diff --git a/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant.py b/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant.py
new file mode 100644
index 000000000..97910bcc3
--- /dev/null
+++ b/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant.py
@@ -0,0 +1,284 @@
+"""
+Selective Expert Dequantization
+===============================
+
+Instead of dequantizing all E expert weight matrices at once (which creates
+a ~1 GB transient buffer for 256 experts), only dequantize the experts that
+are actually routed to by the current batch's top-k selection.
+
+For Qwen3.5-35B-A3B (E=256, top_k=8, hidden=2048, intermediate=512):
+  - Full dequant: [256, 2048, 1024] = 1,074 MB per projection
+  - Selective (8 active): [8, 2048, 1024] = 33.5 MB per projection
+  - Savings: ~97% memory reduction per layer
+
+This module provides format-agnostic selective weight extraction:
+  - BnB 4-bit (nf4/fp4): slice quantized data + absmax per expert
+  - bf16/fp32: direct indexing (no dequant needed)
+  - FP8: slice + cast
+
+The ScatterMoE kernel itself doesn't change — we remap expert indices
+from global (0..E-1) to compact (0..num_active-1) and pass the smaller
+weight tensor.
+"""
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+
+def get_active_experts(sorted_expert_idxs: torch.Tensor, E: int) -> torch.Tensor:
+    """Get sorted unique expert indices from the routing output.
+
+    Args:
+        sorted_expert_idxs: Expert assignments sorted by expert id [T*k]
+        E: Total number of experts
+
+    Returns:
+        active: Sorted unique expert indices [num_active]
+    """
+    return torch.unique(sorted_expert_idxs)
+
+
+def remap_expert_indices(
+    sorted_expert_idxs: torch.Tensor,
+    expert_offsets: torch.Tensor,
+    active_experts: torch.Tensor,
+    E: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Remap global expert indices to compact indices.
+
+    Maps expert ids from [0..E-1] to [0..num_active-1], preserving the
+    sort order. Also compacts expert_offsets to only active experts.
+
+    Args:
+        sorted_expert_idxs: [T*k] expert ids in sorted order
+        expert_offsets: [E] cumulative token counts (original)
+        active_experts: [num_active] sorted unique expert ids
+        E: Total number of experts
+
+    Returns:
+        remapped_idxs: [T*k] expert ids in [0..num_active-1]
+        compact_offsets: [num_active] cumulative token counts
+    """
+    # Build remap table: global_id -> compact_id
+    remap = torch.empty(E, dtype=torch.long, device=sorted_expert_idxs.device)
+    remap[active_experts] = torch.arange(
+        len(active_experts), device=sorted_expert_idxs.device
+    )
+
+    remapped_idxs = remap[sorted_expert_idxs]
+
+    # Compact the expert_offsets: only keep active experts' cumulative counts
+    compact_offsets = expert_offsets[active_experts]
+
+    return remapped_idxs, compact_offsets
+
+
+def _selective_dequant_bnb4(
+    raw_param: torch.Tensor,
+    quant_state,
+    active_experts: torch.Tensor,
+    expert_shape: tuple[int, ...],
+) -> torch.Tensor:
+    """Dequantize only selected experts from BnB 4-bit packed data.
+
+    The raw parameter is a flattened 4-bit packed tensor. Each expert's
+    data is contiguous (stored in expert-major order), so we can gather
+    the packed data and absmax blocks for active experts, then dequantize
+    as one contiguous block.
+
+    Args:
+        raw_param: Flattened uint8 tensor of packed 4-bit weights
+        quant_state: BnB QuantState with absmax, blocksize, code, etc.
+        active_experts: [num_active] expert indices to dequantize
+        expert_shape: (dim1, dim2) shape per expert (e.g. (1024, 2048))
+
+    Returns:
+        Dequantized weights [num_active, dim1, dim2] in original dtype
+    """
+    import bitsandbytes.functional as F  # noqa: N812
+    from bitsandbytes.functional import QuantState
+
+    expert_numel = expert_shape[0] * expert_shape[1]
+    packed_per_expert = expert_numel // 2  # 4-bit = 2 values per byte
+    blocks_per_expert = expert_numel // quant_state.blocksize
+    num_active = len(active_experts)
+
+    if blocks_per_expert == 0:
+        # Expert is smaller than one quantization block — blocks span across
+        # expert boundaries, so per-expert slicing isn't possible.
+        # Fallback: full dequantize + index.
