diff --git a/docker/Dockerfile.gb10 b/docker/Dockerfile.gb10
new file mode 100644
index 0000000000..471e2ce6f1
--- /dev/null
+++ b/docker/Dockerfile.gb10
@@ -0,0 +1,208 @@
+# Dockerfile for NVIDIA DGX Spark (GB10 / sm_121a, aarch64).
+#
+# slime's stock Dockerfile targets x86_64 + H100/B200/GB200 and does not work
+# on GB10 (ptxas in CUDA 12.9 lacks sm_121a; published sgl_kernel wheels have
+# no sm_121 variant and ABI-mismatch against NGC libtorch).
+#
+# This Dockerfile rebases on the NGC vLLM container which ships CUDA 13.2,
+# PyTorch 2.11.0a0 (compiled with compute_120 for Blackwell PTX forward-compat),
+# Triton 3.6.0, and flash-attn 2.7.4.post1 — all verified working on GB10.
+#
+# Digest-pinned for reproducibility. Update the digest together with the tag.
+# Companion notes: NOTES_GB10.md (15 resolved blockers documented).
+
+ARG BASE_IMAGE=nvcr.io/nvidia/vllm:26.03-py3
+ARG BASE_DIGEST=sha256:13e327dad79e6e417f6687fec2ba76b0386d597082ec0ee003c1e964ec6ad0e7
+FROM ${BASE_IMAGE}@${BASE_DIGEST}
+
+# =============================================================================
+# Common env: GB10 reports cc 12.1 but NGC torch only lists compute_120. PTX
+# forward-compat carries sm_120 PTX → sm_121 at runtime. Set the same for all
+# downstream compilation layers.
+# =============================================================================
+ENV TORCH_CUDA_ARCH_LIST="12.0+PTX"
+
+# =============================================================================
+# System packages missing from the NGC base but needed downstream:
+#  - libz3-dev : tilelang's Z3 SMT autoscheduler dlopens libz3.so
+# =============================================================================
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libz3-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# =============================================================================
+# Header shims for CUDA 13 / NVTX removal
+# =============================================================================
+# (1) NVTX3: CUDA 13 dropped bundled NVTX headers; the pypi placeholder is empty.
+# Clone the upstream NVIDIA/NVTX repo and install just the C include tree.
+RUN git clone --depth 1 https://github.com/NVIDIA/NVTX.git /tmp/nvtx_src \
+    && cp -r /tmp/nvtx_src/c/include/nvtx3 /usr/local/cuda/include/nvtx3 \
+    && rm -rf /tmp/nvtx_src
+
+# (2) cuda_profiler_api.h: CUDA 13 removed the public header; libcudart.so.13
+# still exports the two symbols. TE 2.10 only #includes the header without
+# actually calling the APIs in the affected TUs, so a tiny shim is sufficient.
+COPY docker/patch/gb10/cuda_profiler_api.h /usr/local/cuda/include/cuda_profiler_api.h
+
+# =============================================================================
+# cuDNN engines_precompiled library
+# =============================================================================
+# NGC deliberately ships only runtime-compiled cuDNN libs to save space, but
+# TE's bundled cudnn-frontend requires CUDNN::cudnn_engines_precompiled. The
+# pypi wheel nvidia-cudnn-cu13 9.20.0.48 contains the missing .so.
+RUN PIP_CONSTRAINT= pip install --no-deps nvidia-cudnn-cu13==9.20.0.48 \
+    && ln -sf /usr/local/lib/python3.12/dist-packages/nvidia/cudnn/lib/libcudnn_engines_precompiled.so.9 \
+              /usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9 \
+    && ln -sf /usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9 \
+              /usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so
+
+# =============================================================================
+# CMake 4.3.1: needed for CUDA 13 family-specific `f` arch suffix (120f/121f).
+# NGC's /etc/pip/constraint.txt pins cmake==3.31.6; we must clear PIP_CONSTRAINT
+# for this install. CMAKE_POLICY_VERSION_MINIMUM=3.5 lets CMake 4.x accept
+# bundled third-party CMakeLists that declare cmake_minimum_required < 3.5.
