diff --git a/tests/unit/test_production_compress.py b/tests/unit/test_production_compress.py
new file mode 100644
index 00000000..3a3fb20d
--- /dev/null
+++ b/tests/unit/test_production_compress.py
@@ -0,0 +1,82 @@
+"""Test production compressor kernel (CSA + HCA reduce)."""
+import torch
+import math
+
+def test_csa_compress():
+    """CSA: ratio=4, overlapping Ca/Cb streams."""
+    torch.manual_seed(42)
+    device = 'cuda'
+    hd = 512
+    m = 4
+    T = 16  # 4 blocks of 4 tokens
+    n_blocks = T // m
+
+    # Create synthetic kv and gate projections
+    kv = torch.randn(T, 2 * hd, dtype=torch.float32, device=device)
+    gate = torch.randn(T, 2 * hd, dtype=torch.float32, device=device)
+
+    # Reference: PyTorch
+    Ca = kv[:, :hd].reshape(n_blocks, m, hd)
+    Cb = kv[:, hd:].reshape(n_blocks, m, hd)
+    Ga = gate[:, :hd].reshape(n_blocks, m, hd)
+    Gb = gate[:, hd:].reshape(n_blocks, m, hd)
+
+    ref = []
+    for bi in range(n_blocks):
+        if bi > 0:
+            block_kv = torch.cat([Ca[bi-1], Cb[bi]], dim=0)
+            block_gate = torch.cat([Ga[bi-1], Gb[bi]], dim=0)
+        else:
+            block_kv = Cb[bi]
+            block_gate = Gb[bi]
+        probs = torch.softmax(block_gate, dim=0)
+        compressed = (probs * block_kv).sum(0)
+        ref.append(compressed)
+    ref = torch.stack(ref)
+
+    # Production: CUDA kernel
+    from dsv4.kernels.compressor.production_compress import csa_compress_production
+    prod = csa_compress_production(kv, gate, None, None, m=m)
+
+    cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
+    max_err = (ref - prod).abs().max().item()
+    print(f"CSA compress: cos={cos:.6f} max_err={max_err:.6f} ref_max={ref.abs().max().item():.4f} prod_max={prod.abs().max().item():.4f}")
+    assert cos > 0.999, f"CSA compress cosine too low: {cos}"
+    print("  PASSED")
+
+def test_hca_compress():
+    """HCA: ratio=128, single stream."""
+    torch.manual_seed(42)
+    device = 'cuda'
+    hd = 512
+    m = 8  # Use 8 instead of 128 for test speed
+    T = 24  # 3 blocks
+    n_blocks = T // m
+
+    kv = torch.randn(T, hd, dtype=torch.float32, device=device)
+    gate = torch.randn(T, hd, dtype=torch.float32, device=device)
+
+    # Reference
+    ref = []
+    for bi in range(n_blocks):
+        block_kv = kv[bi*m:(bi+1)*m]
+        block_gate = gate[bi*m:(bi+1)*m]
+        probs = torch.softmax(block_gate, dim=0)
+        compressed = (probs * block_kv).sum(0)
+        ref.append(compressed)
+    ref = torch.stack(ref)
+
+    # Production
+    from dsv4.kernels.compressor.production_compress import hca_compress_production
+    prod = hca_compress_production(kv, gate, None, None, m=m)
+
+    cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
+    max_err = (ref - prod).abs().max().item()
+    print(f"HCA compress: cos={cos:.6f} max_err={max_err:.6f}")
+    assert cos > 0.999, f"HCA compress cosine too low: {cos}"
+    print("  PASSED")
+
+if __name__ == "__main__":
+    test_csa_compress()
+    test_hca_compress()
+    print("\nAll compressor tests PASSED")