+        full = F.dequantize_4bit(raw_param, quant_state)
+        E_total = full.numel() // expert_numel
+        return full.reshape(E_total, *expert_shape)[active_experts]
+
+    # Use fused Triton kernel for NF4 (handles selective gather + dequant in one pass)
+    if quant_state.quant_type == "nf4" and raw_param.dtype == torch.uint8:
+        from axolotl.integrations.kernels.libs.scattermoe_lora.selective_dequant_kernel import (
+            selective_dequant_nf4_triton,
+        )
+
+        # Handle nested (double) quantization: dequantize absmax first
+        # BnB uses dequantize_blockwise (not _4bit) for nested absmax + offset
+        if quant_state.nested:
+            absmax = F.dequantize_blockwise(quant_state.absmax, quant_state.state2)
+            absmax += quant_state.offset
+            if absmax.dtype != torch.float32:
+                absmax = absmax.float()
+        else:
+            absmax = quant_state.absmax
+
+        return selective_dequant_nf4_triton(
+            packed_data=raw_param,
+            absmax=absmax,
+            active_experts=active_experts,
+            expert_shape=expert_shape,
+            blocksize=quant_state.blocksize,
+            dtype=quant_state.dtype,
+            codebook=quant_state.code,
+        )
+
+    # Fallback: gather + BnB dequant (for fp4 or non-uint8 packed formats)
+    raw_flat = raw_param.reshape(-1)
+
+    offsets_qt = (
+        active_experts.long()[:, None] * packed_per_expert
+        + torch.arange(packed_per_expert, device=raw_param.device)[None, :]
+    ).reshape(-1)
+    qt_gathered = raw_flat[offsets_qt]
+
+    offsets_abs = (
+        active_experts.long()[:, None] * blocks_per_expert
+        + torch.arange(blocks_per_expert, device=raw_param.device)[None, :]
+    ).reshape(-1)
+
+    if quant_state.nested:
+        full_absmax = F.dequantize_blockwise(quant_state.absmax, quant_state.state2)
+        full_absmax += quant_state.offset
+        if full_absmax.dtype != torch.float32:
+            full_absmax = full_absmax.float()
+        absmax_gathered = full_absmax[offsets_abs]
+    else:
+        absmax_gathered = quant_state.absmax[offsets_abs]
+
+    qt_gathered = qt_gathered.unsqueeze(1) if qt_gathered.dim() == 1 else qt_gathered
+
+    gathered_qs = QuantState(
+        absmax=absmax_gathered,
+        shape=torch.Size([num_active * expert_numel]),
+        blocksize=quant_state.blocksize,
+        quant_type=quant_state.quant_type,
+        code=quant_state.code,
+        dtype=quant_state.dtype,
+    )
+
+    deq = F.dequantize_4bit(qt_gathered, gathered_qs)
+    return deq.reshape(num_active, *expert_shape)
+
+
+def _selective_index_dense(
+    param: torch.Tensor,
+    active_experts: torch.Tensor,
+) -> torch.Tensor:
+    """Select experts from a dense (bf16/fp32) weight tensor.
+
+    Simple indexing — no dequantization needed.
+    """
+    return param[active_experts]
+
+
+def selective_expert_weights(
+    experts_module: nn.Module,
+    param_name: str,
+    active_experts: torch.Tensor,
+) -> torch.Tensor:
+    """Extract and dequantize only the active experts' weights.
+
+    Format-agnostic: dispatches based on whether the parameter is
+    BnB 4-bit quantized (via parametrize), FP8, or dense bf16/fp32.
+
+    Args:
+        experts_module: The base experts module (e.g. Qwen3_5MoeExperts)
+        param_name: "gate_up_proj" or "down_proj"
+        active_experts: [num_active] sorted unique expert indices
+
+    Returns:
+        Compact weight tensor [num_active, dim1, dim2] ready for ScatterMoE
+    """
+    # Check if the parameter is BnB-quantized via parametrize
+    if (
+        hasattr(experts_module, "parametrizations")
+        and param_name in experts_module.parametrizations
+    ):
+        param_list = experts_module.parametrizations[param_name]
+        parametrization = param_list[0]
+
+        # BnB 4-bit parametrization
+        if hasattr(parametrization, "quant_state"):
+            # The raw quantized data is on the ParametrizationList, not the
+            # individual Bnb4bitParametrization module
+            raw_param = param_list.original
+            qs = parametrization.quant_state
+            # qs.shape is the original tensor shape before flattening.
+            # For MoE experts it's [E, d1, d2] (3D) or [total_elements] (1D).
