diff --git a/lib/nnc/ccv_nnc_palettize.c b/lib/nnc/ccv_nnc_palettize.c
index 15ad3e28a..c7f55e6f2 100644
--- a/lib/nnc/ccv_nnc_palettize.c
+++ b/lib/nnc/ccv_nnc_palettize.c
@@ -964,6 +964,7 @@ void ccv_nnc_depalettize(const void* input, const int datatype, const int memory
 #ifdef HAVE_CUDA
 		ccv_nnc_compat_depalettize(input, datatype, input_length, qbits, number_in_blocks, output, output_length, 0);
 #elif defined(HAVE_MPS)
+		ccv_nnc_mps_depalettize(input, datatype, input_length, qbits, number_in_blocks, output, output_length, 0);
 #else
 		assert(memory_type == CCV_TENSOR_CPU_MEMORY);
 #endif
diff --git a/lib/nnc/mfa/ccv_nnc_mfa.cpp b/lib/nnc/mfa/ccv_nnc_mfa.cpp
index 644842c0e..135d40dd0 100644
--- a/lib/nnc/mfa/ccv_nnc_mfa.cpp
+++ b/lib/nnc/mfa/ccv_nnc_mfa.cpp
@@ -94,6 +94,12 @@ void mfa::cache<mfa::normalization::hash, mfa::normalization::pipeline>::prepare
   _mfa_cache_prepare(&map, context, hash);
 }
 
+template <>
+void mfa::cache<mfa::depalettize::hash, mfa::depalettize::pipeline>::prepare(mfa::context* context, mfa::depalettize::hash hash)
+{
+  _mfa_cache_prepare(&map, context, hash);
+}
+
 mfa::context::context(MTL::Device* device)
 {
   auto* pool = NS::AutoreleasePool::alloc()->init();
diff --git a/lib/nnc/mfa/ccv_nnc_mfa.hpp b/lib/nnc/mfa/ccv_nnc_mfa.hpp
index 8dec4dfc8..29bdf51d5 100644
--- a/lib/nnc/mfa/ccv_nnc_mfa.hpp
+++ b/lib/nnc/mfa/ccv_nnc_mfa.hpp
@@ -6,6 +6,7 @@
 #include "ccv_nnc_mfa_attention.hpp"
 #include "ccv_nnc_mfa_gemm.hpp"
 #include "ccv_nnc_mfa_normalization.hpp"
+#include "ccv_nnc_mfa_depalettize.hpp"
 
 #ifdef __cplusplus
 #include "nnc/mfa/3rdparty/metal-cpp/Dispatch.hpp"
@@ -44,6 +45,7 @@ class context {
   cache<attention::hash, attention::pipeline> attention_cache;
   cache<gemm::hash, gemm::pipeline> gemm_cache;
   cache<normalization::hash, normalization::pipeline> normalization_cache;
+  cache<depalettize::hash, depalettize::pipeline> depalettize_cache;
   
