diff --git a/tests/test_full_layer_nan_b200.py b/tests/test_full_layer_nan_b200.py
new file mode 100644
index 00000000..b52bc7b6
--- /dev/null
+++ b/tests/test_full_layer_nan_b200.py
@@ -0,0 +1,348 @@
+#!/usr/bin/env python3
+"""
+DeepSeek-V4 Full Layer Forward Test
+
+Tests a complete transformer layer (attention + MoE) with real weights.
+If this produces NaN, we can bisect which component causes it.
+
+Usage (on B200):
+  cd /root/nvfp4-megamoe-kernel
+  PYTHONPATH=/root/nvfp4-megamoe-kernel tests/venv/bin/python tests/test_full_layer_nan_b200.py
+"""
+
+import sys, os, json, torch, torch.nn.functional as F
+from safetensors import safe_open
+
+REPO = "/root/nvfp4-megamoe-kernel"
+sys.path.insert(0, REPO)
+MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
+DEV = "cuda:0"
+
+H = 7168; NH = 128; HD = 512; NOPE = 448; ROPE = 64
+QL = 1536; OL = 1024; OG = 16; HPG = NH // OG
+INTERMEDIATE = 3072
+TOPK = 6
+EPS = 1e-6; WINDOW = 128; SCALE = HD ** -0.5
+
+E2M1 = torch.tensor([0,.5,1.,1.5,2.,3.,4.,6.,-0,-.5,-1.,-1.5,-2.,-3.,-4.,-6.], dtype=torch.float32)
+
+_cache = {}
+def P(k, wm, md):
+    if k in _cache: return _cache[k]
+    with safe_open(os.path.join(md, wm[k]), framework="pt") as f:
+        t = f.get_tensor(k)
+    _cache[k] = t
+    return t
+
+def rms(x, w, eps=1e-6):
+    v = x.float().pow(2).mean(-1, keepdim=True)
+    return (w.float() * (x * torch.rsqrt(v+eps)).float()).to(x.dtype)
+
+def make_runner(w, sf, gs_t, inf, outf):
+    from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear
+    fp4 = w.view(torch.float4_e2m1fn_x2).permute(1,0).contiguous()
+    s = sf.to(torch.float8_e4m3fn) if sf.dtype != torch.float8_e4m3fn else sf
+    s = s.permute(1,0).contiguous()
+    gs = gs_t.max().item() if gs_t.numel() > 1 else gs_t.item()
+    r = CuTeDSLNvfp4Linear(in_features=inf, out_features=outf, max_num_tokens=8192, device=str(w.device))
+    r.fp4 = [fp4]; r.sf = [s]; r.gs = [gs]
+    r.finalize_weights(); r._ensure_initialized()
+    return r
+
+def build_cos_sin(max_pos=4096, rope_dim=ROPE):
+    half = rope_dim // 2
+    inv_freq = 1.0 / (10000.0 ** (torch.arange(0, half, dtype=torch.float32) / half))
+    freqs = torch.outer(torch.arange(max_pos, dtype=torch.float32), inv_freq)
+    return torch.cat([freqs.cos(), freqs.sin()], dim=-1)
+
+def apply_gptj_rope(x, positions, cos_sin, nope_dim, rope_dim):
+    if rope_dim == 0 or x.numel() == 0: return x
+    half = rope_dim // 2
+    cos = cos_sin[positions, :half].to(x.dtype)
+    sin = cos_sin[positions, half:2*half].to(x.dtype)
+    if x.dim() == 3: cos = cos.unsqueeze(1); sin = sin.unsqueeze(1)
+    x_rope = x[..., nope_dim:].clone()
+    even = x_rope[..., 0::2]; odd = x_rope[..., 1::2]
+    out = x.clone()
+    out[..., nope_dim:][..., 0::2] = even * cos - odd * sin
+    out[..., nope_dim:][..., 1::2] = even * sin + odd * cos
+    return out
+
+def apply_inv_gptj_rope(x, positions, cos_sin, nope_dim, rope_dim):
+    if rope_dim == 0 or x.numel() == 0: return x
+    half = rope_dim // 2
+    cos = cos_sin[positions, :half].to(x.dtype)
+    sin = cos_sin[positions, half:2*half].to(x.dtype)
+    if x.dim() == 3: cos = cos.unsqueeze(1); sin = sin.unsqueeze(1)
+    x_rope = x[..., nope_dim:].clone()