+            orig_shape = qs.shape
+            if isinstance(orig_shape, torch.Size) and len(orig_shape) == 3:
+                expert_shape = (orig_shape[1], orig_shape[2])
+            elif isinstance(orig_shape, torch.Size) and len(orig_shape) == 1:
+                # Flattened — need to infer from module attributes
+                E_total = getattr(experts_module, "num_experts", None)
+                if E_total is None:
+                    E_total = int(active_experts.max().item()) + 1
+                expert_numel = orig_shape[0] // E_total
+                d2 = getattr(experts_module, "hidden_dim", None) or getattr(experts_module, "intermediate_dim", None)
+                if d2 and expert_numel % d2 == 0:
+                    expert_shape = (expert_numel // d2, d2)
+                else:
+                    full = getattr(experts_module, param_name)
+                    return full[active_experts]
+            else:
+                full = getattr(experts_module, param_name)
+                return full[active_experts]
+
+            return _selective_dequant_bnb4(
+                raw_param, qs, active_experts, expert_shape
+            )
+
+    # Dense parameter (bf16/fp32) — direct indexing
+    param = getattr(experts_module, param_name)
+    if param.dim() == 3:
+        return param[active_experts]
+
+    # Fallback: full access
+    return param
+
+
+def selective_lora_weights(
+    lora_A: torch.Tensor,
+    lora_B: torch.Tensor,
+    active_experts: torch.Tensor,
+    E: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Select LoRA A and B weights for only the active experts.
+
+    LoRA layout (scattermoe format):
+      A: [r*E, K] — expert e occupies rows [e*r : (e+1)*r]
+      B: [N, r*E] — expert e occupies cols [e*r : (e+1)*r]
+
+    Returns compact:
+      A: [r*num_active, K]
+      B: [N, r*num_active]
+    """
+    R = lora_A.size(0) // E
+
+    # Vectorized gather: active_experts[:, None] * R + arange(R)[None, :]
+    row_idx = (
+        active_experts.long()[:, None] * R
+        + torch.arange(R, device=lora_A.device)[None, :]
+    ).reshape(-1)
+
+    compact_A = lora_A[row_idx]  # [r*num_active, K]
+    compact_B = lora_B[:, row_idx]  # [N, r*num_active]
+
+    return compact_A, compact_B
diff --git a/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant_kernel.py b/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant_kernel.py
new file mode 100644
index 000000000..e29799c05
--- /dev/null
+++ b/src/axolotl/integrations/kernels/libs/scattermoe_lora/selective_dequant_kernel.py
@@ -0,0 +1,167 @@
+"""
+Triton kernel for fused selective expert gather + NF4 dequantization.
+
+Instead of:
+  1. Gather packed uint8 data for active experts (memory copy)
+  2. Gather absmax for active experts (memory copy)
+  3. Call BnB dequantize_4bit CUDA kernel
+
+This kernel does all three in one pass:
+  - Reads packed NF4 bytes from expert-strided positions
+  - Looks up the NF4 codebook
+  - Multiplies by the per-block absmax
+  - Writes bf16 output directly
+
+This eliminates the intermediate gather buffer entirely.
+"""
+
+import torch
+import triton
+import triton.language as tl
+
+
+# NF4 codebook (16 values, precomputed by BnB)
+# These are the normalized float4 reconstruction values
+NF4_CODEBOOK = [
+    -1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453,
+    -0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0,
+    0.07958029955625534, 0.16093020141124725, 0.24611230194568634,
+    0.33791524171829224, 0.44070982933044434, 0.5626170039176941,
+    0.7229568362236023, 1.0,
+]
+
+
+@triton.jit
+def _selective_dequant_nf4_kernel(
+    # Input: packed NF4 data (flattened, expert-major order)
+    packed_ptr,
+    # Input: absmax values (flattened, expert-major order)
+    absmax_ptr,
+    # Input: active expert indices
+    active_experts_ptr,
+    # Input: NF4 codebook (16 float values)
+    codebook_ptr,
+    # Output: dequantized bf16 weights [num_active, expert_numel]
+    out_ptr,
+    stride_out_e,  # stride for expert dim in output
+    # Dimensions
+    num_active,
+    packed_per_expert,   # expert_numel // 2
+    blocks_per_expert,   # expert_numel // blocksize
+    blocksize: tl.constexpr,
+    # Tile size
+    BLOCK_SIZE: tl.constexpr,  # elements per thread block (must be multiple of 2)
+):
+    """
+    Each program processes BLOCK_SIZE elements from one expert.