+# =============================================================================
+RUN PIP_CONSTRAINT= pip install --no-deps --ignore-installed --force-reinstall cmake==4.3.1
+ENV CMAKE_POLICY_VERSION_MINIMUM=3.5
+
+# =============================================================================
+# Build prerequisites for the source installs below
+# =============================================================================
+RUN pip install --no-deps pybind11 pathspec pyproject_metadata scikit-build-core
+
+# =============================================================================
+# sgl-kernel for sm_121a (SGLang's CUDA kernel library)
+# =============================================================================
+# The published sgl_kernel wheels are cu12x (libnvrtc.so.12 ABI), plus the cu130
+# arm64 wheel only has the sm_100 variant, not sm_121. Also ABI-mismatch against
+# NGC torch. Build from source with the GB10 arch whitelist patch.
+#
+# Patch: docker/patch/gb10/patch_sgl_kernel.py adds SGL_KERNEL_GB10_ONLY CMake
+# option that restricts gencode to sm_120a + sm_121a (skipping sm_90a/sm_100a/
+# sm_103a/sm_110a). This is essential — compiling all seven archs simultaneously
+# OOM-kills cicc on 128 GB Spark hosts.
+ARG SGLANG_COMMIT=v0.5.9
+RUN git clone --depth 1 --branch ${SGLANG_COMMIT} https://github.com/sgl-project/sglang.git /root/src/sglang
+COPY docker/patch/gb10/patch_sgl_kernel.py /tmp/patch_sgl_kernel.py
+RUN python3 /tmp/patch_sgl_kernel.py /root/src/sglang/sgl-kernel/CMakeLists.txt \
+    && rm /tmp/patch_sgl_kernel.py
+
+# Conservative parallelism (8 parallel × 2 nvcc-internal threads = 16 active)
+# keeps peak memory ~25 GB on the 128 GB Spark. ~25 minutes on 20-core GB10.
+RUN cd /root/src/sglang/sgl-kernel \
+    && CMAKE_BUILD_PARALLEL_LEVEL=8 \
+       CMAKE_ARGS="-DSGL_KERNEL_COMPILE_THREADS=2 -DSGL_KERNEL_GB10_ONLY=ON" \
+       pip wheel . --no-build-isolation --no-deps -w /tmp/wheels \
+    && pip install --no-deps /tmp/wheels/sgl_kernel-*.whl \
+    && rm -rf /tmp/wheels /root/.cache/pip
+
+# =============================================================================
+# SGLang runtime + the small pure-python deps it needs.
+# --no-deps preserves NGC torch / transformers / flashinfer / quack-kernels /
+# cutlass-dsl / xgrammar / outlines that were carefully pinned in the NGC base.
+# Pulling sglang's full dep set here would downgrade torch to 2.9.1+cu129 and
+# break everything.
+# =============================================================================
+RUN pip install --no-deps \
+        "sglang==0.5.9" \
+        "torch_memory_saver==0.0.9" \
+        IPython traitlets stack_data executing asttokens pure_eval prompt_toolkit wcwidth \
+        orjson hf_transfer modelscope litellm
+
+# SGLang router: zhuzilin's fork ships x86_64-only. Upstream 0.3.2 has an arm64
+# wheel that satisfies slime's version-parse comparisons (`<=0.2.1`, `<0.3.0`).
+# Only loss: the `'slime' in version` check in wandb_utils.py won't match,
+# which is a non-critical logging branch.
+RUN pip install --no-deps sglang-router==0.3.2
+
+# =============================================================================
+# TransformerEngine 2.10 for sm_121f
+# =============================================================================
+# GB10 needs the CUDA 13 "f" (family-specific) arch suffix — TE's ptx.cuh has
+# static_asserts that reject plain sm_120 / sm_121 in favor of sm_120f / sm_121f.
+# CMake 4 is required to pass this through CMAKE_CUDA_ARCHITECTURES.
+RUN TORCH_CUDA_ARCH_LIST="12.0+PTX" \
+    NVTE_CUDA_ARCHS="120f;121f" \
+    NVTE_FRAMEWORK=pytorch \
+    MAX_JOBS=8 CMAKE_BUILD_PARALLEL_LEVEL=8 \
+    pip install --no-build-isolation --no-deps \
+        "git+https://github.com/NVIDIA/TransformerEngine.git@release_v2.10"
+
+# TE pytorch subpackage pulls onnxscript → onnx_ir → onnx → ml_dtypes at import
+# time (not setup time). Install the runtime chain so `import transformer_engine.pytorch` works.
