ARM CPUs that do not support AVX512 (NVIDIA GH200)?

### Reminder

- [x] I have read the above rules and searched the existing issues.

### System Info

KTransformers: https://github.com/kvcache-ai/ktransformers/commit/7a4b9b0e87467ef1a23c5dd1690d92ca30ca3e26
SGLang: v0.5.6.post1
Platform: Kubernetes (Ubuntu 24.04 Server)
Python Version: 3.12
CPU: NVIDIA GH200 Superchip
GPU: NVIDIA GH200 GPU

### Reproduction

```text
[2026-03-11 11:42:47] Generated KT GPU experts masks using 'uniform' strategy: 89 MoE layers (out of 92 total layers) x 160 experts, total GPU experts in MoE layers = 0
[2026-03-11 11:42:47] Using CompressedTensorsW8A8Fp8MoE
[2026-03-11 11:42:47[] [KT[] Created shared staging buffer: 80.0 MiB (shape=torch.Size([8192, 5120]), dtype=torch.bfloat16)
[2026-03-11 11:42:47] Scheduler hit an exception: Traceback (most recent call last):
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/scheduler.py", line 3118, in run_scheduler_process
scheduler = Scheduler(
^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/scheduler.py", line 363, in __init__
self.init_model_worker()
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/scheduler.py", line 559, in init_model_worker
self.init_tp_model_worker()
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/scheduler.py", line 517, in init_tp_model_worker
self.tp_worker = TpModelWorker(
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/tp_worker.py", line 247, in __init__
self._init_model_runner()
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/managers/tp_worker.py", line 330, in _init_model_runner
self._model_runner = ModelRunner(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/model_executor/model_runner.py", line 415, in __init__
self.initialize(min_per_gpu_memory)
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/model_executor/model_runner.py", line 495, in initialize
self.load_model()
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/model_executor/model_runner.py", line 970, in load_model
self.model = self.loader.load_model(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/model_loader/loader.py", line 671, in load_model
model = _initialize_model(
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/model_loader/loader.py", line 277, in _initialize_model
return model_class(**kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/models/glm4_moe.py", line 1031, in __init__
self.model = Glm4MoeModel(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/models/glm4_moe.py", line 920, in __init__
self.layers, self.start_layer, self.end_layer = make_layers(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/utils/common.py", line 648, in make_layers
+ get_offloader().wrap_modules(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/utils/offloader.py", line 36, in wrap_modules
return list(all_modules_generator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/utils/common.py", line 650, in
layer_fn(idx=idx, prefix=add_prefix(idx, prefix))
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/models/glm4_moe.py", line 922, in
lambda idx, prefix: Glm4MoeDecoderLayer(
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/models/glm4_moe.py", line 741, in __init__
self.mlp = Glm4MoeSparseMoeBlock(
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/models/glm4_moe.py", line 375, in __init__
self.experts = get_moe_impl_class(quant_config)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/layers/moe/fused_moe_triton/layer.py", line 283, in __init__
self.quant_method.create_weights(
File "/usr/local/lib/python3.12/dist-packages/sglang/srt/layers/moe/kt_ep_wrapper.py", line 2095, in create_weights
self.wrapper = KTMoEWrapper(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/kt_kernel/experts.py", line 106, in __new__
return backend_cls(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/kt_kernel/utils/amx.py", line 350, in __init__
raise RuntimeError(
RuntimeError: FP8 backend not available. Required ISA:
- AVX512F + AVX512BW + AVX512_BF16 + AVX512_VBMI
Please recompile kt_kernel_ext with AVX512 + BF16 + VBMI enabled.

[2026-03-11 11:42:47] Received sigquit from a child process. It usually means the child failed.
```

### Others

Are we able to work around `AVX512 + BF16 + VBMI`, which is not present on ARM Neoverse?

The GH200 has a high-bandwidth CPU RAM - VRAM interconnect, which makes it the perfect choice for leveraging KTransformers.

Start by inspecting kt_kernel/utils/amx.py and kt_kernel/experts.py, especially the FP8 backend initialization shown in the traceback. Reproduce the model-loading failure on an ARM Neoverse or GH200 environment and trace how kt_kernel_ext is built. Done means the relevant FP8 path can load on the reported ARM platform without requiring unavailable AVX512 instructions, with the supported behavior documented or tested.

Written by the indexing model from the issue text.