+
+    Grid: (num_active, cdiv(expert_numel, BLOCK_SIZE))
+
+    For each output element:
+      1. Compute which byte in packed data contains this element
+      2. Extract the 4-bit nibble (high or low)
+      3. Look up in NF4 codebook
+      4. Scale by absmax for this block
+    """
+    expert_local_idx = tl.program_id(0)  # which active expert (0..num_active-1)
+    block_id = tl.program_id(1)  # which element block
+
+    # Load the global expert index
+    expert_global = tl.load(active_experts_ptr + expert_local_idx).to(tl.int64)
+
+    expert_numel = packed_per_expert * 2  # 2 elements per packed byte
+    elem_offset = block_id * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = elem_offset < expert_numel
+
+    # Each element is packed as: byte[i//2], low nibble for even i, high for odd i
+    byte_idx = elem_offset // 2
+    is_high = (elem_offset % 2) == 1
+
+    # Read packed bytes from the global expert's region
+    packed_global_offset = expert_global * packed_per_expert + byte_idx
+    packed_bytes = tl.load(packed_ptr + packed_global_offset, mask=mask, other=0).to(tl.int32)
+
+    # Extract 4-bit nibble
+    # BnB packing: high nibble = even element, low nibble = odd element
+    nibble = tl.where(is_high, packed_bytes & 0xF, (packed_bytes >> 4) & 0xF)
+
+    # NF4 codebook lookup
+    # Load all 16 codebook values (small, fits in registers)
+    # Use gather from codebook pointer
+    code_val = tl.load(codebook_ptr + nibble, mask=mask, other=0.0)
+
+    # Load absmax for this element's quantization block
+    block_idx = elem_offset // blocksize
+    absmax_global_offset = expert_global * blocks_per_expert + block_idx
+    absmax_val = tl.load(absmax_ptr + absmax_global_offset, mask=mask, other=1.0)
+
+    # Dequantize: value = codebook[nibble] * absmax
+    result = code_val * absmax_val
+
+    # Store to output
+    out_offset = expert_local_idx * stride_out_e + elem_offset
+    tl.store(out_ptr + out_offset, result.to(out_ptr.dtype.element_ty), mask=mask)
+
+
+def selective_dequant_nf4_triton(
+    packed_data: torch.Tensor,
+    absmax: torch.Tensor,
+    active_experts: torch.Tensor,
+    expert_shape: tuple[int, int],
+    blocksize: int,
+    dtype: torch.dtype = torch.bfloat16,
+    codebook: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """Fused selective gather + NF4 dequantization via Triton kernel.
+
+    Args:
+        packed_data: Flattened packed NF4 data [total_packed] or [total_packed, 1]
+        absmax: Per-block scaling factors [total_blocks]
+        active_experts: Sorted indices of experts to dequantize [num_active]
+        expert_shape: (dim1, dim2) per expert
+        blocksize: Quantization block size
+        dtype: Output dtype (default bf16)
+        codebook: NF4 lookup table [16] (uses default NF4 codebook if None)
+
+    Returns:
+        Dequantized weights [num_active, dim1, dim2]
+    """
+    num_active = active_experts.shape[0]
+    expert_numel = expert_shape[0] * expert_shape[1]
+    packed_per_expert = expert_numel // 2
+    blocks_per_expert = expert_numel // blocksize
+
+    # Prepare codebook on device
+    if codebook is None:
+        codebook = torch.tensor(NF4_CODEBOOK, dtype=torch.float32,
+                                device=packed_data.device)
+    else:
+        codebook = codebook.to(device=packed_data.device, dtype=torch.float32)
+
+    # Flatten inputs
+    packed_flat = packed_data.reshape(-1)
+    absmax_flat = absmax.reshape(-1).float()  # absmax is usually fp32
+
+    # Output buffer
+    out = torch.empty(num_active, expert_numel, dtype=dtype,
+                      device=packed_data.device)
+
+    BLOCK_SIZE = 1024  # Process 1024 elements per thread block
+
+    grid = (num_active, triton.cdiv(expert_numel, BLOCK_SIZE))
+
+    _selective_dequant_nf4_kernel[grid](
+        packed_flat,
+        absmax_flat,
+        active_experts,
+        codebook,
+        out,
+        out.stride(0),
+        num_active=num_active,
+        packed_per_expert=packed_per_expert,
+        blocks_per_expert=blocks_per_expert,
+        blocksize=blocksize,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+
+    return out.reshape(num_active, *expert_shape)