+RUN pip install --no-deps onnxscript onnx_ir onnx ml_dtypes
+
+# =============================================================================
+# apex (NVIDIA fused optimizers / norms)
+# =============================================================================
+# Pinned commit matches slime upstream Dockerfile.
+RUN NVCC_APPEND_FLAGS="--threads 4" MAX_JOBS=8 \
+    pip install --no-cache-dir --no-build-isolation --no-deps \
+        --config-settings "--build-option=--cpp_ext --cuda_ext --parallel 8" \
+        "git+https://github.com/NVIDIA/apex.git@10417aceddd7d5d05d7cbf7b0fc2daad1105f8b4"
+
+# =============================================================================
+# Megatron-LM (+ slime's megatron.patch)
+# =============================================================================
+# slime pins Megatron commit 3714d81d...
+ARG MEGATRON_COMMIT=3714d81d418c9f1bca4594fc35f9e8289f652862
+RUN git clone https://github.com/NVIDIA/Megatron-LM.git /root/src/Megatron-LM \
+    && cd /root/src/Megatron-LM \
+    && git checkout ${MEGATRON_COMMIT}
+COPY docker/patch/latest/megatron.patch /tmp/megatron.patch
+RUN cd /root/src/Megatron-LM \
+    && git apply /tmp/megatron.patch --3way \
+    && ! grep -R -n '^<<<<<<< ' . \
+    && rm /tmp/megatron.patch \
+    && pip install --no-deps --no-build-isolation -e .
+
+# Megatron.training / .rl / .legacy are sibling dirs meant to be on PYTHONPATH
+# (slime's own docs confirm this).
+ENV PYTHONPATH="/root/src/Megatron-LM"
+
+# =============================================================================
+# Remaining slime deps: bridges, modelopt, FLA, tilelang, antlr, omegaconf chain
+# =============================================================================
+RUN pip install --no-deps \
+        "git+https://github.com/ISEEKYAN/mbridge.git@89eb10887887bc74853f89a4de258c0702932a1c" \
+        flash-linear-attention==0.4.1 \
+        tilelang==0.1.8
+
+RUN pip install --no-deps --no-build-isolation \
+        "git+https://github.com/fzyzcjy/Megatron-Bridge.git@dev_rl" \
+        "nvidia-modelopt>=0.37.0"
+
+# antlr4-python3-runtime must be pinned to 4.9.3 — omegaconf's bundled grammar
+# was generated with antlr 4.9 serialized format and runtime 4.13 breaks on it
+# with "Could not deserialize ATN with version 3 (expected 4)".
+RUN pip install --no-deps "antlr4-python3-runtime==4.9.3" omegaconf wandb
+
+# Megatron requires numpy 1.x; NGC ships 2.x.
+RUN PIP_CONSTRAINT= pip install --no-deps --force-reinstall "numpy<2"
+
+# Reward function runtime deps (grade_answer_verl / deepscaler math verifier)
+RUN pip install --no-deps pylatexenc math_verify word2number
+
+# Eval / data-prep runtime deps
+RUN pip install --no-deps pyarrow accelerate
+
+# =============================================================================
+# slime itself
+# =============================================================================
+COPY . /root/slime
+RUN cd /root/slime \
+    && pip install --no-deps --no-build-isolation -e .
+
+# slime's int4 QAT kernel (fused per-channel fake-quant + dequant)
+RUN cd /root/slime/slime/backends/megatron_utils/kernels/int4_qat \
+    && pip install --no-build-isolation --no-deps .