   MTL::Buffer* request_scratch(uint64_t size);
 };
diff --git a/lib/nnc/mfa/ccv_nnc_mfa_depalettize.cpp b/lib/nnc/mfa/ccv_nnc_mfa_depalettize.cpp
new file mode 100644
index 000000000..f62815536
--- /dev/null
+++ b/lib/nnc/mfa/ccv_nnc_mfa_depalettize.cpp
@@ -0,0 +1,225 @@
+#include "ccv_nnc_mfa.hpp"
+#include "ccv_nnc_mfa_hash.hpp"
+#include <simd/simd.h>
+using namespace ccv::nnc;
+
+#include <string>
+
+// MARK: - C
+
+void ccv_nnc_mfa_prepare_depalettize(mfa::context* context, ccv_nnc_mfa_depalettize_params_t params)
+{
+  context->depalettize_cache.prepare(context, mfa::depalettize::hash(params));
+}
+
+void ccv_nnc_mfa_encode_depalettize(ccv_nnc_mfa_context_t* context, ccv_nnc_mfa_depalettize_params_t params, mtl_command_batch_t* command_batch, mtl_buffer_t** tensors, size_t* tensor_offsets)
+{
+  mfa::depalettize::hash hash(params);
+  auto iterator = context->depalettize_cache.map.find(hash);
+  if (iterator == context->depalettize_cache.map.end()) {
+    mfa::precondition_failure("Depalettize hash not cached.", __LINE__, __FILE__, __FUNCTION__);
+  }
+  
+  auto* pipeline = iterator->second;
+  auto encoder = command_batch->startCommand();
+  
+  int num_tensors = 0;
+  while (tensors[num_tensors] != nullptr) {
+    encoder->setBuffer(tensors[num_tensors], tensor_offsets[num_tensors], NS::UInteger(num_tensors));
+    num_tensors += 1;
+  }
+  CCV_NNC_MFA_PRECONDITION(num_tensors == 2);
+  
+  encoder->setComputePipelineState(pipeline->depalettize_pso.get());
+  encoder->useResource(tensors[0], MTL::ResourceUsageRead);
+  encoder->useResource(tensors[1], MTL::ResourceUsageWrite);
+  
+  auto grid_size = pipeline->grid_size;
+  grid_size.depth = 1;
+  CCV_NNC_MFA_PRECONDITION(grid_size.depth > 0);
+  encoder->dispatchThreadgroups(grid_size, pipeline->group_size);
+  command_batch->finishCommand(encoder);
+}
+
+// MARK: - C++
+
+mfa::depalettize::hash::hash(ccv_nnc_mfa_depalettize_params_t params) {
+  data_type = params.data_type;
+  qbits = params.qbits;
+  number_in_blocks = params.number_in_blocks;
+  length = params.length;
+}
+
+bool mfa::depalettize::hash::operator==(const mfa::depalettize::hash& hash) const {
+  return
+  (data_type == hash.data_type) &&
+  (qbits == hash.qbits) &&
+  (number_in_blocks == hash.number_in_blocks) &&
+  (length == hash.length);
+}
+
+std::ostream& operator<<(std::ostream& os, const mfa::depalettize::hash& hash) {
+  os << "mfa::depalettize::hash {";
+  os << " .data_type = " << hash.data_type << ',';
+  os << " .qbits = " << hash.qbits << ',';
+  os << " .number_in_blocks = " << hash.number_in_blocks << ',';
+  os << " .length = " << hash.length << " ";
+  os << "}";
+  return os;
+}
+
+std::size_t std::hash<mfa::depalettize::hash>::operator()(const mfa::depalettize::hash& hash) const noexcept {
+  std::size_t seed = 0;
+  using namespace mfa::hash;
+  combine_64(seed, hash.data_type);
+  combine_64(seed, pack_64(simd::uint2 { (unsigned int)hash.qbits, (unsigned int)hash.number_in_blocks }));
+  combine_64(seed, hash.length);
+  return seed;
+}
+
+mfa::depalettize::pipeline::pipeline(mfa::context* context, mfa::depalettize::hash hash) {
+  // FlashNorm not supported for group depalettize yet.
+  CCV_NNC_MFA_PRECONDITION((hash.data_type == MTL::DataTypeFloat) || (hash.data_type == MTL::DataTypeHalf))
+  
+  auto* pool = NS::AutoreleasePool::alloc()->init();
+  
+  std::string shader;
+  if (hash.qbits == 6) {
+    shader = R"(
+#include <metal_stdlib>
+using namespace metal;
+
+kernel void depalettize(
+  device uchar *source [[buffer(0)]],