+    even = x_rope[..., 0::2]; odd = x_rope[..., 1::2]
+    out = x.clone()
+    out[..., nope_dim:][..., 0::2] = even * cos + odd * sin
+    out[..., nope_dim:][..., 1::2] = -even * sin + odd * cos
+    return out
+
+def kv_quantize_fp8(kv_bf16):
+    amax = kv_bf16.float().abs().amax(dim=-1, keepdim=True).clamp(min=1e-12)
+    fp8_max = torch.tensor(448.0, dtype=torch.float32, device=kv_bf16.device)
+    scale = fp8_max / amax
+    kv_fp8 = (kv_bf16.float() * scale).to(torch.float8_e4m3fn)
+    inv_scale = (amax / fp8_max).to(torch.bfloat16)
+    return kv_fp8, inv_scale
+
+def kv_dequantize_fp8(kv_fp8, inv_scale):
+    return (kv_fp8.to(torch.bfloat16) * inv_scale).to(torch.bfloat16)
+
+def causal_prefill_attention(q, kv, scale):
+    T, NH, HD = q.shape
+    q_t = q.permute(1, 0, 2)
+    kv_exp = kv.unsqueeze(0).expand(NH, -1, -1)
+    out = F.scaled_dot_product_attention(q_t, kv_exp, kv_exp, is_causal=True, scale=scale)
+    return out.permute(1, 0, 2)
+
+
+def test_full_layer(layer_id, num_tokens=8, num_moe_experts=16):
+    """Test a complete transformer layer with attention + MoE."""
+    from cutedsl.runner import CuTeDSLMoERunner
+    
+    torch.cuda.set_device(0)
+    torch.manual_seed(42)
+    torch.cuda.empty_cache()
+    _cache.clear()
+    
+    with open(os.path.join(MODEL, "model.safetensors.index.json")) as f:
+        wm = json.load(f)["weight_map"]
+    G = lambda k: P(k, wm, MODEL).to(DEV)
+    
+    p = f"model.layers.{layer_id}"; a = f"{p}.self_attn"
+    m = f"{p}.mlp"
+    cr = 128 if layer_id == 0 else (0 if layer_id == 60 else 4)
+    lt = f"C{cr}A" if cr > 1 else "SWA"
+    
+    emb = G("model.embed_tokens.weight")
+    anorm = G(f"{p}.input_layernorm.weight")
+    qn = G(f"{a}.q_a_norm.weight"); kvn = G(f"{a}.kv_norm.weight")
+    woa = G(f"{a}.o_a_proj.weight")
+    fnorm = G(f"{p}.post_attention_layernorm.weight")
+    
+    qa_w = G(f"{a}.q_a_proj.weight"); qa_sf = G(f"{a}.q_a_proj.weight_scale"); qa_gs = G(f"{a}.q_a_proj.weight_scale_2")
+    qb_w = G(f"{a}.q_b_proj.weight"); qb_sf = G(f"{a}.q_b_proj.weight_scale"); qb_gs = G(f"{a}.q_b_proj.weight_scale_2")
+    kv_w = G(f"{a}.kv_proj.weight"); kv_sf = G(f"{a}.kv_proj.weight_scale"); kv_gs = G(f"{a}.kv_proj.weight_scale_2")
+    wob_w = G(f"{a}.o_b_proj.weight"); wob_sf = G(f"{a}.o_b_proj.weight_scale"); wob_gs = G(f"{a}.o_b_proj.weight_scale_2")
+    
+    r_qa = make_runner(qa_w, qa_sf, qa_gs, H, qa_w.shape[0])
+    r_qb = make_runner(qb_w, qb_sf, qb_gs, QL, qb_w.shape[0])
+    r_kv = make_runner(kv_w, kv_sf, kv_gs, H, kv_w.shape[0])
+    r_wob = make_runner(wob_w, wob_sf, wob_gs, OG*OL, wob_w.shape[0])
+    cos_sin = build_cos_sin(max_pos=4096).to(DEV)
+    woa_3d = woa.view(OG, OL, HPG * HD)
+    
+    # MoE weights (only first num_moe_experts to fit in memory)
+    gate_ws, gate_sfs, gate_gss = [], [], []
+    up_ws, up_sfs, up_gss = [], [], []
+    down_ws, down_sfs, down_gss = [], [], []
+    for i in range(num_moe_experts):
+        e = f"{m}.experts.{i}"
+        gate_ws.append(G(f"{e}.gate_proj.weight"))
+        gate_sfs.append(G(f"{e}.gate_proj.weight_scale"))
+        gate_gss.append(G(f"{e}.gate_proj.weight_scale_2"))
+        up_ws.append(G(f"{e}.up_proj.weight"))
+        up_sfs.append(G(f"{e}.up_proj.weight_scale"))
