feat(recipes): GLM-5.3-Flash NVFP4 variants — local-inference-lab + RedHatAI

Two new day-0 quants of the same arch, both ride the existing
glm53-flash-nvfp4:local overlay (NoPE-MLA mod is quant-independent):

- glm53-flash-nvfp4-lab: local-inference-lab mixed quant (NVFP4 experts +
  MXFP8 MTP experts, tool-eval 89/100 vs 85 LibertAIDAI / 90 FP8). Ships
  MTP=5 with a documented caveat: needs the modelopt.py language_model-
  prefix fix from forum 381350 #41 (MTP load may abort; drop the flag for
  baseline until the fix is vendored as overlay patch part 5).
- glm53-flash-nvfp4-redhat: RedHatAI W4A4 compressed-tensors quant. Weight
  index verified to contain ZERO MTP tensors -> no --speculative-config
  (baseline decode only). marlin flag documented with W4A4 fallback notes.

Both: served-model-name glm53, 2x Spark TP=2, 262144 ctx, GMU 0.85.

recipes/glm53-flash-nvfp4-lab/recipe.toml

@@ -0,0 +1,74 @@
+# GLM-5.3-Flash NVFP4 — local-inference-lab quant (vLLM) — 2× DGX Spark, TP=2.
+#
+# local-inference-lab's mixed-precision quant: main MoE experts NVFP4
+# (group-16), MTP layer-45 routed experts in MXFP8, W4A4 input scales
+# included. tool-eval-bench 89/100 (vs 85 LibertAIDAI, 90 official FP8) —
+# best quant for agents so far.
+# Model: https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4
+#        (~186 GB → ~93 GB/node on 2 Sparks; GMU 0.85 = ~101 GiB budget)
+#
+# Image: shared glm53-flash-nvfp4:local overlay (day-0 vLLM image + the
+# NoPE-MLA / sm120 sparse-MLA mod). Rebuilt from docker-images/glm53-flash-nvfp4/.
+#
+# ⚠ KNOWN DAY-0 ISSUE (forum 381350 #41): vLLM needs a small `modelopt.py`
+#   fix for THIS quant — the MTP weights are stored under
+#   `language_model.model.layers.45.*` while the quant config lists
+#   `model.language_model.layers.45.*`, so the drafter's scale lookup misses.
+#   Until the fix is vendored into the overlay (as patch part 5), a boot with
+#   --speculative-config may fail at MTP weight load. Fallback: comment the
+#   speculative-config line → clean baseline serve (~15 tok/s class).
+#
+# GB10 gotchas handled here (same as glm53-flash-nvfp4 / LibertAIDAI):
+#   --moe-backend marlin        (silent FP4 MoE corruption fix)
+#   VLLM_ENGINE_READY_TIMEOUT_S (60-min FlashInfer autotune is not a hang)
+#   --language-model-only       (vision front-end OOM-kills on GB10)
+#
+# PREREQUISITES: weights cached on both nodes:
+#   huggingface-cli download local-inference-lab/GLM-5.3-Flash-NVFP4
+#
+# REFERENCE:
+#   https://forums.developer.nvidia.com/t/glm-5-3-flash-320b-total-parameters-18b-active/381350/41
+#   https://github.com/kingjones30/GLM-5.3-Flash-2x-DGX-Spark (mod + base config)
+
+engine = "vllm"
+name = "GLM-5.3-Flash NVFP4-lab (vLLM, 2x Spark)"
+docker_image = "glm53-flash-nvfp4:local"
+docker_image_dir = "glm53-flash-nvfp4"
+model = "local-inference-lab/GLM-5.3-Flash-NVFP4"
+required_nodes = 2
+tensor_parallel_size = 2
+pipeline_parallel_size = 1
+distributed_backend = "mp"
+shm_size = "32gb"
+
+args = [
+    # Stable served name — quant-agnostic (shared across glm53 quants)
+    "--served-model-name", "glm53",
+    "--trust-remote-code",
+    "--distributed-executor-backend", "mp",
+    # ~93 GiB weights/node of 119.6 GiB; drop to 0.83 or 262144→131072 if the
+    # UMA MemFree gate (vLLM #48140) refuses boot
+    "--gpu-memory-utilization", "0.85",
+    "--max-model-len", "262144",
+    "--max-num-seqs", "1",
+    "--kv-cache-dtype", "fp8_ds_mla",
+    "--language-model-only",
+    "--moe-backend", "marlin",
+    # MTP-5 — REQUIRES the modelopt.py prefix fix (header). Remove if load aborts.
+    "--speculative-config", """
+'{"method":"mtp","num_speculative_tokens":5}'""",
+    "--tool-call-parser", "glm47",
+    "--enable-auto-tool-choice",
+    "--reasoning-parser", "glm45",
+]
+
+[env]
+HF_HUB_OFFLINE = "1"
+# Activates the NoPE-MLA rope-pad mod baked into the image
+VLLM_MLA_NOPE_PAD_ROPE = "1"
+# FlashInfer autotune can legitimately loop 60+ min on first boot
+VLLM_ENGINE_READY_TIMEOUT_S = "3600"
+
+TORCH_CUDA_ARCH_LIST = "12.1a"
+CUTE_DSL_ARCH = "sm_121a"
+OMP_NUM_THREADS = "4"

