-node TRTLLM Workers with Dynamo on Slurm
 
+> **Note:** The scripts referenced in this example (such as `srun_aggregated.sh` and `srun_disaggregated.sh`) can be found in [`examples/basics/multinode/trtllm/`](https://github.com/ai-dynamo/dynamo/tree/main/examples/basics/multinode/trtllm/).
+
 To run a single Dynamo+TRTLLM Worker that spans multiple nodes (ex: TP16),
 the set of nodes need to be launched together in the same MPI world, such as
 via `mpirun` or `srun`. This is true regardless of whether the worker is
@@ -106,8 +108,8 @@ export IMAGE="<dynamo_trtllm_image>"
 # For example, assuming your cluster had a `/lustre` directory on the host, you
 # could add that as a mount like so:
 #
-# export MOUNTS="${PWD}/../:/mnt,/lustre:/lustre"
-export MOUNTS="${PWD}/../:/mnt"
+# export MOUNTS="${PWD}/../../../../:/mnt,/lustre:/lustre"
+export MOUNTS="${PWD}/../../../../:/mnt"
 
 # NOTE: In general, Deepseek R1 is very large, so it is recommended to
 # pre-download the model weights and save them in some shared location,
@@ -136,7 +138,7 @@ follow these steps below to launch an **aggregated** deployment across 4 nodes:
 
 ```bash
 # Default set in srun_aggregated.sh, but can customize here.
-# export ENGINE_CONFIG="/mnt/recipes/deepseek-r1/trtllm/agg/wide_ep/wide_ep_agg.yaml"
+# export ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/deepseek-r1/agg/wide_ep/wide_ep_agg.yaml"
 
 # Customize NUM_NODES to match the desired parallelism in ENGINE_CONFIG
 # The product of NUM_NODES*NUM_GPUS_PER_NODE should match the number of
@@ -165,8 +167,8 @@ deployment across 8 nodes:
 
 ```bash
 # Defaults set in srun_disaggregated.sh, but can customize here.
-# export PREFILL_ENGINE_CONFIG="/mnt/recipes/deepseek-r1/trtllm/disagg/wide_ep/wide_ep_prefill.yaml"
-# export DECODE_ENGINE_CONFIG="/mnt/recipes/deepseek-r1/trtllm/disagg/wide_ep/wide_ep_decode.yaml"
+# export PREFILL_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/deepseek-r1/disagg/wide_ep/wide_ep_prefill.yaml"
+# export DECODE_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/deepseek-r1/disagg/wide_ep/wide_ep_decode.yaml"
 
 # Customize NUM_PREFILL_NODES to match the desired parallelism in PREFILL_ENGINE_CONFIG
 # Customize NUM_DECODE_NODES to match the desired parallelism in DECODE_ENGINE_CONFIG

docs/backends/trtllm/multinode/multinode-multimodal-example.md

@@ -17,6 +17,8 @@ limitations under the License.
 
 # Example: Multi-node TRTLLM Workers with Dynamo on Slurm for multimodal models
 
+> **Note:** The scripts referenced in this example (such as `srun_aggregated.sh` and `srun_disaggregated.sh`) can be found in [`examples/basics/multinode/trtllm/`](https://github.com/ai-dynamo/dynamo/tree/main/examples/basics/multinode/trtllm/).
+
 > [!IMPORTANT]
 > There are some known issues in tensorrt_llm==1.1.0rc5 version for multinode multimodal support. It is important to rebuild the dynamo container with a specific version of tensorrt_llm commit to use multimodal feature.
 >
@@ -34,7 +36,7 @@ limitations under the License.
 >
 > Before running the deployment, you must update the engine configuration files to change `backend: DEFAULT` to `backend: default` (lowercase). Run the following command:
 > ```bash
-> sed -i 's/backend: DEFAULT/backend: default/g' /mnt/recipes/llama4/trtllm/multimodal/prefill.yaml /mnt/recipes/llama4/trtllm/multimodal/decode.yaml
+> sed -i 's/backend: DEFAULT/backend: default/g' /mnt/examples/backends/trtllm/engine_configs/llama4/multimodal/prefill.yaml /mnt/examples/backends/trtllm/engine_configs/llama4/multimodal/decode.yaml
 > ```
 
 
@@ -71,8 +73,8 @@ export IMAGE="<dynamo_trtllm_image>"
 # For example, assuming your cluster had a `/lustre` directory on the host, you
 # could add that as a mount like so:
 #
-# export MOUNTS="${PWD}/../:/mnt,/lustre:/lustre"
-export MOUNTS="${PWD}/../:/mnt"
+# export MOUNTS="${PWD}/../../../../:/mnt,/lustre:/lustre"
+export MOUNTS="${PWD}/../../../../:/mnt"
 