+
+WORKDIR /root/slime
+
+# Sanity: load the full arg parser on container build
+RUN python train.py --help > /tmp/help.txt \
+    && test $(wc -l < /tmp/help.txt) -gt 3000 \
+    && echo "slime train.py --help OK ($(wc -l < /tmp/help.txt) lines)" \
+    && rm /tmp/help.txt
+
+CMD ["/bin/bash"]
diff --git a/docker/NOTES_GB10.md b/docker/NOTES_GB10.md
new file mode 100644
index 0000000000..d5417321f5
--- /dev/null
+++ b/docker/NOTES_GB10.md
@@ -0,0 +1,99 @@
+# slime on NVIDIA DGX Spark (GB10) — Porting Notes
+
+Target platform:
+- Chip: NVIDIA GB10 (Grace + consumer Blackwell, SM 12.1 / sm_121a)
+- Arch: aarch64
+- OS: Ubuntu 24.04, NVIDIA driver 580.142 (CUDA 13.x forward-compat)
+- Unified memory: 128 GB (CPU+GPU shared)
+
+## Why a new Dockerfile is needed
+
+slime's published Docker images (`slimerl/slime:*`) are x86_64-only. The arm64 base
+it derives from (`slimerl/sglang:v0.5.9`) ships with CUDA 12.9 and stock PyTorch
+2.9.1+cu129 — neither of which knows about the `sm_121a` target used by GB10:
+the Triton PTX pipeline crashes with `ptxas fatal: Value 'sm_121a' is not defined`.
+
+The `ENABLE_CUDA_13=1` branch in the upstream Dockerfile is aimed at GB200/GB300
+(sm_100a) and still uses an x86-only router wheel and amd64 base, so it does not
+directly apply to GB10.
+
+This port rebases slime on `nvcr.io/nvidia/vllm:26.03-py3` (arm64), which ships:
+- CUDA 13.2 (ptxas understands sm_121a ✅)
+- PyTorch 2.11.0a0 compiled with `compute_120` (PTX forward-compat to sm_121 ✅)
+- Triton 3.6.0 (verified: Triton kernel JITs on GB10 ✅)
+- flash-attn 2.7.4.post1 preinstalled
+
+### Base image pinning (for reproducibility)
+
+Pull from NGC and verify digest:
+
+```bash
+docker pull nvcr.io/nvidia/vllm:26.03-py3
+docker inspect nvcr.io/nvidia/vllm:26.03-py3 --format '{{range .RepoDigests}}{{.}}{{end}}'
+# Expected digest:
+#   nvcr.io/nvidia/vllm@sha256:13e327dad79e6e417f6687fec2ba76b0386d597082ec0ee003c1e964ec6ad0e7
+```
+
+All downstream steps pin this digest. Product page:
+https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=26.03-py3
+
+## Known blockers and their resolutions
+
+| # | Blocker | Root cause | Resolution |
+|---|---------|-----------|------------|
+| 1 | `ptxas fatal: sm_121a` | CUDA 12.9 ptxas predates sm_121a (added in CUDA 13.0) | Rebase on NGC CUDA 13.2 image |
+| 2 | `libnvrtc.so.12` missing from sgl_kernel wheel | Published sgl_kernel wheel is cu12x | Use cu130 wheel OR build from source |
+| 3 | `sgl_kernel/sm100/...abi3.so: undefined symbol _ZN3c104cuda29c10_cuda_check_implementationEiPKcS2_ib` | Wheel built against stock libtorch, NGC libtorch has different C++ ABI | Build sgl_kernel from source against NGC torch |
+| 4 | sgl_kernel arm64 wheels only build sm_100 variant | sgl-project CI doesn't target GB10 | Build sm_121 variant from source |
+| 5 | sgl-kernel source build: cicc OOM on sm_90+100+103+110+120+121 concurrent compile | Cutlass FP8 templates × 7 arches × 12 parallel → >128GB | `SGL_KERNEL_GB10_ONLY=ON` CMake option (see `docker/patch/gb10/sgl-kernel-arch.patch`), drops to sm_120a+121a |
+| 6 | TE 2.10 build: `CUDNN::cudnn_engines_precompiled` target not found | NGC vLLM image ships only runtime-compiled cuDNN libs | Install `nvidia-cudnn-cu13==9.20.0.48` pypi wheel (has the precompiled engines), symlink into `/usr/lib/aarch64-linux-gnu/` |
+| 7 | TE 2.10 build: `nvtx3/nvToolsExt.h: No such file` | CUDA 13 dropped bundled NVTX headers; `nvidia-nvtx-cu13` pypi wheel is an empty 0.0.1 placeholder | Clone `NVIDIA/NVTX` github, copy `c/include/nvtx3/*` to `/usr/local/cuda/include/nvtx3/` |
