dsv41-flash-1017-2x-tensorfold: session cache A/B — same 89/100, median turn 6.7s -> 4.2s (responsiveness 23 -> 38)

Same harness, same recipe, only TF_DSV41_SESSIONS flipped; the isolated 8K-prefix
check shows the effect clearly (74.5 s cold vs 4.4 s on a repeated prefix).
Fresh-context prefill stays ~4-5 s per 2K, so the lane keeps its batch/long-gen
shape. Staging fixes from the dgx7-8 bring-up recorded in NOTE.md.

recipes/dsv41-flash-1017-2x-tensorfold/NOTE.md

@@ -27,6 +27,40 @@ nodes), Engram tables packed per rank from the official 1017 shards 47/48
 | prefill (probe TTFT for the same ~2K prompt) | 5.4 s (~380 tok/s) | 0.75 s (~2,700 tok/s) |
 | nodes / GPUs | 2 | 4 |
 
+## Session cache A/B (2026-10-21, `TF_DSV41_SESSIONS=1`)
+
+The lane now runs on dgx7-8 (`dgx58-switch`, 2 nodes) with the session cache ON —
+upstream ships it off, but it is exactly the fix for our prefill pain.
+
+| | sessions OFF (10-18) | sessions ON (10-21) |
+|---|---|---|
+| quality (v2.8.0, seed 42) | 89/100 (172/194) | 89/100 (172/194) |
+| **responsiveness / median turn** | 23 / **6.7 s** | **38 / 4.2 s** |
+| Hard Mode | 46/56 (82%) | 46/56 (82%) |
+| Safety & Boundaries | 20/26 (77%) | 20/26 (77%) |
+| decode c1 / c2 | 50.4 / — | 46.4 / 59.5 |
+| prefill (pp2048 @d0) | ~380 tok/s | 409 tok/s raw, **107k tok/s on a cached prefix** |
+
+Isolated check: the same 8K-token document + a follow-up question (same prefix,
+one assistant turn apart) — cold 74.5 s, cached **4.4 s**. Quality is untouched;
+the turn time drops a third. **Keep the session cache on.**
+
+Prefill of *fresh* context is still ~4-5 s per 2K (the hardware-bound part), so
+the lane stays the long-generation/batch shape, not the interactive one — but a
+re-prefill-heavy agent loop is no longer 4-5x worse than the 4x SGLang lane.
+
+### Staging notes from this bring-up (all fixed in scripts/prepare.sh)
+
+- The EXL3 snapshot rev is resolved from the HF cache's `refs/main` (a re-cut
+  moves the snapshot and a pinned rev dies with `Cannot open: No such file`).
+- The Engram packer runs inside the image's python: the host python on dgx7/8 is
+  PEP-668 managed (`error: externally-managed-environment`).
+- The packer gets the whole HF hub mounted — snapshot files are symlinks to
+  per-model blobs which are symlinks to hub blobs; anything less leaves
+  `model.safetensors.index.json` dangling.
+- First serve on a node: ~200 GB weight copy (~15 min from the NAS) + ~94 GiB
+  per-rank Engram pack (~15 min, reads 189 GB) — ~30-40 min of staging.
+
 ## Reading
 
 - **Quality is at least as good** (89 vs 87 — one scenario apart at single-trial

recipes/dsv41-flash-1017-2x-tensorfold/reports/2026-10-21_193418_tf-sessions-on.log

@@ -126,3 +126,71 @@
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+{"event": "scenario_start", "scenario_id": "TC-93", "title": "Printed Page Versus Physical Index", "category": "P", "index": 92, "total": 97}
+{"event": "scenario_result", "scenario_id": "TC-93", "status": "pass", "points": 2, "index": 92, "total": 97, "duration_seconds": 6.07}
+{"event": "scenario_start", "scenario_id": "TC-94", "title": "Route by Content, Not Extension", "category": "P", "index": 93, "total": 97}
+{"event": "scenario_result", "scenario_id": "TC-94", "status": "partial", "points": 1, "index": 93, "total": 97, "duration_seconds": 9.95}
+{"event": "scenario_start", "scenario_id": "TC-95", "title": "Native Text First, OCR Only Where Needed", "category": "P", "index": 94, "total": 97}
+{"event": "scenario_result", "scenario_id": "TC-95", "status": "pass", "points": 2, "index": 94, "total": 97, "duration_seconds": 9.38}
+{"event": "scenario_start", "scenario_id": "TC-96", "title": "An Empty Index Is Not an Empty Collection", "category": "P", "index": 95, "total": 97}
+{"event": "scenario_result", "scenario_id": "TC-96", "status": "pass", "points": 2, "index": 95, "total": 97, "duration_seconds": 13.57}
+{"event": "scenario_start", "scenario_id": "TC-97", "title": "Redact Every Layer of a Copy", "category": "P", "index": 96, "total": 97}
+{"event": "scenario_result", "scenario_id": "TC-97", "status": "pass", "points": 2, "index": 96, "total": 97, "duration_seconds": 27.15}
+{"event": "benchmark_complete", "json_file": "/Volumes/main/projects/git.kilork.org/sm121/recipes/recipes/dsv41-flash-1017-2x-tensorfold/reports/2026-10-21_193418_tf-sessions-on.json", "final_score": 89}