 # Can point to local FS as weel
 # export MODEL_PATH="/location/to/model"
@@ -100,8 +102,8 @@ deployment across 4 nodes:
 
 ```bash
 # Defaults set in srun_disaggregated.sh, but can customize here.
-# export PREFILL_ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/multimodal/prefill.yaml"
-# export DECODE_ENGINE_CONFIG="/mnt/recipes/llama4/trtllm/multimodal/decode.yaml"
+# export PREFILL_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/llama4/multimodal/prefill.yaml"
+# export DECODE_ENGINE_CONFIG="/mnt/examples/backends/trtllm/engine_configs/llama4/multimodal/decode.yaml"
 
 # Customize NUM_PREFILL_NODES to match the desired parallelism in PREFILL_ENGINE_CONFIG
 # Customize NUM_DECODE_NODES to match the desired parallelism in DECODE_ENGINE_CONFIG
@@ -123,7 +125,7 @@ deployment across 4 nodes:
 
 ## Understanding the Output
 
-1. The `srun_disaggregated.sh` launches three srun jobs instead of two. One for frontend, one for prefill   worker, and one for decode worker.
+1. The `srun_disaggregated.sh` launches three srun jobs instead of two. One for frontend, one for prefill worker, and one for decode worker.
 
 2. The OpenAI frontend will listen for and dynamically discover workers as
    they register themselves with Dynamo's distributed runtime:

docs/kubernetes/README.md

@@ -203,7 +203,7 @@ args:
   - python3 -m dynamo.trtllm
     --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-8B
     --served-model-name deepseek-ai/DeepSeek-R1-Distill-Llama-8B
-    --extra-engine-args /workspace/recipes/deepseek-r1-distill-llama-8b/agg.yaml
+    --extra-engine-args /workspace/examples/backends/trtllm/engine_configs/deepseek-r1-distill-llama-8b/agg.yaml
 ```
 
 Key customization points include:

examples/backends/trtllm/deploy/agg-with-config.yaml

@@ -67,4 +67,4 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/agg.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/agg.yaml

examples/backends/trtllm/deploy/agg.yaml

@@ -36,4 +36,4 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/agg.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/agg.yaml

examples/backends/trtllm/deploy/agg_router.yaml

@@ -39,5 +39,5 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/agg.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/agg.yaml
             - --publish-events-and-metrics

examples/backends/trtllm/deploy/disagg.yaml

@@ -37,7 +37,7 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/prefill.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/prefill.yaml
             - --disaggregation-mode
             - prefill
     TRTLLMDecodeWorker:
@@ -63,6 +63,6 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/decode.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/decode.yaml
             - --disaggregation-mode
             - decode

examples/backends/trtllm/deploy/disagg_planner.yaml

@@ -101,7 +101,7 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/decode.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/decode.yaml
             - --disaggregation-mode
             - decode
     TRTLLMPrefillWorker:
@@ -128,6 +128,6 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/prefill.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/prefill.yaml
             - --disaggregation-mode
             - prefill

examples/backends/trtllm/deploy/disagg_router.yaml

@@ -39,7 +39,7 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/prefill.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/prefill.yaml
             - --disaggregation-mode
             - prefill
             - --publish-events-and-metrics
@@ -65,6 +65,6 @@ spec:
             - --served-model-name
             - Qwen/Qwen3-0.6B
             - --extra-engine-args
-            - ./recipes/qwen3/trtllm/decode.yaml
+            - ./examples/backends/trtllm/engine_configs/qwen3/decode.yaml
             - --disaggregation-mode
             - decode

examples/backends/trtllm/engine_configs/README.md

+# TensorRT-LLM Engine Configurations
+
+This directory contains TensorRT-LLM engine configuration files for various model deployments.
+
+
+## Usage
+
+These YAML configuration files can be passed to TensorRT-LLM workers using the `--extra-engine-args` parameter:
+
+```bash
+python3 -m dynamo.trtllm \
+    --extra-engine-args "${ENGINE_ARGS}" \
+    ...
+```
+
+Where `ENGINE_ARGS` points to one of the configuration files in this directory.
+
+## Configuration Types
+
+### Aggregated (agg/)
+Single-node configurations that combine prefill and decode operations:
+- **simple/**: Basic aggregated setup
+- **mtp/**: Multi-token prediction configurations
+- **wide_ep/**: Wide expert parallel configurations
+
+### Disaggregated (disagg/)
+Separate configurations for prefill and decode workers:
+- **simple/**: Basic prefill/decode split
+- **mtp/**: Multi-token prediction with separate prefill/decode
+- **wide_ep/**: Wide expert parallel with expert load balancer
+
+## Key Configuration Parameters
+
+- **Parallelism**: `tensor_parallel_size`, `moe_expert_parallel_size`, `pipeline_parallel_size`
+- **Memory**: `kv_cache_config.free_gpu_memory_fraction`, `kv_cache_config.dtype`
+- **Batching**: `max_batch_size`, `max_num_tokens`, `max_seq_len`
+- **Scheduling**: `disable_overlap_scheduler`, `cuda_graph_config`
+
+## Notes
+
+- For disaggregated setups, ensure `kv_cache_config.dtype` matches between prefill and decode configs
+- WideEP configurations require an expert load balancer config (`eplb.yaml`)
+- Adjust `free_gpu_memory_fraction` based on your workload and attention DP settings