+        up_gss.append(G(f"{e}.up_proj.weight_scale_2"))
+        down_ws.append(G(f"{e}.down_proj.weight"))
+        down_sfs.append(G(f"{e}.down_proj.weight_scale"))
+        down_gss.append(G(f"{e}.down_proj.weight_scale_2"))
+    
+    w13_w = torch.cat([torch.stack(gate_ws), torch.stack(up_ws)], dim=1)
+    w13_sf = torch.cat([torch.stack(gate_sfs), torch.stack(up_sfs)], dim=1)
+    w13_gs = torch.cat([torch.stack(gate_gss), torch.stack(up_gss)], dim=0)
+    w2_w = torch.stack(down_ws)
+    w2_sf = torch.stack(down_sfs)
+    w2_gs = torch.stack(down_gss)
+    
+    # Free per-expert lists
+    del gate_ws, gate_sfs, gate_gss, up_ws, up_sfs, up_gss, down_ws, down_sfs, down_gss
+    
+    moe_runner = CuTeDSLMoERunner(
+        num_experts=num_moe_experts,
+        hidden_size=H,
+        intermediate_size=INTERMEDIATE,
+        max_num_tokens=8192,
+        top_k=TOPK,
+        device=str(DEV),
+    )
+    
+    l1_fp4 = w13_w.view(torch.float4_e2m1fn_x2)
+    l2_fp4 = w2_w.view(torch.float4_e2m1fn_x2)
+    l1_sf = w13_sf.to(torch.float8_e4m3fn)
+    l2_sf = w2_sf.to(torch.float8_e4m3fn)
+    
+    moe_runner.prepare_weights_from_stacked(
+        l1_fp4, l1_sf, w13_gs.flatten().tolist(),
+        l2_fp4, l2_sf, w2_gs.flatten().tolist(),
+    )
+    
+    del w13_w, w13_sf, w13_gs, w2_w, w2_sf, w2_gs, l1_fp4, l2_fp4, l1_sf, l2_sf
+    torch.cuda.empty_cache()
+    
+    # Shared expert
+    se_gate_w = G(f"{m}.shared_experts.gate_proj.weight"); se_gate_sf = G(f"{m}.shared_experts.gate_proj.weight_scale"); se_gate_gs = G(f"{m}.shared_experts.gate_proj.weight_scale_2")
+    se_up_w = G(f"{m}.shared_experts.up_proj.weight"); se_up_sf = G(f"{m}.shared_experts.up_proj.weight_scale"); se_up_gs = G(f"{m}.shared_experts.up_proj.weight_scale_2")
+    se_down_w = G(f"{m}.shared_experts.down_proj.weight"); se_down_sf = G(f"{m}.shared_experts.down_proj.weight_scale"); se_down_gs = G(f"{m}.shared_experts.down_proj.weight_scale_2")
+    
+    r_se_gate = make_runner(se_gate_w, se_gate_sf, se_gate_gs, H, se_gate_w.shape[0])
+    r_se_up = make_runner(se_up_w, se_up_sf, se_up_gs, H, se_up_w.shape[0])
+    r_se_down = make_runner(se_down_w, se_down_sf, se_down_gs, INTERMEDIATE, se_down_w.shape[0])
+    
+    # Run the layer
+    token_ids = torch.randint(1, 1000, (num_tokens,), dtype=torch.long, device=DEV)
+    positions = torch.arange(num_tokens, dtype=torch.int64, device=DEV)
+    
+    with torch.no_grad():
+        hidden = emb[token_ids]
+        
+        # ── Attention ──────────────────────────────────────────
+        normed = rms(hidden, anorm, EPS)
+        qa = r_qa.run(normed); kv = r_kv.run(normed)
+        qa_n = rms(qa, qn, EPS); kv_n = rms(kv, kvn, EPS)
+        q = r_qb.run(qa_n).view(num_tokens, NH, HD)
+        q_rope = apply_gptj_rope(q, positions, cos_sin, NOPE, ROPE)
+        kv_rope = apply_gptj_rope(kv_n.unsqueeze(1), positions, cos_sin, NOPE, ROPE).squeeze(1)
+        
+        o_attn = causal_prefill_attention(q_rope, kv_rope, SCALE)
+        o_inv = apply_inv_gptj_rope(o_attn, positions, cos_sin, NOPE, ROPE)
+        o_grouped = o_inv.reshape(num_tokens, OG, HPG * HD).permute(1, 0, 2)
+        z = torch.bmm(o_grouped, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(num_tokens, OG * OL)
