diff --git a/benchmarks/quantization/eval_accuracy_for_readme.sh b/benchmarks/quantization/eval_accuracy_for_readme.sh
deleted file mode 100755
index 10e3150681..0000000000
--- a/benchmarks/quantization/eval_accuracy_for_readme.sh
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/bin/bash
-
-# eval_accuracy_for_readme.sh - Evaluate quantization recipe accuracy
-#
-# Usage: ./eval_accuracy_for_readme.sh [TAG_OR_RECIPE] [MODEL_ID] [LOG_FILE]
-#
-# Arguments:
-#   TAG_OR_RECIPE  (optional) Tag group, single recipe name, or "all" (default: all)
-#                  Valid tags: all, h100
-#                  Valid recipes: None, float8_rowwise,
-#                                 int4_groupwise_weight_float8_rowwise_activation,
-#                                 int4_groupwise_hqq_weight_only,
-#                                 int8_rowwise_weight_only, int8_rowwise
-#   MODEL_ID       (optional) HuggingFace model ID (default: meta-llama/Llama-3.1-8B)
-#   LOG_FILE       (optional) Output log file path (default: benchmarks/data/eval_accuracy_for_readme_log.txt)
-#
-# Examples:
-#   ./eval_accuracy_for_readme.sh                    # Run all recipes with default model
-#   ./eval_accuracy_for_readme.sh h100               # Run H100-compatible recipes only
-#   ./eval_accuracy_for_readme.sh float8_rowwise     # Run single recipe
-#   ./eval_accuracy_for_readme.sh h100 meta-llama/Llama-3.2-8B  # Custom model with H100 recipes
-#   ./eval_accuracy_for_readme.sh int8_rowwise meta-llama/Llama-3.2-8B my_log.txt  # All custom args
-
-set -e
-
-# Define all available quantization recipes
-QUANT_RECIPES_ALL=(
-  # no quantization (baseline)
-  "None"
-  "float8_rowwise"
-  "int4_groupwise_weight_float8_rowwise_activation"
-  # note: below only works on A100
-  "int4_groupwise_hqq_weight_only"
-  "int8_rowwise_weight_only"
-  "int8_rowwise"
-)
-
-# Define H100-compatible recipes (excludes A100-only recipes)
-# TODO(future PR): add `int4_groupwise_weight_float8_rowwise_activation` here, 
-#   need to fix https://gist.github.com/vkuzo/6b128681b628744d445c553cdeac8a85
-QUANT_RECIPES_H100=(
-  "None"
-  "float8_rowwise"
-  "int8_rowwise_weight_only"
-  "int8_rowwise"
-)
-
-# TODO(future PR): add A100 and B200 tag groups
-
-# Get tag/recipe as first positional argument (optional, default: all)
-TAG_OR_RECIPE="${1:-all}"
-
-# Get model_id as second positional argument (optional)
-MODEL_ID="${2:-meta-llama/Llama-3.1-8B}"
-
-# Get log file as third positional argument (optional)
-LOG_FILE="${3:-benchmarks/data/eval_accuracy_for_readme_log.txt}"
-
-# Select recipes based on tag or specific recipe
-if [ "$TAG_OR_RECIPE" = "all" ]; then
-  QUANT_RECIPES=("${QUANT_RECIPES_ALL[@]}")
-elif [ "$TAG_OR_RECIPE" = "h100" ]; then
-  QUANT_RECIPES=("${QUANT_RECIPES_H100[@]}")
-else
-  # Check if it's a valid recipe name
-  VALID_RECIPE=false
-  for recipe in "${QUANT_RECIPES_ALL[@]}"; do
-    if [ "$recipe" = "$TAG_OR_RECIPE" ]; then
-      VALID_RECIPE=true
-      QUANT_RECIPES=("$TAG_OR_RECIPE")
-      break
-    fi
-  done
-
-  if [ "$VALID_RECIPE" = false ]; then
-    echo "Error: Invalid tag or recipe name: '$TAG_OR_RECIPE'"
-    echo ""
-    echo "Valid tags:"
-    echo "  - all"
-    echo "  - h100"
-    echo ""
-    echo "Valid recipe names:"
-    for recipe in "${QUANT_RECIPES_ALL[@]}"; do
-      echo "  - $recipe"
-    done
-    exit 1
-  fi
-fi
-
-rm $LOG_FILE
-touch $LOG_FILE
-
-for quant_recipe in "${QUANT_RECIPES[@]}"; do
-
-  echo "processing $quant_recipe"
- 
-  OUTPUT_DIR="benchmarks/data/quantized_model/$MODEL_ID-$quant_recipe/"
-
-  # create quantized model