+| 8 | TE 2.10 build: `ptx.cuh` static_assert "Compile for smXXXf instead of smXXX" | Blackwell family-specific TMA features require CUDA 13's new `f` (family-specific) arch suffix, not plain integer | Set `NVTE_CUDA_ARCHS="120f;121f"` — but CMake ≤3.31 rejects `f` suffix |
+| 9 | CMake 3.31 rejects `CMAKE_CUDA_ARCHITECTURES=120f;121f` | `f` suffix for CUDA 13 Blackwell family is only supported in CMake ≥4.0 | Upgrade to `cmake==4.3.1` via pip (must override NGC `/etc/pip/constraint.txt` with `PIP_CONSTRAINT=`) and set `CMAKE_POLICY_VERSION_MINIMUM=3.5` for old bundled deps |
+| 10 | TE 2.10 build: `cuda_profiler_api.h: No such file` | CUDA 13 removed the public header for `cudaProfilerStart/Stop`; the symbols still exist in `libcudart.so.13`. TE's 3 `fused_softmax` TUs `#include` the header but don't call the APIs | Install a 20-line shim header at `/usr/local/cuda/include/cuda_profiler_api.h` declaring the two functions extern. Stored as `docker/patch/gb10/cuda_profiler_api.h` |
+| 11 | slime `train.py --help`: `'tuple' object has no attribute 'strip'` | Typo in `slime/utils/arguments.py:1073`: `help=("string",)` (trailing comma → tuple) instead of `help=("string")` | Remove trailing comma — simple one-line slime fix, upstream-able |
+| 12 | `sglang_router` x86_64-only wheel from `zhuzilin/sgl-router` fork | slime Dockerfile pins `zhuzilin/sgl-router` release (no arm64 builds); slime's `'slime' in version` assertion is only in `wandb_utils.py` (non-critical path) | Install upstream `sglang-router==0.3.2` from PyPI (has arm64 wheel). Accept wandb path fallback |
+| 13 | `antlr4-python3-runtime==4.13.2` → `Could not deserialize ATN with version 3` | Omegaconf's bundled grammar was generated with antlr 4.9 serialized format; runtime 4.13 only reads format v4 | Pin `antlr4-python3-runtime==4.9.3` |
+| 14 | `megatron.training` not importable after `pip install -e Megatron-LM` | Megatron-LM's setup.py only packages `megatron-core`; `megatron.training`, `megatron.rl`, `megatron.legacy` are sibling dirs meant to be on `PYTHONPATH` | `export PYTHONPATH=/root/src/Megatron-LM:$PYTHONPATH` (slime docs confirm this) |
+| 15 | `libz3.so` missing for tilelang | tilelang uses Z3 SMT solver for autoscheduling; NGC vllm base doesn't include libz3 | `apt-get update && apt-get install -y libz3-dev` (libz3-4 package alias needs update first) |
+
+## Build journal
+
+### sgl-kernel OOM during build → arch whitelist patch
+
+First build attempt on GB10 with stock sgl-kernel v0.5.9 CMake: cicc
+(NVIDIA's CUDA frontend compiler) was OOM-killed while compiling
+`csrc/gemm/fp8_gemm_kernel.cu`, `fp8_blockwise_gemm_kernel.cu`, and
+`nvfp4_scaled_mm_kernels.cu`.
+
+Root cause: for `CUDA_VERSION >= 13.0 && aarch64`, sgl-kernel unconditionally
+emits seven gencodes per TU — `sm_90, sm_90a, sm_100a, sm_103a, sm_110a,
+sm_120a, sm_121a`. Cutlass FP8/NVFP4 gemm template instantiation uses
+~10–15 GB of RAM per TU per arch. Combined with 12 parallel nvcc jobs, peak
+memory exceeded the 128 GB unified memory limit.
+
+Only `sm_121a` (and `sm_120a` as PTX fallback) actually runs on GB10. Hopper
+(sm_90a) and datacenter Blackwell (sm_100a/sm_103a) binaries are dead weight.
+
+Fix: `docker/patch/gb10/sgl-kernel-arch.patch` adds a CMake option
+`SGL_KERNEL_GB10_ONLY`. When set, the other gencodes and FA3 (sm_90a-only)
+are skipped. Default OFF preserves upstream behavior. See the patch python
+script `docker/patch/gb10/patch_sgl_kernel.py` for a programmatic apply.