+  device real *destination [[buffer(1)]],
+
+  uint3 tgid [[threadgroup_position_in_grid]],
+  ushort lid [[thread_index_in_threadgroup]]
+) {
+  device const uchar *ui0 = source + (sizeof(real) * palette_size + number_in_blocks * 3) * tgid.y;
+  threadgroup real palette[palette_size];
+  if (lid < palette_size) {
+    palette[lid] = ((device real*)ui0)[lid];
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  destination += number_in_blocks * 4 * tgid.y + tgid.x * threadgroup_size * num_repeats * 4;
+  device const uchar *ui1 = ui0 + sizeof(real) * palette_size + tgid.x * threadgroup_size * num_repeats * 3;
+  #pragma clang loop unroll(full)
+  for (uint k = 0; k < num_repeats; k++)
+  {
+    const uint8_t u0 = ui1[(k * threadgroup_size + lid) * 3];
+    const uint8_t u1 = ui1[(k * threadgroup_size + lid) * 3 + 1];
+    const uint8_t u2 = ui1[(k * threadgroup_size + lid) * 3 + 2];
+    destination[(k * threadgroup_size + lid) * 4] = palette[u0 >> 2];
+    destination[(k * threadgroup_size + lid) * 4 + 1] = palette[((u0 & 3) << 4) | (u1 >> 4)];
+    destination[(k * threadgroup_size + lid) * 4 + 2] = palette[((u1 & 15) << 2) | (u2 >> 6)];
+    destination[(k * threadgroup_size + lid) * 4 + 3] = palette[u2 & 63];
+  }
+}
+    )";
+  } else if (hash.qbits == 8) {
+    shader = R"(
+#include <metal_stdlib>
+using namespace metal;
+
+kernel void depalettize(
+  device uchar *source [[buffer(0)]],
+  device real *destination [[buffer(1)]],
+
+  uint3 tgid [[threadgroup_position_in_grid]],
+  ushort lid [[thread_index_in_threadgroup]]
+) {
+  device const uchar *ui0 = source + (sizeof(real) * palette_size + number_in_blocks) * tgid.y;
+  threadgroup real palette[palette_size];
+  if (lid < palette_size) {
+    palette[lid] = ((device real*)ui0)[lid];
+  }
+  threadgroup_barrier(mem_flags::mem_threadgroup);
+  destination += number_in_blocks * tgid.y + tgid.x * threadgroup_size * num_repeats * 4;
+  device const uint *ui1 = (device uint*)(ui0 + sizeof(real) * palette_size) + tgid.x * threadgroup_size * num_repeats;
+  #pragma clang loop unroll(full)
+  for (uint k = 0; k < num_repeats; k++)
+  {
+    const uint u0 = ui1[k * threadgroup_size + lid];
+    destination[(k * threadgroup_size + lid) * 4] = palette[u0 & 0xff];
+    destination[(k * threadgroup_size + lid) * 4 + 1] = palette[(u0 >> 8) & 0xff];
+    destination[(k * threadgroup_size + lid) * 4 + 2] = palette[(u0 >> 16) & 0xff];
+    destination[(k * threadgroup_size + lid) * 4 + 3] = palette[u0 >> 24];
+  }
+}
+    )";
+  }
+  
+  std::string defines = "";
+  if (hash.data_type == MTL::DataTypeFloat) {
+    defines += std::string("typedef float real;");
+    defines += "\n";
+  } else {
+    defines += std::string("typedef half real;");
+    defines += "\n";
+  }
+  
+  uint16_t threadgroup_size = 256;
+  defines += "constant ushort threadgroup_size = ";
+  defines += std::to_string(threadgroup_size) + ";";
+  defines += "\n";
+  this->group_size = MTL::Size(threadgroup_size, 1, 1);
+
+  if (hash.qbits == 6) {
+    defines += "constant ushort palette_size = 64;\n";
+
+    defines += "constant uint number_in_blocks = ";
+    defines += std::to_string(hash.number_in_blocks / 4) + ";";
+    defines += "\n";
+    const int num_blocks = hash.length / hash.number_in_blocks;
+    const int repeat_4 = hash.number_in_blocks / (256 * 4);
+
+    defines += "constant uint num_repeats = ";
+    defines += std::to_string(1) + ";";
+    defines += "\n";
+    this->grid_size = MTL::Size(repeat_4, num_blocks, 1);