-  # Note: the -u flag is to prevent python from buffering stdout and stderr
-  # and make the output log file be in chronological order
-  rm -rf $OUTPUT_DIR && python -u benchmarks/quantization/create_quantized_model.py --model_id $MODEL_ID --output_dir $OUTPUT_DIR --quant_recipe_name $quant_recipe 2>&1 | tee -a "$LOG_FILE"
-
-  # run eval
-  lm_eval --model hf --model_args "pretrained=$OUTPUT_DIR" --tasks "wikitext,winogrande" --device "cuda:0" --batch_size auto --output_path "$OUTPUT_DIR/lm_eval_outputs/" 2>&1 | tee -a "$LOG_FILE"
-done
-
-# TODO(future PR): script to parse the log file instead of manual copy-paste
diff --git a/benchmarks/quantization/measure_accuracy_and_performance.sh b/benchmarks/quantization/measure_accuracy_and_performance.sh
new file mode 100755
index 0000000000..9793ec569d
--- /dev/null
+++ b/benchmarks/quantization/measure_accuracy_and_performance.sh
@@ -0,0 +1,159 @@
+#!/bin/bash
+
+# measure_accuracy_and_performance.sh - Evaluate quantization recipe accuracy
+#
+# Usage: ./measure_accuracy_and_performance.sh [TAG_OR_RECIPE] [MODEL_ID] [LOG_FILE]
+#
+# Arguments:
+#   TAG_OR_RECIPE  (optional) Tag group, single recipe name, or "all" (default: all)
+#                  Valid tags: all, h100
+#                  Valid recipes: None, float8_rowwise,
+#                                 int4_groupwise_weight_float8_rowwise_activation,
+#                                 int4_groupwise_hqq_weight_only,
+#                                 int8_rowwise_weight_only, int8_rowwise
+#   MODEL_ID       (optional) HuggingFace model ID (default: meta-llama/Llama-3.1-8B)
+#   LOG_FILE       (optional) Output log file path (default: benchmarks/data/measure_accuracy_and_performance_log.txt)
+#
+# Environment Variables:
+#   SKIP_MODEL_CREATE  If set to 1, skip creating quantized models (assumes models already exist)
+#   SKIP_LM_EVAL       If set to 1, skip running lm_eval (only creates quantized models)
+#   SKIP_VLLM          If set to 1, skip running vllm performance benchmarking
+#
+# Examples:
+#   ./measure_accuracy_and_performance.sh                    # Run all recipes with default model
+#   ./measure_accuracy_and_performance.sh h100               # Run H100-compatible recipes only
+#   ./measure_accuracy_and_performance.sh float8_rowwise     # Run single recipe
+#   ./measure_accuracy_and_performance.sh h100 meta-llama/Llama-3.2-8B  # Custom model with H100 recipes
+#   ./measure_accuracy_and_performance.sh int8_rowwise meta-llama/Llama-3.2-8B my_log.txt  # All custom args
+#   SKIP_MODEL_CREATE=1 ./measure_accuracy_and_performance.sh h100  # Skip model creation, only run eval
+#   SKIP_LM_EVAL=1 ./measure_accuracy_and_performance.sh h100  # Skip lm_eval, only create models
+#   SKIP_VLLM=1 ./measure_accuracy_and_performance.sh h100  # Skip vllm benchmarking
+
+set -e
+
+# Define all available quantization recipes
+QUANT_RECIPES_ALL=(
+  # no quantization (baseline)
+  "None"
+  "float8_rowwise"
+  "int4_groupwise_weight_float8_rowwise_activation"
+  # note: below only works on A100
+  "int4_groupwise_hqq_weight_only"
+  "int8_rowwise_weight_only"
+  "int8_rowwise"
+)
+
+# Define H100-compatible recipes (excludes A100-only recipes)
+# TODO(future PR): add `int4_groupwise_weight_float8_rowwise_activation` here,
+#   need to fix https://gist.github.com/vkuzo/6b128681b628744d445c553cdeac8a85
+QUANT_RECIPES_H100=(
+  "None"
+  "float8_rowwise"
+  "int8_rowwise_weight_only"
+  "int8_rowwise"
+)
+
+# Define recipes that are known to be broken in vllm
+VLLM_BROKEN_RECIPES=(
+  # TODO(future PR): fix this
