diff --git a/tests/test_vllm_codepaths_b200.py b/tests/test_vllm_codepaths_b200.py
new file mode 100644
index 00000000..48cf0234
--- /dev/null
+++ b/tests/test_vllm_codepaths_b200.py
@@ -0,0 +1,251 @@
+#!/usr/bin/env python3
+"""
+Test the EXACT code paths used in vLLM's Blackwell attention.
+
+Imports the actual functions from csa_attention.py and blackwell_attention.py
+and verifies they produce correct output with real weights.
+
+This is the closest possible test to what runs in the container.
+"""
+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
+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 main():
+    """Test the exact csa_attention.py code paths used in the container."""
+    from cutedsl.blackwell_attention import (
+        blackwell_attention_kv_write,
+        blackwell_attention_decode,
+        blackwell_attention_forward,
+    )
+    # Also import the vLLM patch version
+    sys.path.insert(0, os.path.join(REPO, "vllm", "patches", "layers"))
+    from csa_attention import (
+        fused_qnorm_rope_kv_insert_py,
+        blackwell_attention_kv_write as vllm_kv_write,
+        blackwell_attention_decode as vllm_decode,
+        kv_quantize_fp8 as vllm_kv_quantize,
+        kv_dequantize_fp8 as vllm_kv_dequantize,
+    )
+    
+    torch.cuda.set_device(0)
+    
+    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)
+    
+    # Test layer 60 (SWA)
+    layer_id = 60
+    p = f"model.layers.{layer_id}"; a = f"{p}.self_attn"
+    
+    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")
+    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)
+    
+    N = 8
+    token_ids = torch.tensor([1, 450, 8403, 315, 5413, 374, 2198, 643], dtype=torch.long, device=DEV)
+    
+    with torch.no_grad():
+        # ── Test 1: Verify fused_qnorm_rope_kv_insert_py ──────────
+        print("=== Test 1: fused_qnorm_rope_kv_insert_py ===")
+        positions_p = torch.arange(N, dtype=torch.int64, device=DEV)
+        hidden_p = emb[token_ids]
+        normed_p = rms(hidden_p, anorm, EPS)
+        qa_p = r_qa.run(normed_p)
+        kv_p = r_kv.run(normed_p)
+        
+        # Manual Q norm + RoPE (reference)
+        qa_n_ref = rms(qa_p, qn, EPS)
+        q_ref = r_qb.run(qa_n_ref).view(N, NH, HD)
+        q_rope_ref = apply_gptj_rope(q_ref, positions_p, cos_sin, NOPE, ROPE)
+        
+        # Using fused_qnorm_rope_kv_insert_py
+        q_test = r_qb.run(qa_n_ref).view(N, NH, HD)
+        fused_qnorm_rope_kv_insert_py(
+            q_test, kv_p, None, None, positions_p,
+            cos_sin, EPS, 64,  # block_size
+            nope_dim=NOPE, rope_dim=ROPE,
+        )
+        
+        c = F.cosine_similarity(q_rope_ref.flatten().unsqueeze(0).float(), q_test.flatten().unsqueeze(0).float()).item()
+        print(f"  fused_qnorm_rope vs manual: cosine = {c:.6f} {'PASS' if c>=0.999 else 'FAIL'}")
+        
+        # ── Test 2: Verify blackwell_attention_kv_write ───────────
+        print("\n=== Test 2: blackwell_attention_kv_write ===")
+        block_size = 64; max_tokens = 256
+        num_blocks = (max_tokens + block_size - 1) // block_size
+        
+        # uint8 cache (like vLLM uses)
+        swa_cache = torch.zeros(num_blocks, block_size, HD, dtype=torch.uint8, device=DEV)
+        inv_scale_cache = torch.zeros(max_tokens, 1, dtype=torch.bfloat16, device=DEV)
+        slot_mapping = positions_p  # Simple: slot = position
+        
+        # Manual KV RoPE + fp8 quant
+        kv_n = rms(kv_p, kvn, EPS)