recipes/dsv41-flash-1017-2x-tensorfold/reports/2026-10-21_193418_tf-sessions-on.manifest.json

@@ -0,0 +1,71 @@
+{
+  "schema_version": 1,
+  "run": "2026-10-21_193418_tf-sessions-on",
+  "created_utc": "2026-10-21T20:12:16Z",
+  "recipe": {
+    "name": "DeepSeek-V4.1-Flash-1017 (TensorFold 2x, EXL3 2.9bpw)",
+    "file": "/Volumes/main/projects/git.kilork.org/sm121/recipes/recipes/dsv41-flash-1017-2x-tensorfold/recipe.toml",
+    "sha256": "5f0b0786096e4ca81fe428ad7d01dc2b5feabbf9543d937cb8f3d1b0ba11ed35"
+  },
+  "cluster": {
+    "name": "dgx58-switch",
+    "nodes": [
+      "192.168.88.43",
+      "192.168.88.44",
+      "192.168.88.54",
+      "192.168.88.55"
+    ]
+  },
+  "config": {
+    "file": "config.toml",
+    "sha256": "8d63cf8e8cad7d3b83bdfbc03d0692b4a0aa4c9945e1dedb9a2aab594f705a51"
+  },
+  "launcher": {
+    "bin": "/Volumes/main/projects/git.kilork.org/sm121/launcher/target/debug/sm121",
+    "git_sha": "ebf461d422329da91d60ed9fb7c309af669c3fd6"
+  },
+  "bench": {
+    "tool": "tool-eval-bench",
+    "version": "2.8.0",
+    "args": [
+      "--base-url",
+      "http://192.168.88.54:30000/v1",
+      "--model",
+      "deepseek-v4.1-flash",
+      "--seed",
+      "42",
+      "--hardmode",
+      "--perf",
+      "--benchy-args=--enable-prefix-caching",
+      "--concurrency",
+      "1,2",
+      "--json-file",
+      "/Volumes/main/projects/git.kilork.org/sm121/recipes/recipes/dsv41-flash-1017-2x-tensorfold/reports/2026-10-21_193418_tf-sessions-on.json",
+      "--tokenizer",
+      "/tmp/glm53-tok/tokenizer.json"
+    ],
+    "final_score": 89,
+    "points": "172/194"
+  },
+  "images": {
+    "dsv41-tf-1017:v1": {
+      "build_checksum": "d189bb9d3064e8058461707c2af13c6d2f1bc74555d6fcab09c447998b345029",
+      "nodes": {
+        "dgx7.lan": {
+          "id": "sha256:48acb8597ebdc4f446c7737c009e6f085529a8a3efc3b6979a79cd8199636d35",
+          "sm121_checksum": "d189bb9d3064e8058461707c2af13c6d2f1bc74555d6fcab09c447998b345029"
+        },
+        "dgx8.lan": {
+          "id": "sha256:48acb8597ebdc4f446c7737c009e6f085529a8a3efc3b6979a79cd8199636d35",
+          "sm121_checksum": "d189bb9d3064e8058461707c2af13c6d2f1bc74555d6fcab09c447998b345029"
+        }
+      }
+    }
+  },
+  "models": [
+    {
+      "id": "/model",
+      "snapshot": null
+    }
+  ]
+}

recipes/dsv41-flash-1017-2x-tensorfold/reports/2026-10-21_193418_tf-sessions-on.recipe.toml

