diff --git a/docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx b/docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx
index db455a6ed..2ac8aa5ff 100644
--- a/docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx
+++ b/docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx
@@ -28,7 +28,8 @@ reload the model.
 The Docker form installs the platform-specific diffusion extra from the source
 bundled in the image. For conditioned requests, set **Host media directory**
 under **Variables**; the builder mounts it read-only at `/data/minimax-h3`.
-AMD currently offers the Python form, while NVIDIA also offers Docker.
+AMD and Grace Blackwell currently offer the Python form; the other NVIDIA
+recipes also offer Docker.
 
 To use ModelScope through the same normal `sglang serve` path, prefix the copied
 command with `SGLANG_USE_MODELSCOPE=true` and replace the model path with
@@ -72,6 +73,29 @@ The builder accepts legal custom GPU counts and topologies, marking them
 Static H3 head or partition violations disable Copy before they reach
 `sglang serve`.
 
+### Grace Blackwell
+
+Select **GB300** or **GB200** in the builder. The default is one Linux ARM64
+host with four GPUs, Ulysses4, and resident weights. This follows the
+[four-GPU compute-tray layout](https://docs.nvidia.com/dgx/dgxgb200-user-guide/hardware.html);
+an NVL72 rack is not a single 72-GPU process host.
+
+For two four-GPU hosts, set **Nodes = 2** and **GPUs / node = 4**. The builder
+emits Ulysses4 inside each host, Ring2 across hosts, and replicated encoders.
+Set the same reachable **Head address** on both hosts and use node ranks 0 and
+1. Run the generated command once per host, with the same SGLang revision and
+dependencies. Let `sglang serve` resolve and download the selected weights.
+
+GB200 is a **derived, unverified recipe**, not a measured GB300-equivalent
+performance claim. Keep the native precision and platform-default attention
+for the baseline; the B200/B300 quantization measurements and H200 Cache-DiT
+quality audit do not establish coverage on Grace Blackwell.
+
+GB300 end-to-end coverage currently includes single-host FL2VA text-to-video
+with audio, native precision, eager execution, and `quality="lossless"`.
+Ref2VA and multi-host commands remain **Unverified**. See the
+[GB300 measurements](#gb300-single-host) for the tested workload.
+
 ### Checkpoint and adapter formats
 
 Start with the command emitted by the builder. Every row below is an overlay on
@@ -1181,6 +1205,8 @@ the configurations with collected measurements:
 | --- | --- | --- |
 | B300 | 8× Ulysses8 resident | 8× FSDP + Ulysses8; the 8-GPU sweep is not a minimum-GPU claim. |
 | B200 | 8× Ulysses8 resident | 4× FSDP + Ulysses4 |
+| GB300 | 4× Ulysses4 resident (FL2VA T2VA) | 2 nodes × 4× Ulysses4×Ring2 is unverified. |
+| GB200 | 4× Ulysses4 resident, derived/unverified | No performance measurements. |
 | H200 | 4× Ulysses4 resident | 4× FSDP + Ulysses4; 4× TP2 + Ulysses2; 2 nodes × 8× Ulysses8×Ring2 cross-node |
 | H100 | 4× TP2 + Ulysses2 resident | 4× TP4 + Ulysses1; 4× FSDP + Ulysses4 |
 | Ascend NPU | 8 NPUs, TP2 + SP4, Laser Attention | 4 NPUs, TP2 + SP2, Laser Attention |
@@ -1188,6 +1214,33 @@ the configurations with collected measurements:
 | RTX 5090 | 2× TP2 + layerwise offload | — |
 | RTX 4090 24 GB | 1× layerwise offload + `kitchen_int8` | Approximate attention backends are opt-in |
 