+  # current stack trace: https://gist.github.com/vkuzo/eed4894c5f3434e15d70b163e6077f60
+  "float8_rowwise"
+  # as of this PR, this recipe is still using AQT and CUDA graph capture time
+  # in vLLM is really slow (>5 mins)
+  # TODO(future PR): reenable this once we migrate this recipe off of AQT
+  "int8_rowwise_weight_only"
+  # TODO(future PR): fix this
+  # error: https://gist.github.com/vkuzo/5bf389079442bb9851ef315cdcb797b4
+  "int8_rowwise"
+)
+
+# TODO(future PR): add A100 and B200 tag groups
+
+# Get tag/recipe as first positional argument (optional, default: all)
+TAG_OR_RECIPE="${1:-all}"
+
+# Get model_id as second positional argument (optional)
+MODEL_ID="${2:-meta-llama/Llama-3.1-8B}"
+
+# Get log file as third positional argument (optional)
+LOG_FILE="${3:-benchmarks/data/measure_accuracy_and_performance_log.txt}"
+
+# Select recipes based on tag or specific recipe
+if [ "$TAG_OR_RECIPE" = "all" ]; then
+  QUANT_RECIPES=("${QUANT_RECIPES_ALL[@]}")
+elif [ "$TAG_OR_RECIPE" = "h100" ]; then
+  QUANT_RECIPES=("${QUANT_RECIPES_H100[@]}")
+else
+  # Check if it's a valid recipe name
+  VALID_RECIPE=false
+  for recipe in "${QUANT_RECIPES_ALL[@]}"; do
+    if [ "$recipe" = "$TAG_OR_RECIPE" ]; then
+      VALID_RECIPE=true
+      QUANT_RECIPES=("$TAG_OR_RECIPE")
+      break
+    fi
+  done
+
+  if [ "$VALID_RECIPE" = false ]; then
+    echo "Error: Invalid tag or recipe name: '$TAG_OR_RECIPE'"
+    echo ""
+    echo "Valid tags:"
+    echo "  - all"
+    echo "  - h100"
+    echo ""
+    echo "Valid recipe names:"
+    for recipe in "${QUANT_RECIPES_ALL[@]}"; do
+      echo "  - $recipe"
+    done
+    exit 1
+  fi
+fi
+
+rm -rf $LOG_FILE
+touch $LOG_FILE
+
+for quant_recipe in "${QUANT_RECIPES[@]}"; do
+
+  echo "processing $quant_recipe"
+
+  OUTPUT_DIR="benchmarks/data/quantized_model/$MODEL_ID-$quant_recipe/"
+
+  # create quantized model (unless skipped via environment variable)
+  if [ "${SKIP_MODEL_CREATE:-0}" != "1" ]; then
+    # Note: the -u flag is to prevent python from buffering stdout and stderr
+    # and make the output log file be in chronological order
+    rm -rf $OUTPUT_DIR && python -u benchmarks/quantization/create_quantized_model.py --model_id $MODEL_ID --output_dir $OUTPUT_DIR --quant_recipe_name $quant_recipe 2>&1 | tee -a "$LOG_FILE"
+  else
+    echo "Skipping model creation (SKIP_MODEL_CREATE=1), using existing model at $OUTPUT_DIR"
+  fi
+
+  # run eval (unless skipped via environment variable)
+  if [ "${SKIP_LM_EVAL:-0}" != "1" ]; then
+    lm_eval --model hf --model_args "pretrained=$OUTPUT_DIR" --tasks "wikitext,winogrande" --device "cuda:0" --batch_size auto --output_path "$OUTPUT_DIR/lm_eval_outputs/" 2>&1 | tee -a "$LOG_FILE"
+  else
+    echo "Skipping lm_eval (SKIP_LM_EVAL=1)"
+  fi
+
+  # simple performance test (unless skipped via environment variable)
+  if [ "${SKIP_VLLM:-0}" != "1" ]; then
+    # Check if this recipe is known to be broken in vllm
+    RECIPE_BROKEN_IN_VLLM=false
+    for broken_recipe in "${VLLM_BROKEN_RECIPES[@]}"; do
+      if [ "$quant_recipe" = "$broken_recipe" ]; then
+        RECIPE_BROKEN_IN_VLLM=true
+        break
+      fi
+    done
+
+    if [ "$RECIPE_BROKEN_IN_VLLM" = true ]; then
+      echo "Skipping vllm benchmarking for $quant_recipe (known to be broken in vllm)"
+    else
+      vllm bench latency --input_len 256 --output_len 256 --model $OUTPUT_DIR --batch_size 1 2>&1 | tee -a "$LOG_FILE"
+    fi
+  else
+    echo "Skipping vllm benchmarking (SKIP_VLLM=1)"
+  fi
+
+done
+
+# TODO(future PR): script to parse the log file instead of manual copy-paste