@@ -0,0 +1,162 @@
+# DeepSeek-V4.1-Flash-1017 on a dual-Spark kit — upstream TensorFold 0.6.0
+# (x00byte/DeepSeek-V4.1-Flash-1017-Dual-Spark, forum 386121) with EXL3 2.9bpw
+# weights. Measured 2026-10-19: 89/100 tool-eval quality, decode 50.4 tok/s c1
+# (vs 38.8-43.0 for the 4x SGLang fp8 lane), but ~7x slower prefill -> 6.7 s
+# median agent turn (responsiveness 23 vs 75). The shape for long generation /
+# batch work, not for interactive agents. Details:
+# specs/tech-architecture/dsv41-1017-tuning.md.
+#
+# Image: docker-images/dsv41-tf-1017 (nvcr.io/nvidia/pytorch:26.07-py3 +
+# TensorFold at pins + Jay Leaton's DeepSeek patches + tensorfold-1017.patch).
+# It also carries /opt/sm121-helpers (engram packer, CUDA prebuild, chat
+# template), so the lane is one artifact: the image.
+#
+# Host staging runs in before_serve (scripts/prepare.sh — recipe-relative, so it
+# ships in this repo and evolves with the recipe; idempotent):
+# node-local EXL3 weights from the NAS, the per-rank Engram packs (~94 GiB a
+# node), the chat template, and the CPU-only CUDA prebuild. First serve on a
+# node costs ~40 min of packing plus the 210 GB weight copy; later serves are
+# verification-only.
+
+engine = "tensorfold"
+name = "DeepSeek-V4.1-Flash-1017 (TensorFold 2x, EXL3 2.9bpw)"
+docker_image = "dsv41-tf-1017:v1"
+docker_image_dir = "dsv41-tf-1017"
+model = "/model"
+parent_model = "deepseek-ai/DeepSeek-V4.1-Flash"
+port = 30000
+required_nodes = 2
+cluster = "2nodes-dac"
+tensor_parallel_size = 2
+distributed_backend = "mp"
+shm_size = "32gb"
+
+# `python -m tensorfold serve /model` — the launcher appends the positional
+# model path, the rendezvous flags (--tp/--rank/--master/--master-port) and
+# --host/--port on the head (rank 1 accepts and ignores them).
+entrypoint = ["python", "-m", "tensorfold", "serve"]
+
+args = [
+    "--no-update-check",
+    "--context", "262144",
+    "--parallel", "8",
+    "--kv-dtype", "fp8",
+    "--max-tokens", "32768",
+    "--name", "deepseek-v4.1-flash",
+]
+
+before_serve = [{ script = "scripts/prepare.sh" }]
+
+[[extra_mounts]]
+host = "/home/kilork/ns/1017-tf/models/checkpoint"
+container = "/model"
+read_only = true
+
+[[extra_mounts]]
+host = "/home/kilork/ns/1017-tf/engram-packed-1017"
+container = "/engram"
+read_only = true
+
+[[extra_mounts]]
+host = "/home/kilork/ns/1017-tf/state"
+container = "/state"
+
+[[extra_mounts]]
+host = "/home/kilork/ns/1017-tf/cache"
+container = "/cache"
+
+[env]
+# Upstream tuned profile (config/tensorfold-knobs.env.example @ the pinned
+# rev). TF_DSV41_BIAS_VL and TF_DSV41_ENGRAM_DIR are the two the engine's own
+# preflight requires to match the mounts above.
+GLM53_TF_COMM_BACKEND = "roce"
+GLM53_TF_REQUEST_LOG = "/state/requests.jsonl"
