diff --git a/benchmarks/streaming/summarize_results.py b/benchmarks/streaming/summarize_results.py
new file mode 100755
index 000000000..ab72f6f8d
--- /dev/null
+++ b/benchmarks/streaming/summarize_results.py
@@ -0,0 +1,79 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Collapse a directory of benchmark JSON results into one comparison table (and a combined CSV).
+
+python benchmarks/streaming/summarize_results.py benchmarks/streaming/results
+"""
+
+import csv
+import json
+import sys
+from pathlib import Path
+
+COLUMNS = [
+    ("source", "source"),
+    ("mode", "mode"),
+    ("video_decode_device", "decode"),
+    ("num_workers", "workers"),
+    ("batch_size", "bs"),
+    ("frames_per_s_node", "frames/s/node"),
+    ("first_batch_latency_s", "first_batch_s"),
+    ("p50_sample_latency_ms", "p50_ms"),
+    ("p95_sample_latency_ms", "p95_ms"),
+    ("p99_sample_latency_ms", "p99_ms"),
+]
+
+
+def main() -> None:
+    results_dir = Path(sys.argv[1] if len(sys.argv) > 1 else "benchmarks/streaming/results")
+    files = sorted(results_dir.rglob("*.json"))
+    if not files:
+        print(f"No JSON results under {results_dir}")
+        return
+
+    rows = []
+    for f in files:
+        d = json.loads(f.read_text())
+        d["hit_rate"] = d.get("video_decoder_cache", {}).get("hit_rate")
+        rows.append(d)
+
+    rows.sort(key=lambda r: (r.get("source", ""), r.get("mode", ""), r.get("video_decode_device", "")))
+
+    headers = [label for _, label in COLUMNS] + ["cache_hit_rate"]
+    widths = {h: len(h) for h in headers}
+    table = []
+    for r in rows:
+        row = {label: r.get(key, "") for key, label in COLUMNS}
+        row["cache_hit_rate"] = r.get("hit_rate", "")
+        table.append(row)
+        for h in headers:
+            widths[h] = max(widths[h], len(str(row[h])))
+
+    line = "  ".join(h.ljust(widths[h]) for h in headers)
+    print(line)
+    print("  ".join("-" * widths[h] for h in headers))
+    for row in table:
+        print("  ".join(str(row[h]).ljust(widths[h]) for h in headers))
+
+    combined = results_dir / "summary.csv"
+    with open(combined, "w", newline="") as fh:
+        writer = csv.DictWriter(fh, fieldnames=headers)
+        writer.writeheader()
+        writer.writerows(table)
+    print(f"\nWrote {combined}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/slurm/run_streaming_matrix.sh b/slurm/run_streaming_matrix.sh
new file mode 100755
index 000000000..85527b978
--- /dev/null
+++ b/slurm/run_streaming_matrix.sh
@@ -0,0 +1,77 @@
+#!/bin/bash
+# Submit the FULL streaming dataloading-benchmark matrix as isolated single-GPU SLURM jobs.
+#
+#   sources : hub (Hub streaming) | bucket (cold HF bucket) | warmed_bucket (prewarmed HF bucket)
+#   modes   : single (1 frame, all cameras) | sarm (8-step / 8s delta window)
+#   decode  : cpu (torchcodec on CPU, scales with workers) | cuda (NVDEC, offloads decode to the GPU)
+#
+# => 3 x 2 x 2 = 12 jobs. Each runs in its OWN job (1 node, 1 GPU) so an OOM is isolated and reported
+# per-job by SLURM (check `sacct -j <id> --format=JobID,State,MaxRSS,ReqMem`). Submit from a login node
+# inside the repo:  bash slurm/run_streaming_matrix.sh
+#
+# Knobs (env overrides):
+#   REPO_ID, BUCKET, WARM_BUCKET, OUT_DIR, NUM_BATCHES, TIME, MEM, GPUS
+#   CPU_WORKERS / CPU_BUFFER  (cpu-decode jobs)   GPU_WORKERS / GPU_BUFFER (cuda-decode jobs, kept low to
+#   bound VRAM + NVDEC sessions).   RUN  ("python" by default; set RUN="uv run python" if using uv).