+        attn_out = r_wob.run(z)
+        
+        hidden = hidden + attn_out
+        print(f"  Layer {layer_id} ({lt}): after attention: amax={hidden.amax():.4f} NaN={torch.isnan(hidden).any()}")
+        
+        # ── MoE ────────────────────────────────────────────────
+        fnormed = rms(hidden, fnorm, EPS)
+        
+        # Shared expert
+        gate_out = r_se_gate.run(fnormed)
+        up_out = r_se_up.run(fnormed)
+        activated = F.silu(gate_out) * up_out
+        se_out = r_se_down.run(activated)
+        
+        # Routed experts (using MoE runner with subset of experts)
+        topk_ids = torch.randint(0, num_moe_experts, (num_tokens, TOPK), device=DEV)
+        topk_weights = torch.softmax(torch.randn(num_tokens, TOPK, device=DEV), dim=-1)
+        moe_out = moe_runner.run(fnormed, topk_weights, topk_ids)
+        
+        hidden = hidden + se_out + moe_out
+        print(f"  Layer {layer_id} ({lt}): after MoE: amax={hidden.amax():.4f} NaN={torch.isnan(hidden).any()}")
+    
+    del r_qa, r_qb, r_kv, r_wob, r_se_gate, r_se_up, r_se_down, moe_runner
+    torch.cuda.empty_cache()
+    _cache.clear()
+    
+    return not torch.isnan(hidden).any()
+
+
+def test_multi_layer():
+    """Test multiple layers chained together to see if NaN propagates."""
+    emb = None
+    
+    # Load embedding once
+    with open(os.path.join(MODEL, "model.safetensors.index.json")) as f:
+        wm = json.load(f)["weight_map"]
+    G = lambda k: P(k, wm, MODEL).to(DEV)
+    emb = G("model.embed_tokens.weight")
+    
+    num_tokens = 8
+    token_ids = torch.randint(1, 1000, (num_tokens,), dtype=torch.long, device=DEV)
+    hidden = emb[token_ids]
+    
+    # Test just layers 0, 2, 60 (one of each type)
+    # For each layer, do attention only (skip MoE to save memory)
+    for layer_id in [0, 2, 60]:
+        p = f"model.layers.{layer_id}"; a = f"{p}.self_attn"
+        cr = 128 if layer_id == 0 else (0 if layer_id == 60 else 4)
+        lt = f"C{cr}A" if cr > 1 else "SWA"
+        
+        anorm = G(f"{p}.input_layernorm.weight")
+        qn = G(f"{a}.q_a_norm.weight"); kvn = G(f"{a}.kv_norm.weight")
+        woa = G(f"{a}.o_a_proj.weight")
+        fnorm = G(f"{p}.post_attention_layernorm.weight")
+        
+        qa_w = G(f"{a}.q_a_proj.weight"); qa_sf = G(f"{a}.q_a_proj.weight_scale"); qa_gs = G(f"{a}.q_a_proj.weight_scale_2")
+        qb_w = G(f"{a}.q_b_proj.weight"); qb_sf = G(f"{a}.q_b_proj.weight_scale"); qb_gs = G(f"{a}.q_b_proj.weight_scale_2")
+        kv_w = G(f"{a}.kv_proj.weight"); kv_sf = G(f"{a}.kv_proj.weight_scale"); kv_gs = G(f"{a}.kv_proj.weight_scale_2")
+        wob_w = G(f"{a}.o_b_proj.weight"); wob_sf = G(f"{a}.o_b_proj.weight_scale"); wob_gs = G(f"{a}.o_b_proj.weight_scale_2")
+        
+        r_qa = make_runner(qa_w, qa_sf, qa_gs, H, qa_w.shape[0])
+        r_qb = make_runner(qb_w, qb_sf, qb_gs, QL, qb_w.shape[0])
+        r_kv = make_runner(kv_w, kv_sf, kv_gs, H, kv_w.shape[0])
+        r_wob = make_runner(wob_w, wob_sf, wob_gs, OG*OL, wob_w.shape[0])
+        cos_sin = build_cos_sin(max_pos=4096).to(DEV)
+        woa_3d = woa.view(OG, OL, HPG * HD)
+        
+        # Shared expert
+        m = f"{p}.mlp"