recipes/glm53-flash-nvfp4-redhat/recipe.toml

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+# GLM-5.3-Flash NVFP4 — RedHatAI quant (vLLM) — 2× DGX Spark, TP=2.
+#
+# RedHatAI/LLM-Compressor quant: MoE experts W4A4 NVFP4 (group-16, local
+# per-group FP8 scales, compressed-tensors nvfp4-pack-quantized). Vision /
+# embeddings / lm_head kept original precision. Quantizes via regex targets,
+# so no name-prefix matching bugs.
+# Model: https://huggingface.co/RedHatAI/GLM-5.3-Flash-NVFP4
+#        (~177 GB → ~88 GB/node on 2 Sparks; GMU 0.85 = ~101 GiB budget)
+#
+# ⚠ NO MTP DRAFTER IN THIS CHECKPOINT — the index has 146,745 tensors and
+#   zero layer-45/MTP weights. `--speculative-config mtp` WILL fail to load.
+#   This recipe is baseline decode only (~15 tok/s class on this arch —
+#   spec decode buys 1.3–2×; use glm53-flash-nvfp4[-lab] when MTP matters).
+#
+# Image: shared glm53-flash-nvfp4:local overlay (day-0 vLLM image + the
+# NoPE-MLA / sm120 sparse-MLA mod). The mod is arch-level, quant-independent.
+#
+# GB10 gotchas handled (see glm53-flash-nvfp4 header for detail):
+#   --moe-backend marlin → if vLLM rejects marlin for W4A4 at startup (marlin
+#   is mainly W4A16), remove the flag — but then verify output is not
+#   token-loop garbage (the silent FLASHINFER_CUTLASS corruption) before
+#   trusting it; `--moe-backend triton` is the other candidate.
+#
+# Vendor note: RedHatAI's own README suggests --no-enable-flashinfer-autotune
+# for a fast boot — deliberately NOT set here: the GB10 sparse-MLA kernels
+# rely on the autotune result (first boot is slow by design, tune persists to
+# ~/.cache/vllm). Add it if you want to skip re-tuning after the first run.
+#
+# PREREQUISITES: weights cached on both nodes:
+#   huggingface-cli download RedHatAI/GLM-5.3-Flash-NVFP4
+#
+# REFERENCE:
+#   https://huggingface.co/RedHatAI/GLM-5.3-Flash-NVFP4 (vendor vLLM cmd)
+#   https://github.com/kingjones30/GLM-5.3-Flash-2x-DGX-Spark (mod)
+
+engine = "vllm"
+name = "GLM-5.3-Flash NVFP4-redhat (vLLM, 2x Spark, no MTP)"
+docker_image = "glm53-flash-nvfp4:local"
+docker_image_dir = "glm53-flash-nvfp4"
+model = "RedHatAI/GLM-5.3-Flash-NVFP4"
+required_nodes = 2
+tensor_parallel_size = 2
+pipeline_parallel_size = 1
+distributed_backend = "mp"
+shm_size = "32gb"
+
+args = [
+    # Stable served name — quant-agnostic (shared across glm53 quants)
+    "--served-model-name", "glm53",
+    "--trust-remote-code",
+    "--distributed-executor-backend", "mp",
+    "--gpu-memory-utilization", "0.85",
+    "--max-model-len", "262144",
+    "--max-num-seqs", "1",
+    "--kv-cache-dtype", "fp8_ds_mla",
+    "--language-model-only",
+    "--moe-backend", "marlin",
+    # NO --speculative-config: checkpoint ships no MTP weights (verified index)
+    "--tool-call-parser", "glm47",
+    "--enable-auto-tool-choice",
+    "--reasoning-parser", "glm45",
+]
+
+[env]
+HF_HUB_OFFLINE = "1"
+VLLM_MLA_NOPE_PAD_ROPE = "1"
+VLLM_ENGINE_READY_TIMEOUT_S = "3600"
+
+TORCH_CUDA_ARCH_LIST = "12.1a"
+CUTE_DSL_ARCH = "sm_121a"
+OMP_NUM_THREADS = "4"