+  } else if (hash.qbits == 8) {
+    defines += "constant ushort palette_size = 256;\n";
+
+    defines += "constant uint number_in_blocks = ";
+    defines += std::to_string(hash.number_in_blocks) + ";";
+    defines += "\n";
+    const int num_blocks = hash.length / hash.number_in_blocks;
+    const int repeat_4 = hash.number_in_blocks / (256 * 4 * 2);
+
+    defines += "constant uint num_repeats = ";
+    defines += std::to_string(2) + ";";
+    defines += "\n";
+    this->grid_size = MTL::Size(repeat_4, num_blocks, 1);
+  }
+  
+  auto constants = NS::TransferPtr(MTL::FunctionConstantValues::alloc()->init());
+  NS::SharedPtr<MTL::ComputePipelineState>* pso = &depalettize_pso;
+
+  std::string source = defines;
+  if (METAL_LOG_LEVEL(context) >= 4) {
+    std::cerr << source << std::endl;
+  }
+  source += shader;
+
+  NS::Error *error = nullptr;
+  auto swift_source = NS::String::string(source.c_str(),
+  NS::UTF8StringEncoding);
+  auto library = NS::TransferPtr(context->device->newLibrary(swift_source, nullptr, &error));
+  if (!library) {
+    CCV_NNC_MFA_CHECK_ERROR(error)
+  }
+    
+  auto swift_name = NS::String::string("depalettize", NS::UTF8StringEncoding);
+  auto function = NS::TransferPtr(library->newFunction(swift_name, constants.get(), &error));
+  if (!function) {
+    CCV_NNC_MFA_CHECK_ERROR(error)
+  }
+    
+  *pso = NS::TransferPtr(context->device->newComputePipelineState(function.get(), &error));
+  if (!*pso) {
+    CCV_NNC_MFA_CHECK_ERROR(error)
+  }
+  
+  pool->drain();
+}
diff --git a/lib/nnc/mfa/ccv_nnc_mfa_depalettize.hpp b/lib/nnc/mfa/ccv_nnc_mfa_depalettize.hpp
new file mode 100644
index 000000000..4257ac536
--- /dev/null
+++ b/lib/nnc/mfa/ccv_nnc_mfa_depalettize.hpp
@@ -0,0 +1,66 @@
+#ifndef GUARD_ccv_nnc_mfa_depalettize_hpp
+#define GUARD_ccv_nnc_mfa_depalettize_hpp
+
+typedef struct {
+  uint64_t data_type;
+  int qbits;
+  int number_in_blocks;
+  uint64_t length;
+} ccv_nnc_mfa_depalettize_params_t;
+
+#ifdef __cplusplus
+#include "nnc/mfa/3rdparty/metal-cpp/Dispatch.hpp"
+#include "nnc/mfa/3rdparty/metal-cpp/Metal.hpp"
+#include <simd/simd.h>
+
+namespace ccv {
+namespace nnc {
+namespace mfa {
+namespace depalettize {
+
+class hash {
+public:
+  uint64_t data_type;
+  int qbits;
+  int number_in_blocks;
+  uint64_t length;
+
+  hash(ccv_nnc_mfa_depalettize_params_t);
+  
+  bool operator==(const hash& rhs) const;
+};
+
+class pipeline {
+public:
+  NS::SharedPtr<MTL::ComputePipelineState> depalettize_pso;
+  
+  MTL::Size grid_size;
+  MTL::Size group_size;
+  
+  pipeline(context* context, hash hash);
+};
+
+} // namespace depalettize
+} // namespace mfa
+} // namespace nnc
+} // namespace ccv
+
+std::ostream& operator<<(std::ostream& os, const ccv::nnc::mfa::depalettize::hash& hash);
+
+template<>
+struct std::hash<ccv::nnc::mfa::depalettize::hash>
+{
+  std::size_t operator()(const ccv::nnc::mfa::depalettize::hash& hash) const noexcept;
+};
+
+extern "C" {
+#endif // __cplusplus
+
+void ccv_nnc_mfa_prepare_depalettize(ccv_nnc_mfa_context_t* context, ccv_nnc_mfa_depalettize_params_t params);
+void ccv_nnc_mfa_encode_depalettize(ccv_nnc_mfa_context_t* context, ccv_nnc_mfa_depalettize_params_t params, mtl_command_batch_t* command_batch, mtl_buffer_t** tensors, size_t* tensor_offsets);
+
+#ifdef __cplusplus
+} // extern "C"
+#endif // __cplusplus
+
+#endif
diff --git a/lib/nnc/mfa/makefile b/lib/nnc/mfa/makefile
index c7efef61f..0d3a76c95 100644
--- a/lib/nnc/mfa/makefile
+++ b/lib/nnc/mfa/makefile
@@ -2,7 +2,7 @@ include ../../config.mk
 