+### GB300 single-host
+
+Four requests on **1 host × 4 NVIDIA GB300**, using Linux ARM64, SGLang main
+[`15d2cbcc90fc`](https://github.com/sgl-project/sglang/commit/15d2cbcc90fc66b4d08745994cc707a45b067ffc),
+PyTorch 2.13.0+cu130, and NCCL 2.29.7. The source checkout was installed with
+`python3 -m pip install -e "python[diffusion]" --upgrade` inside
+`lmsysorg/sglang:latest`; this is a source-validation environment, not a claim
+that the image's bundled release contains that revision.
+
+The command matches the GB300 default in the builder: Ulysses4, resident
+weights, automatic encoder folding, native BF16/FP32, platform-default
+Dynamic cuDNN/FA attention, and eager execution. The model was downloaded by
+`sglang serve` from `MiniMaxAI/MiniMax-H3`.
+
+The workload uses the builder's cat-band prompt, FL2VA T2VA, a 5-second request
+at a 768-pixel short edge, 50 inference steps, `quality="lossless"`, one output,
+and seed 1101. Every MP4 contained video and audio and passed a full decode check.
+
+| First full request after server warmup | Following three requests | Warm median |
+| ---: | --- | ---: |
+| 35.16 s | 33.11 / 33.10 / 33.10 s | **33.10 s** |
+
+These are client-observed completion times, including up to one second of
+status-polling delay. Download, model loading, and server warmup are excluded.
+This is a deployment smoke/latency measurement, not a cross-framework quality
+audit, a minimum-GPU claim, or an extrapolation to GB200/NVL72 scaling.
+
 ### Ascend NPU topology comparison
 
 Both topologies used Laser Attention and the explicit Cache-DiT configuration
diff --git a/docs/scripts/check_cookbook_configs.mjs b/docs/scripts/check_cookbook_configs.mjs
index ec77b6438..9e661b138 100755
--- a/docs/scripts/check_cookbook_configs.mjs
+++ b/docs/scripts/check_cookbook_configs.mjs
@@ -300,6 +300,33 @@ for (const path of walk(CONFIGS)) {
       checkH3("B200 1x8", { hw: "b200", nodes: 1, gpus_per_node: 8, placement: "resident" }, { tp_size: 1, ulysses_degree: 8, ring_degree: 1 });
       checkH3("H100 1x4", { hw: "h100", nodes: 1, gpus_per_node: 4, placement: "resident" }, { tp_size: 2, ulysses_degree: 2, ring_degree: 1 });
       checkH3("H200 2x8", { hw: "h200", nodes: 2, gpus_per_node: 8, placement: "resident" }, { tp_size: 1, ulysses_degree: 8, ring_degree: 2 });
+      for (const hw of ["gb200", "gb300"]) {
+        checkH3(`${hw} 1x4`, { hw, nodes: 1, gpus_per_node: 4, placement: "resident" }, { tp_size: 1, ulysses_degree: 4, ring_degree: 1 }, hw === "gb300");
+        checkH3(`${hw} 2x4`, { hw, nodes: 2, gpus_per_node: 4, placement: "resident" }, { tp_size: 1, ulysses_degree: 4, ring_degree: 2 }, false);
+        const selection = selectionOf({ hw, nodes: 2, gpus_per_node: 4 });
+        const encoder = config.overlayDims.find((dim) => dim.id === "encoder").options.find((option) => option.id === "auto");
+        if (!encoder.flags(selection).includes("--encoder-parallel replicate")) {
+          fail(where, `${hw} cross-node auto encoder must replicate`);
+        }
+        if (config.runModes(selection).includes("docker")) {
+          fail(where, `${hw} must not advertise an unvalidated Docker command`);
+        }
+      }
+      for (const extra of [
+        { hw: "gb200" },
+        { hw: "gb300", mode: "i2va" },
+        { hw: "gb300", weights: "ref2va", mode: "v2v" },
+        { hw: "gb300", quality: "extra-high" },
+        { hw: "gb300", outputs: "2" },
+        { hw: "gb300", precision: "fp8" },
+        { hw: "gb300", attention: "sage" },
+      ]) {
+        const selection = selectionOf({ nodes: 1, gpus_per_node: 4, placement: "resident", ...extra });
+        const resolved = validateResolved(selection, "H3 Grace Blackwell coverage");
+        if (resolved?.builder.verification?.request !== "unverified") {
+          fail(where, `H3 Grace Blackwell request is outside the measured scope: ${JSON.stringify(extra)}`);
+        }
+      }
       for (const hw of ["mi300x", "mi355x"]) {
         for (const count of [1, 2, 4, 8]) {
           checkH3(`${hw} 1x${count}`, { hw, nodes: 1, gpus_per_node: count, placement: "resident" }, { tp_size: 1, ulysses_degree: count, ring_degree: 1 });
diff --git a/docs/src/snippets/configs/MiniMaxAI/minimax-h3.jsx b/docs/src/snippets/configs/MiniMaxAI/minimax-h3.jsx
index 59f1268eb..c77b3aa49 100644
--- a/docs/src/snippets/configs/MiniMaxAI/minimax-h3.jsx
+++ b/docs/src/snippets/configs/MiniMaxAI/minimax-h3.jsx
@@ -147,6 +147,8 @@ return {
   supportedHardware: [
     "b200",
     "b300",
+    "gb300",
+    "gb200",
     "h200",
     "h100",
     "mi300x",
@@ -550,6 +552,8 @@ return {
         { id: "b200-fsdp-4", hw: "b200", nodes: 1, gpus_per_node: 4, placement: "fsdp", tp_size: 1, ulysses_degree: 4, ring_degree: 1, encoder: "auto" },
         { id: "b300-resident-8", hw: "b300", nodes: 1, gpus_per_node: 8, placement: "resident", tp_size: 1, ulysses_degree: 8, ring_degree: 1, encoder: "auto", default: true },
         { id: "b300-fsdp-8", hw: "b300", nodes: 1, gpus_per_node: 8, placement: "fsdp", tp_size: 1, ulysses_degree: 8, ring_degree: 1, encoder: "auto" },
+        { id: "gb300-resident-4", hw: "gb300", nodes: 1, gpus_per_node: 4, placement: "resident", tp_size: 1, ulysses_degree: 4, ring_degree: 1, encoder: "auto", default: true },
+        { id: "gb200-resident-4", hw: "gb200", nodes: 1, gpus_per_node: 4, placement: "resident", tp_size: 1, ulysses_degree: 4, ring_degree: 1, encoder: "auto", default: true, unverified: true },
         { id: "h200-resident-4", hw: "h200", nodes: 1, gpus_per_node: 4, placement: "resident", tp_size: 1, ulysses_degree: 4, ring_degree: 1, encoder: "auto", default: true },
         { id: "h200-fsdp-4", hw: "h200", nodes: 1, gpus_per_node: 4, placement: "fsdp", tp_size: 1, ulysses_degree: 4, ring_degree: 1, encoder: "auto" },
         { id: "h200-cross-node-16", hw: "h200", nodes: 2, gpus_per_node: 8, placement: "resident", tp_size: 1, ulysses_degree: 8, ring_degree: 2, encoder: "replicate" },
@@ -673,9 +677,12 @@ return {
         || (s.precision === "fp8" && ["b200", "b300"].includes(s.hw));
       const executionVerified = s.execution === "eager"
         || (s.execution === "bcg" && ["b200", "h200"].includes(s.hw) && s.weights === "ref2va");
+      const checkpointVerified = s.hw !== "gb300" || s.weights === "fl2va";
       const serveVerified = topologyVerified && encoderVerified && attentionVerified
-        && precisionVerified && executionVerified;
-      const requestVerified = topologyVerified && (["lossless", "extra-high"].includes(s.quality)
+        && precisionVerified && executionVerified && checkpointVerified;
+      const requestCovered = s.hw !== "gb300" || (serveVerified && s.weights === "fl2va"
+        && s.mode === "t2va" && s.quality === "lossless" && Number(s.outputs) === 1);
+      const requestVerified = topologyVerified && requestCovered && (["lossless", "extra-high"].includes(s.quality)
         || (s.quality === "high" && highAudited && s.execution === "eager"));
 