+#   SOURCES / MODES / DECODES  to run a subset (e.g. SOURCES="hub bucket" DECODES="cpu").
+#   ACCOUNT / PARTITION / QOS  passed through to sbatch if set.
+set -euo pipefail
+
+REPO_DIR=$(git rev-parse --show-toplevel)
+REPO_ID=${REPO_ID:-pepijn223/robocasa_pretrain_human300_v4}
+BUCKET=${BUCKET:-hf://buckets/pepijn223/robocasa-stream}
+WARM_BUCKET=${WARM_BUCKET:-hf://buckets/pepijn223/robocasa-stream-warm}
+OUT_DIR=${OUT_DIR:-benchmarks/streaming/results}
+NUM_BATCHES=${NUM_BATCHES:-200}
+TIME=${TIME:-01:00:00}
+MEM=${MEM:-64G}
+GPUS=${GPUS:-1}
+CPU_WORKERS=${CPU_WORKERS:-8}
+GPU_WORKERS=${GPU_WORKERS:-2}   # low on purpose: each cuda worker holds a CUDA context + NVDEC session
+CPU_BUFFER=${CPU_BUFFER:-4000}
+GPU_BUFFER=${GPU_BUFFER:-1000}  # smaller buffer bounds on-GPU frame memory
+BATCH_SIZE=${BATCH_SIZE:-64}
+RUN=${RUN:-python}
+
+SOURCES=${SOURCES:-"hub bucket warmed_bucket"}
+MODES=${MODES:-"single sarm"}
+DECODES=${DECODES:-"cpu cuda"}
+
+mkdir -p "$REPO_DIR/logs" "$REPO_DIR/$OUT_DIR"
+
+data_root_for () {
+  case "$1" in
+    hub) echo "" ;;
+    bucket) echo "$BUCKET" ;;
+    warmed_bucket) echo "$WARM_BUCKET" ;;
+  esac
+}
+
+n=0
+for SOURCE in $SOURCES; do
+  DATA_ROOT=$(data_root_for "$SOURCE")
+  ROOTFLAG=""
+  [ -n "$DATA_ROOT" ] && ROOTFLAG="--data_files_root $DATA_ROOT"
+  for MODE in $MODES; do
+    for DECODE in $DECODES; do
+      if [ "$DECODE" = cpu ]; then W=$CPU_WORKERS; B=$CPU_BUFFER; else W=$GPU_WORKERS; B=$GPU_BUFFER; fi
+      sbatch \
+        --job-name="bench_${SOURCE}_${MODE}_${DECODE}" \
+        --nodes=1 --ntasks=1 --gpus="$GPUS" --cpus-per-task=$((W + 4)) \
+        --mem="$MEM" --time="$TIME" --output="$REPO_DIR/logs/%x-%j.out" \
+        ${ACCOUNT:+--account=$ACCOUNT} ${PARTITION:+--partition=$PARTITION} ${QOS:+--qos=$QOS} \
+        --wrap "set -euo pipefail; cd '$REPO_DIR'; \
+          export TOKENIZERS_PARALLELISM=false HF_HOME=\${HF_HOME:-\$SCRATCH/hf_home}; \
+          $RUN benchmarks/streaming/benchmark_streaming.py \
+            --repo_id $REPO_ID $ROOTFLAG \
+            --mode $MODE --source $SOURCE --video_decode_device $DECODE \
+            --batch_size $BATCH_SIZE --num_workers $W --buffer_size $B \
+            --num_batches $NUM_BATCHES --out_dir $OUT_DIR"
+      n=$((n + 1))
+    done
+  done
+done
+
+echo "Submitted $n jobs. Watch:  squeue -u \$USER"
+echo "Results land in $OUT_DIR/<source>_<mode>_bs${BATCH_SIZE}_w<workers>_<decode>.{json,csv}"
+echo "After they finish, summarize:  python benchmarks/streaming/summarize_results.py $OUT_DIR"