+
+### sgl-kernel build success (M1)
+
+With `SGL_KERNEL_GB10_ONLY=ON`, `CMAKE_BUILD_PARALLEL_LEVEL=8`, and
+`SGL_KERNEL_COMPILE_THREADS=2`, the build completed in 24 minutes with RAM
+never exceeding ~25 GB. Produced `sgl_kernel-0.3.21-cp310-abi3-linux_aarch64.whl`
+(74 MB), ABI-compatible with NGC libtorch 2.11.0a0 (verified via clean import
+and functional rmsnorm kernel on GB10).
+
+## Work items
+
+- [x] M1: Build sgl_kernel from source for sm_121 + NGC torch ABI
+- [x] M2: TransformerEngine 2.10 (sm_121f), apex, Megatron-LM all built + verified
+- [x] M3: slime installed with megatron.patch applied; `train.py --help` prints 3714-line arg list
+- [x] M4: End-to-end RL loop (Qwen2.5-0.5B + dapo-math-17k + GRPO colocated on 1 GB10 GPU) runs clean in 2m10s
+
+Reproducible image: `slime-gb10:m4-success` (36.3 GB, committed post-smoke)
+- Step 0 train metrics logged; full rollout → reward → policy-update → weight-sync cycle confirmed.
diff --git a/docker/patch/gb10/cuda_profiler_api.h b/docker/patch/gb10/cuda_profiler_api.h
new file mode 100644
index 0000000000..276b80b656
--- /dev/null
+++ b/docker/patch/gb10/cuda_profiler_api.h
@@ -0,0 +1,26 @@
+#ifndef __CUDA_PROFILER_API_H__
+#define __CUDA_PROFILER_API_H__
+
+/* CUDA 13 dropped the public cuda_profiler_api.h header, but the runtime still
+ * exports cudaProfilerStart / cudaProfilerStop from libcudart.so. This shim
+ * declares them against the public types so legacy callers (e.g., TE 2.10,
+ * which only includes the header without actually calling the APIs in the
+ * affected TUs) continue to compile.
+ *
+ * Installed by slime GB10 port. See docker/patch/gb10/ and NOTES_GB10.md.
+ */
+
+#include <cuda_runtime_api.h>
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+extern __host__ cudaError_t CUDARTAPI cudaProfilerStart(void);
+extern __host__ cudaError_t CUDARTAPI cudaProfilerStop(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* __CUDA_PROFILER_API_H__ */
diff --git a/docker/patch/gb10/patch_sgl_kernel.py b/docker/patch/gb10/patch_sgl_kernel.py
new file mode 100644
index 0000000000..75454c906e
--- /dev/null
+++ b/docker/patch/gb10/patch_sgl_kernel.py
@@ -0,0 +1,51 @@
+"""Patch sgl-kernel CMakeLists.txt to add a SGL_KERNEL_GB10_ONLY build option.
+
+On GB10 (DGX Spark, sm_121a aarch64 with unified 128 GB memory), the stock
+sgl-kernel CMake emits gencodes for sm_90a + sm_100a + sm_103a + sm_110a +
+sm_120a + sm_121a. Cutlass FP8 gemm template instantiation per extra arch uses
+10-15 GB RAM, and 12 parallel nvcc jobs OOM-kill cicc.
+
+This patch wraps the non-GB10 gencode blocks in `if (NOT SGL_KERNEL_GB10_ONLY)`
+and adds a GB10-only branch that emits just sm_120a + sm_121a. Default OFF
+preserves upstream behavior.
+"""
+import sys
+from pathlib import Path
+
+p = Path(sys.argv[1] if len(sys.argv) > 1 else "/root/src/sglang/sgl-kernel/CMakeLists.txt")
+s = p.read_text()
+
+marker = 'option(SGL_KERNEL_ENABLE_SM100A           "Enable SM100A"           OFF)'
+new_opt = (
+    marker
+    + "\n"
+    + 'option(SGL_KERNEL_GB10_ONLY              "Build only for GB10 (sm_121a + sm_120a). Skips sm_90a/sm_100a/sm_103a/sm_110a and FA3." OFF)'
+)
+assert marker in s, "marker 1 (SM100A option line) not found"
+s = s.replace(marker, new_opt, 1)
+
+old = 'if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_SM100A)'
+assert old in s, "marker 2 (CUDA 12.8 block) not found"
+s = s.replace(old, "if (NOT SGL_KERNEL_GB10_ONLY)\n\n" + old, 1)
+
+old2 = 'if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_FP4)'
+assert old2 in s, "marker 3 (FP4 block) not found"
+insert_before = """endif()  # NOT SGL_KERNEL_GB10_ONLY
+
+if (SGL_KERNEL_GB10_ONLY)
+    # GB10 (DGX Spark) is sm_121a (consumer Blackwell, aarch64). It cannot run
+    # Hopper (sm_90a) nor datacenter Blackwell (sm_100a) binaries. Compiling
+    # them wastes ~6x build time and ~10-15 GB RAM per TU (cutlass templates),
+    # which OOM-kills cicc on 128 GB Spark hosts.
