diff --git a/arg_parser.py b/arg_parser.py
index 46d0359..a985380 100644
--- a/arg_parser.py
+++ b/arg_parser.py
@@ -64,6 +64,7 @@ def parse_commandline():
     # V2 model support
     parser_training.add_argument("--groupsize", type=int, default=-1, help="Groupsize of v2 model, use -1 to load v1 model")
 
+    # Multi GPU Support
     parser_training.add_argument("--local_rank", type=int, default=0, help="local rank if using torch.distributed.launch")
 
     return vars(parser.parse_args())
diff --git a/autograd_4bit.py b/autograd_4bit.py
index 26597a5..bb63cab 100644
--- a/autograd_4bit.py
+++ b/autograd_4bit.py
@@ -46,7 +46,8 @@ class Autograd4bitQuantLinear(nn.Module):
             self.register_buffer('qzeros',
                                   torch.empty((math.ceil(infeatures/groupsize), outfeatures // 256 * (bits * 8)), dtype=torch.int)
                                 )
-            self.register_buffer('scales', torch.empty((math.ceil(infeatures/groupsize),outfeatures)))
+            self.register_buffer('scales', torch.empty((math.ceil(infeatures/groupsize), outfeatures)))
+            self.register_buffer('g_idx', torch.tensor([i // self.groupsize  for i in range(infeatures)], dtype = torch.int32))
         self.bias = nn.Parameter(torch.empty(outfeatures))
         self.register_buffer(
             'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
diff --git a/triton_utils.py b/triton_utils.py
new file mode 100644
index 0000000..7f50c5e
--- /dev/null
+++ b/triton_utils.py
@@ -0,0 +1,205 @@
+import triton
+import triton.language as tl
+import torch
+
+# code based https://github.com/fpgaminer/GPTQ-triton
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+    ],
+    key=['M', 'N', 'K'],
+)
+
+@triton.jit
+def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
+                      scales_ptr, zeros_ptr, g_ptr,
+                      M, N, K, bits, maxq,
+                      stride_am, stride_ak,
+                      stride_bk, stride_bn,
+                      stride_cm, stride_cn,
+                      stride_scales, stride_zeros,
+                      BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+                      GROUP_SIZE_M: tl.constexpr):
+    """
+    Compute the matrix multiplication C = A x B.
+    A is of shape (M, K) float16
+    B is of shape (K//8, N) int32
+    C is of shape (M, N) float16
+    scales is of shape (G, N) float16
+    zeros is of shape (G, N) float16
+    g_ptr is of shape (K) int32 
+    """
+    infearure_per_bits = 32 // bits
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+    a_mask = (offs_am[:, None] < M)
+    # b_ptrs is set up such that it repeats elements along the K axis 8 times
+    b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+    g_ptrs = g_ptr + offs_k
+    # shifter is used to extract the N bits of each element in the 32-bit word from B
+    scales_ptrs = scales_ptr + offs_bn[None, :]
+    zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits) 
+    
+    shifter = (offs_k % infearure_per_bits) * bits
+    zeros_shifter = (offs_bn % infearure_per_bits) * bits
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+            
+    for k in range(0, num_pid_k):
+        g_idx = tl.load(g_ptrs)
+
+        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+        scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        
+        zeros = (zeros >> zeros_shifter[None, :]) & maxq
+        zeros = (zeros + 1)
+        
+        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+        # Now we need to unpack b (which is N-bit values) into 32-bit values
+        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+        b = (b - zeros) * scales  # Scale and shift
+
+        accumulator += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
+        g_ptrs += BLOCK_SIZE_K
+
+    c = accumulator.to(tl.float16)
+    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
+    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+    
+# code based https://github.com/fpgaminer/GPTQ-triton
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
+    ],
+    key=['M', 'N', 'K'],
+)
+
+@triton.jit
+def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
+                            scales_ptr, zeros_ptr, g_ptr,
+                            M, N, K, bits, maxq,
+                            stride_am, stride_ak,
+                            stride_bk, stride_bn,
+                            stride_cm, stride_cn,
+                            stride_scales, stride_zeros,
+                            BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+                            GROUP_SIZE_M: tl.constexpr):
+    """
+    Compute the matrix multiplication C = A x B.
+    A is of shape (M, N) float16
+    B is of shape (K//8, N) int32
+    C is of shape (M, K) float16
+    scales is of shape (G, N) float16
+    zeros is of shape (G, N) float16
+    g_ptr is of shape (K) int32 
+    """
+    infearure_per_bits = 32 // bits
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_k
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_k = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+    offs_n = tl.arange(0, BLOCK_SIZE_N)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+    a_mask = (offs_am[:, None] < M)
+    # b_ptrs is set up such that it repeats elements along the K axis 8 times
+    b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn)   # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+    g_ptrs = g_ptr + offs_bk
+    g_idx = tl.load(g_ptrs)
+    
+    # shifter is used to extract the N bits of each element in the 32-bit word from B
+    scales_ptrs = scales_ptr + offs_n[None, :]  + g_idx[:, None] * stride_scales
+    zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
+    
+    shifter = (offs_bk % infearure_per_bits) * bits
+    zeros_shifter = (offs_n % infearure_per_bits) * bits
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
+    
+    for k in range(0, num_pid_n):
+        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+        scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        
+        zeros = (zeros >> zeros_shifter[None, :]) & maxq
+        zeros = (zeros + 1)
+        
+        a = tl.load(a_ptrs, mask=a_mask, other=0.)   # (BLOCK_SIZE_M, BLOCK_SIZE_N)
+        b = tl.load(b_ptrs)   # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+        # Now we need to unpack b (which is N-bit values) into 32-bit values
+        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+        b = (b - zeros) * scales  # Scale and shift
+        b = tl.trans(b)
+
+        accumulator += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_N
+        b_ptrs += BLOCK_SIZE_N
+        scales_ptrs += BLOCK_SIZE_N
+        zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
+        
+    c = accumulator.to(tl.float16)
+    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
+    c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+    
+    
+def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
+    grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
+    matmul_248_kernel[grid](input, qweight, output,
+                            scales, qzeros, g_idx,
+                            input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
+                            input.stride(0), input.stride(1),
+                            qweight.stride(0), qweight.stride(1),
+                            output.stride(0), output.stride(1),
+                            scales.stride(0), qzeros.stride(0))
+    return output
+    
\ No newline at end of file