+        kv_rope_manual = apply_gptj_rope(kv_n.unsqueeze(1), positions_p, cos_sin, NOPE, ROPE).squeeze(1)
+        kv_fp8_manual, inv_s_manual = kv_quantize_fp8(kv_rope_manual)
+        
+        # Write using vLLM's function
+        vllm_kv_write(
+            kv_n, positions_p, swa_cache, inv_scale_cache,
+            slot_mapping, block_size, cos_sin,
+            nope_dim=NOPE, rope_dim=ROPE,
+        )
+        
+        # Read back and compare
+        bi = slot_mapping // block_size; oi = slot_mapping % block_size
+        kv_read = swa_cache[bi, oi].view(torch.float8_e4m3fn)
+        inv_read = inv_scale_cache[slot_mapping]
+        kv_dequant = kv_dequantize_fp8(kv_read, inv_read)
+        
+        c = F.cosine_similarity(kv_rope_manual.flatten().unsqueeze(0).float(), kv_dequant.flatten().unsqueeze(0).float()).item()
+        print(f"  vllm_kv_write roundtrip: cosine = {c:.6f} {'PASS' if c>=0.99 else 'FAIL'}")
+        
+        # ── Test 3: Decode attention using swa_indices ────────────
+        print("\n=== Test 3: Decode attention with swa_indices ===")
+        decode_id = torch.tensor([991], dtype=torch.long, device=DEV)
+        pos_d = torch.tensor([N], dtype=torch.int64, device=DEV)
+        
+        # Write decode KV to cache
+        hidden_d = emb[decode_id]
+        normed_d = rms(hidden_d, anorm, EPS)
+        qa_d = r_qa.run(normed_d); kv_d = r_kv.run(normed_d)
+        qa_n_d = rms(qa_d, qn, EPS); kv_n_d = rms(kv_d, kvn, EPS)
+        q_d = r_qb.run(qa_n_d).view(1, NH, HD)
+        q_rope_d = apply_gptj_rope(q_d, pos_d, cos_sin, NOPE, ROPE)
+        
+        vllm_kv_write(kv_n_d, pos_d, swa_cache, inv_scale_cache,
+                      pos_d, block_size, cos_sin, nope_dim=NOPE, rope_dim=ROPE)
+        
+        # swa_indices: simulate vLLM's pre-computed indices
+        # These are flat slot indices for each decode token's window
+        all_slots = torch.arange(N + 1, dtype=torch.int64, device=DEV)
+        swa_indices = all_slots.unsqueeze(0)  # (1, N+1) — all tokens in window
+        swa_lens = torch.tensor([N + 1], dtype=torch.int64, device=DEV)
+        
+        o_decode = vllm_decode(
+            q_rope_d, pos_d, swa_cache, inv_scale_cache,
+            pos_d, block_size, SCALE, WINDOW,
+            swa_indices=swa_indices,
+            swa_lens=swa_lens,
+            decode_token_idx=0,
+        )
+        print(f"  Decode output: amax={o_decode.amax():.4f} NaN={torch.isnan(o_decode).any()}")
+        
+        # ── Reference: full prefill attention ────────────────────
+        all_ids = torch.cat([token_ids, decode_id])
+        all_pos = torch.arange(N + 1, dtype=torch.int64, device=DEV)
+        hidden_ref = emb[all_ids]
+        normed_ref = rms(hidden_ref, anorm, EPS)
+        qa_ref = r_qa.run(normed_ref); kv_ref = r_kv.run(normed_ref)
+        qa_n_ref = rms(qa_ref, qn, EPS); kv_n_ref = rms(kv_ref, kvn, EPS)
+        q_ref = r_qb.run(qa_n_ref).view(N + 1, NH, HD)
+        q_rope_ref = apply_gptj_rope(q_ref, all_pos, cos_sin, NOPE, ROPE)
+        kv_rope_ref = apply_gptj_rope(kv_n_ref.unsqueeze(1), all_pos, cos_sin, NOPE, ROPE).squeeze(1)
+        o_ref = causal_prefill_attention(q_rope_ref, kv_rope_ref, SCALE)
+        o_ref_decode = o_ref[-1:]
+        
+        c = F.cosine_similarity(o_decode.flatten().unsqueeze(0).float(), o_ref_decode.flatten().unsqueeze(0).float()).item()
+        print(f"  Decode vs reference cosine: {c:.6f} {'PASS' if c>=0.98 else 'FAIL}")
+    
+    print("\n=== DONE ===")
+
+
+if __name__ == "__main__":
+    main()