+    list(APPEND SGL_KERNEL_CUDA_FLAGS
+        "-gencode=arch=compute_120a,code=sm_120a"
+        "-gencode=arch=compute_121a,code=sm_121a"
+        "--compress-mode=size"
+    )
+endif()
+
+"""
+s = s.replace(old2, insert_before + old2, 1)
+
+p.write_text(s)
+print(f"edits applied to {p}")
diff --git a/docker/patch/gb10/sgl-kernel-arch.patch b/docker/patch/gb10/sgl-kernel-arch.patch
new file mode 100644
index 0000000000..a507e04c09
--- /dev/null
+++ b/docker/patch/gb10/sgl-kernel-arch.patch
@@ -0,0 +1,40 @@
+--- CMakeLists.txt.orig	2026-04-15 07:39:19.526964507 +0000
++++ CMakeLists.txt	2026-04-15 07:39:19.548828896 +0000
+@@ -188,6 +188,7 @@
+ option(SGL_KERNEL_ENABLE_FA3              "Enable FA3"              OFF)
+ option(SGL_KERNEL_ENABLE_SM90A            "Enable SM90A"            OFF)
+ option(SGL_KERNEL_ENABLE_SM100A           "Enable SM100A"           OFF)
++option(SGL_KERNEL_GB10_ONLY              "Build only for GB10 (sm_121a + sm_120a). Skips sm_90a/sm_100a/sm_103a/sm_110a and FA3." OFF)
+ 
+ if (SGL_KERNEL_ENABLE_BF16)
+     list(APPEND SGL_KERNEL_CUDA_FLAGS
+@@ -216,6 +217,8 @@
+ 
+ endif()
+ 
++if (NOT SGL_KERNEL_GB10_ONLY)
++
+ if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_SM100A)
+     list(APPEND SGL_KERNEL_CUDA_FLAGS
+         "-gencode=arch=compute_100a,code=sm_100a"
+@@ -249,6 +252,20 @@
+     )
+ endif()
+ 
++endif()  # NOT SGL_KERNEL_GB10_ONLY
++
++if (SGL_KERNEL_GB10_ONLY)
++    # GB10 (DGX Spark) is sm_121a (consumer Blackwell, aarch64). It cannot run
++    # Hopper (sm_90a) nor datacenter Blackwell (sm_100a) binaries. Compiling
++    # them wastes ~6x build time and ~10-15 GB RAM per TU (cutlass templates),
++    # which OOM-kills cicc on 128 GB Spark hosts.
++    list(APPEND SGL_KERNEL_CUDA_FLAGS
++        "-gencode=arch=compute_120a,code=sm_120a"
++        "-gencode=arch=compute_121a,code=sm_121a"
++        "--compress-mode=size"
++    )
++endif()
++
+ if ("${CUDA_VERSION}" VERSION_GREATER_EQUAL "12.8" OR SGL_KERNEL_ENABLE_FP4)
+     list(APPEND SGL_KERNEL_CUDA_FLAGS
+         "-DENABLE_NVFP4=1"
diff --git a/scripts/run-qwen2.5-0.5B-gb10-smoke.sh b/scripts/run-qwen2.5-0.5B-gb10-smoke.sh
new file mode 100755
index 0000000000..294c6836fc
--- /dev/null
+++ b/scripts/run-qwen2.5-0.5B-gb10-smoke.sh
@@ -0,0 +1,113 @@
+#!/bin/bash
+# Minimal GRPO smoke test for slime on NVIDIA DGX Spark (GB10, single GPU).
+# Goal: exercise the full rollout → reward → policy-update loop for one tiny
+# step and exit cleanly. Used only to validate the GB10 port; not a training
+# recipe.