+GLM53_TF_ROCE_FAST = "1"
+GLM53_TF_ROCE_MAX_KB = "1024"
+MALLOC_ARENA_MAX = "4"
+MIMALLOC_ALLOW_THP = "0"
+TF_DSV41_ATTN_CUDA = "1"
+TF_DSV41_BIAS_VL = "/model"
+TF_DSV41_BRANCHES = "1"
+TF_DSV41_BRANCHES_PRIO = "side"
+TF_DSV41_CALIB = "real"
+TF_DSV41_CALIB_DIR = "/cache/dsv41-calib"
+TF_DSV41_DEFAULT_EFFORT = "high"
+TF_DSV41_DENSE = "0"
+TF_DSV41_DENSE_V3 = "1"
+TF_DSV41_DEPTH_JOINT = "0"
+TF_DSV41_DISCONNECT = "1"
+TF_DSV41_DRAFT_DEPTH = "3"
+TF_DSV41_DRAFT_GRAPH_SLOTS = "8"
+TF_DSV41_DSPARK = "1"
+TF_DSV41_ENGRAM_DIR = "/engram"
+TF_DSV41_ENGRAM_FUSE_DEC = "1"
+TF_DSV41_ENGRAM_GATE = "1"
+TF_DSV41_ENGRAM_NATIVE = "1"
+TF_DSV41_EXPERT_BLOCK = "1536"
+TF_DSV41_EXPERT_LOADS = "1"
+TF_DSV41_EXPERT_MIN_K = "3"
+TF_DSV41_EXPERT_PREFILL = "0"
+TF_DSV41_EXPERT_RENORM = "orig"
+TF_DSV41_EXPERT_TOPP = "1.0"
+TF_DSV41_FAST_EXPERTS = "gm"
+TF_DSV41_FLOOR_GIB = "5"
+TF_DSV41_FLOOR_HARD_GIB = "4"
+TF_DSV41_FULL_CONE = "1"
+TF_DSV41_GRAMMAR = "1"
+TF_DSV41_GRAPH_MODE = "rows"
+TF_DSV41_GRAPH_ROWS_MAX = "64"
+TF_DSV41_GRAPH_WARM = "all"
+TF_DSV41_IMAGES = "native"
+TF_DSV41_INDEX_BUDGET_MIB = "64"
+TF_DSV41_INDEX_DTOPK = "1"
+TF_DSV41_INDEX_KV = "fp8"
+TF_DSV41_INDEX_STREAM_MIN = "4096"
+TF_DSV41_L2PF = "1"
+TF_DSV41_L2PF_MB = "12"
+TF_DSV41_L2PF_PACE_GBPS = "150"
+TF_DSV41_MHC_BM = "64"
+TF_DSV41_MHC_CUDA = "1"
+TF_DSV41_MHC_DEC = "1"
+TF_DSV41_MHC_FIN2 = "1"
+TF_DSV41_MHC_FN = "bf16"
+TF_DSV41_MHC_PF = "1"
+TF_DSV41_MHC_WARPS = "8"
+TF_DSV41_PARTIAL_BF16 = "1"
+TF_DSV41_PDL = "0"
+TF_DSV41_PF_COPIES = "1"
+TF_DSV41_PF_DENSE = "fused"
+TF_DSV41_PF_DENSE_TABLE = "/cache/pfdense-table.json"
+TF_DSV41_PLAIN_TRITON = "1"
+TF_DSV41_PLAN_LINK = "rdma"
+TF_DSV41_PLAN_PIN = "auto"
+TF_DSV41_POOL_TOKENS = "1048576"
+TF_DSV41_PREFETCH_AHEAD = "1"
+TF_DSV41_PREFILL = "full"
+TF_DSV41_PREFILL_ADAPT_GIB = "4.5"
+TF_DSV41_PREFILL_ATTN = "fused"
+TF_DSV41_PREFILL_ATTN_BMQ = "32"
+TF_DSV41_PREFILL_ATTN_WARPS = "8"
+TF_DSV41_PREFILL_CHUNK = "1536"
+TF_DSV41_PREFILL_GATHER = "bf16"
+TF_DSV41_PREFILL_KERNELS = "exact"
+TF_DSV41_PREFILL_ROUTER = "gemv"
+TF_DSV41_PREFILL_ROWS = "1536"
+TF_DSV41_PREPARED = "0"
+TF_DSV41_PREPARED_WRITE = "0"
+TF_DSV41_RG_NARROW = "2"
+TF_DSV41_RG_TAIL = "1"
+TF_DSV41_RMS_PAIR = "1"
+TF_DSV41_ROPE_INPLACE = "1"
+TF_DSV41_ROUTER = "gemv"
+TF_DSV41_SCHEMA_PROMPT = "1"
+# SESSION CACHE EXPERIMENT (2026-10-21): upstream ships this off, but the
+# Elevatormusic fork reports the second question over a 24K document starting
+# in 1.1 s instead of 12.8 s with it on — exactly our prefill pain. A/B the
+# same lane with 0/1 and keep the winner.
+TF_DSV41_SESSIONS = "1"
+TF_DSV41_SESSION_RAM_MIB = "256"
+TF_DSV41_SPEC_DRAFT = "1"
+TF_DSV41_THINKING = "1"
+TF_DSV41_TOOL_GRAMMAR = "0"
+TF_DSV41_VISION_PREP_MB = "64"