 CFLAGS := -std=c++17 -O3 -Wall -I"../../" $(CFLAGS)
 
-SRCS := Metal.cpp ccv_nnc_mfa.cpp ccv_nnc_mfa_attention.cpp ccv_nnc_mfa_error.cpp ccv_nnc_mfa_gemm.cpp ccv_nnc_mfa_normalization.cpp 3rdparty/metal-cpp/Dispatch.cpp
+SRCS := Metal.cpp ccv_nnc_mfa.cpp ccv_nnc_mfa_attention.cpp ccv_nnc_mfa_error.cpp ccv_nnc_mfa_gemm.cpp ccv_nnc_mfa_normalization.cpp ccv_nnc_mfa_depalettize.cpp 3rdparty/metal-cpp/Dispatch.cpp
 
 SRC_OBJS := $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(SRCS)))
 
diff --git a/lib/nnc/mps/ccv_nnc_mps.h b/lib/nnc/mps/ccv_nnc_mps.h
index 42d88e2e6..b8177d715 100644
--- a/lib/nnc/mps/ccv_nnc_mps.h
+++ b/lib/nnc/mps/ccv_nnc_mps.h
@@ -32,6 +32,7 @@ void ccv_nnc_deinit_stream_signal(ccv_nnc_stream_signal_t* const signal);
 CCV_WARN_UNUSED(int) ccv_nnc_gpu_device_count(void);
 void ccv_nnc_mps_unbounded_command_buffers(int state);
 void ccv_nnc_mps_clear_graph_executable_cache(void);
+void ccv_nnc_mps_depalettize(const void* input, const int datatype, const size_t input_length, const int qbits, const int number_in_blocks, void* output, const size_t output_length, void* const command_buffer);
 
 #ifdef __OBJC__
 
diff --git a/lib/nnc/mps/ccv_nnc_palettize.m b/lib/nnc/mps/ccv_nnc_palettize.m
new file mode 100644
index 000000000..69f37b7b5
--- /dev/null
+++ b/lib/nnc/mps/ccv_nnc_palettize.m
@@ -0,0 +1,44 @@
+#include "ccv_nnc_mps.h"
+#include "ccv_internal.h"
+#include "nnc/ccv_nnc_internal.h"
+#include "nnc/ccv_nnc_easy.h"
+
+void ccv_nnc_mps_depalettize(const void* input, const int datatype, const size_t input_length, const int qbits, const int number_in_blocks, void* output, const size_t output_length, void* const command_buffer)
+{
+	uint32_t mtl_data_type = UINT32_MAX;
+	switch (datatype) {
+		case CCV_16F: {
+			mtl_data_type = 16;
+			break;
+		}
+		case CCV_32F: {
+			mtl_data_type = 3;
+			break;
+		}
+		default: {
+			break;
+		}
+	}
+	ccv_nnc_mfa_depalettize_params_t params = {
+		.data_type = mtl_data_type,
+		.qbits = (uint32_t)qbits,
+		.number_in_blocks = (uint32_t)number_in_blocks,
+		.length = (uint64_t)output_length,
+	};
+	ccv_nnc_mfa_context_t* context = ccv_nnc_default_mfa_context();
+
+	ccv_nnc_mfa_prepare_depalettize(context, params);
+
+	mtl_command_batch_t* command_batch = ccv_nnc_stream_context_start_command_batch(0);
+	mtl_buffer_t* tensors[3] = {
+		(mtl_buffer_t*)input, // A
+		(mtl_buffer_t*)output, // B
+		NULL,
+	};
+	size_t tensor_offsets[2] = {
+		0, // A offset
+		0, // B offset
+	};
+	ccv_nnc_mfa_encode_depalettize(context, params, command_batch, tensors, tensor_offsets);
+	ccv_nnc_stream_context_finish_command_batch(0, command_batch);
+}
diff --git a/lib/nnc/mps/makefile b/lib/nnc/mps/makefile
index cbba0a9c9..a5e41211f 100644
--- a/lib/nnc/mps/makefile
+++ b/lib/nnc/mps/makefile
@@ -2,7 +2,7 @@ include ../../config.mk
 