+#
+# Prerequisites:
+#   - /root/Qwen2.5-0.5B-Instruct                    (HF checkpoint)
+#   - /root/Qwen2.5-0.5B-Instruct_torch_dist         (from tools/convert_hf_to_torch_dist.py)
+#   - /root/dapo-math-17k/dapo-math-17k.jsonl
+
+set -ex
+
+# clean any leftover ray/sglang
+pkill -9 sglang 2>/dev/null || true
+ray stop --force 2>/dev/null || true
+pkill -9 ray python 2>/dev/null || true
+sleep 2
+
+export PYTHONBUFFERED=1
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
+source "${SCRIPT_DIR}/models/qwen2.5-0.5B.sh"
+
+CKPT_ARGS=(
+   --hf-checkpoint /root/Qwen2.5-0.5B-Instruct/
+   --ref-load /root/Qwen2.5-0.5B-Instruct_torch_dist/
+   --save /tmp/slime_smoke_save/
+   --save-interval 9999
+)
+
+ROLLOUT_ARGS=(
+   --prompt-data /root/dapo-math-17k/dapo-math-17k.jsonl
+   --input-key prompt
+   --label-key label
+   --apply-chat-template
+   --rollout-shuffle
+   --rm-type deepscaler
+
+   --num-rollout 1
+   --rollout-batch-size 2
+   --n-samples-per-prompt 2
+   --num-steps-per-rollout 1
+   --global-batch-size 4
+
+   --rollout-max-response-len 256
+   --rollout-temperature 1
+)
+
+PERF_ARGS=(
+   --tensor-model-parallel-size 1
+   --pipeline-model-parallel-size 1
+   --context-parallel-size 1
+   --expert-model-parallel-size 1
+   --expert-tensor-parallel-size 1
+
+   --use-dynamic-batch-size
+   --max-tokens-per-gpu 2048
+)
+
+GRPO_ARGS=(
+   --advantage-estimator grpo
+   --use-kl-loss
+   --kl-loss-coef 0.00
+   --kl-loss-type low_var_kl
+   --entropy-coef 0.00
+   --eps-clip 0.2
+   --eps-clip-high 0.28
+)
+
+OPTIMIZER_ARGS=(
+   --optimizer adam
+   --lr 1e-6
+   --lr-decay-style constant
+   --weight-decay 0.1
+   --adam-beta1 0.9
+   --adam-beta2 0.98
+)
+
+SGLANG_ARGS=(
+   --rollout-num-gpus-per-engine 1
+   --sglang-mem-fraction-static 0.4
+)
+
+MISC_ARGS=(
+   --attention-dropout 0.0
+   --hidden-dropout 0.0
+   --accumulate-allreduce-grads-in-fp32
+   --attention-softmax-in-fp32
+   --attention-backend flash
+)
+
+ray start --head --node-ip-address 127.0.0.1 --num-gpus 1 --disable-usage-stats
+
+ray job submit --address="http://127.0.0.1:8265" \
+   --runtime-env-json='{
+     "env_vars": {
+        "PYTHONPATH": "/root/src/Megatron-LM",
+        "CUDA_DEVICE_MAX_CONNECTIONS": "1"
+     }
+   }' \
+   -- python3 train.py \
+   --actor-num-nodes 1 \
+   --actor-num-gpus-per-node 1 \
+   --colocate \
+   "${MODEL_ARGS[@]}" \
+   "${CKPT_ARGS[@]}" \
+   "${ROLLOUT_ARGS[@]}" \
+   "${OPTIMIZER_ARGS[@]}" \
+   "${GRPO_ARGS[@]}" \
+   "${PERF_ARGS[@]}" \
+   "${SGLANG_ARGS[@]}" \
+   "${MISC_ARGS[@]}"
diff --git a/slime/utils/arguments.py b/slime/utils/arguments.py
index a634d1f003..8b5d301952 100644
--- a/slime/utils/arguments.py
+++ b/slime/utils/arguments.py
@@ -1070,7 +1070,7 @@ def add_wandb_arguments(parser):
                 default=None,
                 help=(
                     "Log statistics of the category of reward, such as why the reward function considers it as failed. "
-                    "Specify the key in the reward dict using this argument.",
+                    "Specify the key in the reward dict using this argument."
                 ),
             )
             parser.add_argument(