+        se_gate_w = G(f"{m}.shared_experts.gate_proj.weight"); se_gate_sf = G(f"{m}.shared_experts.gate_proj.weight_scale"); se_gate_gs = G(f"{m}.shared_experts.gate_proj.weight_scale_2")
+        se_up_w = G(f"{m}.shared_experts.up_proj.weight"); se_up_sf = G(f"{m}.shared_experts.up_proj.weight_scale"); se_up_gs = G(f"{m}.shared_experts.up_proj.weight_scale_2")
+        se_down_w = G(f"{m}.shared_experts.down_proj.weight"); se_down_sf = G(f"{m}.shared_experts.down_proj.weight_scale"); se_down_gs = G(f"{m}.shared_experts.down_proj.weight_scale_2")
+        
+        r_se_gate = make_runner(se_gate_w, se_gate_sf, se_gate_gs, H, se_gate_w.shape[0])
+        r_se_up = make_runner(se_up_w, se_up_sf, se_up_gs, H, se_up_w.shape[0])
+        r_se_down = make_runner(se_down_w, se_down_sf, se_down_gs, INTERMEDIATE, se_down_w.shape[0])
+        
+        positions = torch.arange(num_tokens, dtype=torch.int64, device=DEV)
+        
+        with torch.no_grad():
+            # Attention
+            normed = rms(hidden, anorm, EPS)
+            qa = r_qa.run(normed); kv = r_kv.run(normed)
+            qa_n = rms(qa, qn, EPS); kv_n = rms(kv, kvn, EPS)
+            q = r_qb.run(qa_n).view(num_tokens, NH, HD)
+            q_rope = apply_gptj_rope(q, positions, cos_sin, NOPE, ROPE)
+            kv_rope = apply_gptj_rope(kv_n.unsqueeze(1), positions, cos_sin, NOPE, ROPE).squeeze(1)
+            
+            o_attn = causal_prefill_attention(q_rope, kv_rope, SCALE)
+            o_inv = apply_inv_gptj_rope(o_attn, positions, cos_sin, NOPE, ROPE)
+            o_grouped = o_inv.reshape(num_tokens, OG, HPG * HD).permute(1, 0, 2)
+            z = torch.bmm(o_grouped, woa_3d.transpose(1, 2)).permute(1, 0, 2).reshape(num_tokens, OG * OL)
+            attn_out = r_wob.run(z)
+            hidden = hidden + attn_out
+            
+            # Shared expert MoE
+            fnormed = rms(hidden, fnorm, EPS)
+            gate_out = r_se_gate.run(fnormed)
+            up_out = r_se_up.run(fnormed)
+            activated = F.silu(gate_out) * up_out
+            se_out = r_se_down.run(activated)
+            hidden = hidden + se_out
+            
+            attn_nan = torch.isnan(attn_out).any().item()
+            moe_nan = torch.isnan(se_out).any().item()
+            hs_nan = torch.isnan(hidden).any().item()
+            print(f"  Layer {layer_id} ({lt}): attn_nan={attn_nan} moe_nan={moe_nan} hidden_nan={hs_nan} amax={hidden.amax():.4f}")
+            
+            if hs_nan:
+                print(f"  NaN detected at layer {layer_id}! Stopping.")
+                break
+        
+        del r_qa, r_qb, r_kv, r_wob, r_se_gate, r_se_up, r_se_down
+        torch.cuda.empty_cache()
+        _cache.clear()
+
+
+def main():
+    print("=" * 70)
+    print("  DeepSeek-V4 Full Layer NaN Test")
+    print("  Tests attention + MoE to find where NaN originates")
+    print("=" * 70)
+    
+    print("\n=== Test 1: Single full layer (attention + MoE) ===")
+    test_full_layer(layer_id=2, num_tokens=8, num_moe_experts=16)
+    
+    print("\n=== Test 2: Multi-layer chain (attention + shared expert only) ===")
+    test_multi_layer()
+    
+    print(f"\n{'='*70}")
+
+
+if __name__ == "__main__":
+    main()