 CFLAGS := -O3 -Wall -I"../../" $(CFLAGS)
 
-SRCS := ccv_nnc_mps.m
+SRCS := ccv_nnc_mps.m ccv_nnc_palettize.m
 
 SRC_OBJS := $(patsubst %.c,%.o,$(patsubst %.m,%.o,$(SRCS)))
 
diff --git a/test/int/nnc/palettize.tests.c b/test/int/nnc/palettize.tests.c
index 4bc15df15..d855ca2f9 100644
--- a/test/int/nnc/palettize.tests.c
+++ b/test/int/nnc/palettize.tests.c
@@ -647,27 +647,27 @@ TEST_CASE("quantize float to 6-bit and dequantize on GPU losslessly, fast path")
 
 TEST_CASE("quantize half-precision to 6-bit and dequantize on GPU losslessly, fast path")
 {
-	GUARD_ELSE_RETURN(ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_GPU_REF));
+	GUARD_ELSE_RETURN(ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_GPU_REF) || ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_MPS));
 	float lut_f32[64];
 	int i;
 	for (i = 0; i < 64; i++)
 		lut_f32[i] = (float)i;
 	uint16_t lut[64];
 	ccv_float_to_half_precision(lut_f32, lut, 64);
-	uint16_t* const values = ccmalloc(sizeof(uint16_t) * 2840);
-	for (i = 0; i < 2840; i++)
+	uint16_t* const values = ccmalloc(sizeof(uint16_t) * 8192);
+	for (i = 0; i < 8192; i++)
 		values[i] = lut[i % 64];
-	ccv_nnc_tensor_t* tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, (2130 + 6 * 64 * 2 + 3) / 4), 0);
+	ccv_nnc_tensor_t* tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, (6144 + 2 * 64 * 2 + 3) / 4), 0);
 	uint8_t* compressed = tensor->data.u8;
-	const size_t output_size = ccv_nnc_palettize(values, CCV_16F, CCV_TENSOR_CPU_MEMORY, 2840, 6, 512, compressed, 2130 + 6 * 64 * 2);
-	REQUIRE_EQ(output_size, 2130 + 6 * 64 * 2, "output size should match");
-	ccv_nnc_tensor_t* g_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, (2130 + 6 * 64 * 2 + 3) / 4), 0);
+	const size_t output_size = ccv_nnc_palettize(values, CCV_16F, CCV_TENSOR_CPU_MEMORY, 8192, 6, 4096, compressed, 6144 + 2 * 64 * 2);
+	REQUIRE_EQ(output_size, 6144 + 2 * 64 * 2, "output size should match");
+	ccv_nnc_tensor_t* g_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, (6144 + 2 * 64 * 2 + 3) / 4), 0);
 	ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD(), ccv_nnc_no_hint, 0, TENSOR_LIST(tensor), TENSOR_LIST(g_tensor), 0);
-	ccv_nnc_tensor_t* gv_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 16F, 2840), 0);
-	ccv_nnc_depalettize(g_tensor->data.u8, CCV_16F, CCV_TENSOR_GPU_MEMORY, output_size, 6, 512, gv_tensor->data.u8, 2840);
-	ccv_nnc_tensor_t* v_tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(16F, 2840), 0);
+	ccv_nnc_tensor_t* gv_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 16F, 8192), 0);
+	ccv_nnc_depalettize(g_tensor->data.u8, CCV_16F, CCV_TENSOR_GPU_MEMORY, output_size, 6, 4096, gv_tensor->data.u8, 8192);
+	ccv_nnc_tensor_t* v_tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(16F, 8192), 0);
 	ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD(), ccv_nnc_no_hint, 0, TENSOR_LIST(gv_tensor), TENSOR_LIST(v_tensor), 0);
-	REQUIRE_ARRAY_EQ(uint16_t, values, v_tensor->data.f16, 2840, "should be lossless");
+	REQUIRE_ARRAY_EQ(uint16_t, values, v_tensor->data.f16, 8192, "should be lossless");
 	ccfree(values);
 	ccv_nnc_tensor_free(tensor);
 	ccv_nnc_tensor_free(g_tensor);
@@ -791,25 +791,25 @@ TEST_CASE("quantize double to 8-bit and dequantize on GPU losslessly, fast path"
 