       const topologyParts = [];
@@ -712,7 +719,7 @@ return {
       let automaticAttention = "FlashAttention (auto)";
       if (["mi300x", "mi355x"].includes(s.hw)) {
         automaticAttention = "AITER (auto)";
-      } else if (topology.ring_degree === 1 && ["b200", "b300"].includes(s.hw)) {
+      } else if (topology.ring_degree === 1 && ["b200", "b300", "gb200", "gb300"].includes(s.hw)) {
         automaticAttention = "Dynamic cuDNN / FA (auto)";
       } else if (topology.ring_degree === 1 && ["rtx5090", "rtx4090"].includes(s.hw)) {
         automaticAttention = "Torch SDPA (auto)";
@@ -964,9 +971,9 @@ return {
       ? `bash -lc 'python -m pip install -e "/sgl-workspace/sglang/python[diffusion_hip]" && exec sglang serve "$@"' --`
       : `bash -lc 'python -m pip install -e "/sgl-workspace/sglang/python[diffusion]" && exec sglang serve "$@"' --`,
 
-  // Publish AMD Docker only after an H3-capable ROCm image has been validated.
+  // Publish Docker only after the platform's H3 image/command has been validated.
   runModes: (s) =>
-    ["mi300x", "mi355x"].includes(s.hw)
+    ["mi300x", "mi355x", "gb200", "gb300"].includes(s.hw)
       ? ["python"]
       : ["python", "docker"],