 TEST_CASE("quantize float to 8-bit and dequantize on GPU losslessly, fast path")
 {
-	GUARD_ELSE_RETURN(ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_GPU_REF));
+	GUARD_ELSE_RETURN(ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_GPU_REF) || ccv_nnc_cmd_ok(CCV_NNC_DATA_TRANSFER_FORWARD, CCV_NNC_BACKEND_MPS));
 	float lut[256];
 	int i;
 	for (i = 0; i < 256; i++)
 		lut[i] = (float)i;
-	float* const values = ccmalloc(sizeof(float) * 2840);
-	for (i = 0; i < 2840; i++)
+	float* const values = ccmalloc(sizeof(float) * 8192);
+	for (i = 0; i < 8192; i++)
 		values[i] = lut[i % 256];
-	ccv_nnc_tensor_t* tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, (2840 + 3 * 256 * 4 + 3) / 4), 0);
+	ccv_nnc_tensor_t* tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, (8192 + 2 * 256 * 4 + 3) / 4), 0);
 	uint8_t* compressed = tensor->data.u8;
-	const size_t output_size = ccv_nnc_palettize(values, CCV_32F, CCV_TENSOR_CPU_MEMORY, 2840, 8, 1280, compressed, 2840 + 3 * 256 * 4);
-	REQUIRE_EQ(output_size, 2840 + 3 * 256 * 4, "output size should match");
-	ccv_nnc_tensor_t* g_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, (2840 + 3 * 256 * 4 + 3) / 4), 0);
+	const size_t output_size = ccv_nnc_palettize(values, CCV_32F, CCV_TENSOR_CPU_MEMORY, 8192, 8, 4096, compressed, 8192 + 2 * 256 * 4);
+	REQUIRE_EQ(output_size, 8192 + 2 * 256 * 4, "output size should match");
+	ccv_nnc_tensor_t* g_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, (8192 + 2 * 256 * 4 + 3) / 4), 0);
 	ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD(), ccv_nnc_no_hint, 0, TENSOR_LIST(tensor), TENSOR_LIST(g_tensor), 0);
-	ccv_nnc_tensor_t* gv_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, 2840), 0);
-	ccv_nnc_depalettize(g_tensor->data.u8, CCV_32F, CCV_TENSOR_GPU_MEMORY, output_size, 8, 1280, gv_tensor->data.u8, 2840);
-	ccv_nnc_tensor_t* v_tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, 2840), 0);
+	ccv_nnc_tensor_t* gv_tensor = ccv_nnc_tensor_new(0, GPU_TENSOR_NCHW(000, 32F, 8192), 0);
+	ccv_nnc_depalettize(g_tensor->data.u8, CCV_32F, CCV_TENSOR_GPU_MEMORY, output_size, 8, 4096, gv_tensor->data.u8, 8192);
+	ccv_nnc_tensor_t* v_tensor = ccv_nnc_tensor_new(0, CPU_TENSOR_NCHW(32F, 8192), 0);
 	ccv_nnc_cmd_exec(CMD_DATA_TRANSFER_FORWARD(), ccv_nnc_no_hint, 0, TENSOR_LIST(gv_tensor), TENSOR_LIST(v_tensor), 0);
-	REQUIRE_ARRAY_EQ(float, values, v_tensor->data.f32, 2840, "should be lossless");
+	REQUIRE_ARRAY_EQ(float, values, v_tensor->data.f32, 8192, "should be lossless");
 	ccfree(values);
 	ccv_nnc_tensor_free(tensor);
 	ccv_nnc_tensor_free(g_tensor);