IQ4_NL_X4 (#118)

* Adding iq4_nl_x4

Looks very promising - I get PP-512(LLaMA-3.1-8B) = 230 t/s
on the Ryzen-7950X! This is faster than any other quant and
~40% faster than iq4_nl.

* iq4_nl_x4: getting amazing

This Zen4 variant gets us to PP-512(LLaMA-3.1-8B) = 263 t/s!

* iq4_nl_x4: AVX2

Here we gain only 25% compared to iq4_nl

* iq4_nl_x4: NEON

On M2-Max we get PP-512(LLaMA-3.1-8B) = 109.7 t/s, up from
82.4 t/s for iq4_nl.

* iq4_nl_x4: minor NEON improvement and cleanup

This gets us to 110.3 t/s. In comparison,
IQ4_NL_4_4 in mainline llama.cpp achieves 92.3 t/s.

* iq4_nl_x4: NEON specialization for matrix x vector

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

10 files changed, 398 insertions, 13 deletions

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index b5907e2b..333fae36 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -40,6 +40,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q3_K_M",   LLAMA_FTYPE_MOSTLY_Q3_K_M,   " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
     { "Q3_K_L",   LLAMA_FTYPE_MOSTLY_Q3_K_L,   " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
     { "IQ4_NL",   LLAMA_FTYPE_MOSTLY_IQ4_NL,   " 4.50 bpw non-linear quantization", },
+    { "IQ4_NL_X4",LLAMA_FTYPE_MOSTLY_IQ4_NL_X4," 4.50 bpw non-linear quantization", },
     { "IQ4_XS",   LLAMA_FTYPE_MOSTLY_IQ4_XS,   " 4.25 bpw non-linear quantization", },
     { "IQ4_KS",   LLAMA_FTYPE_MOSTLY_IQ4_KS,   " 4.25 bpw non-linear quantization", },
     { "IQ4_KSS",  LLAMA_FTYPE_MOSTLY_IQ4_KSS,  " 4.0 bpw non-linear quantization",  },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 8980285f..dabb2264 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -406,6 +406,8 @@ extern "C" {
         GGML_TYPE_IQ4_KS  = 144,
         GGML_TYPE_IQ2_KS  = 145,
         GGML_TYPE_IQ4_KSS = 146,
+
+        GGML_TYPE_IQ4_NL_X4 = 220,
         GGML_TYPE_COUNT,
     };
 
@@ -464,6 +466,8 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_KS  = 137, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_KS  = 138, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ4_KSS = 139, // except 1d tensors
+                                         //
+        GGML_FTYPE_MOSTLY_IQ4_NL_X4 = 219, // except 1d tensors
     };
 
     // available tensor operations:
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index f0c1ae68..2af3323d 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -419,6 +419,11 @@ typedef struct {
     uint8_t qs[QK4_NL/2];
 } block_iq4_nl;
 static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
+typedef struct {
+    ggml_half d[4];
+    uint8_t qs[2*QK4_NL];
+} block_iq4_nl_x4;
+static_assert(sizeof(block_iq4_nl_x4) == 4*sizeof(ggml_half) + 2*QK4_NL, "wrong iq4_nl_x4 block size/padding");
 
 typedef struct {
     ggml_half d;
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index d18b1981..376c97f8 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -15196,6 +15196,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_IQ6_K: break;
         case GGML_TYPE_IQ4_KS: break;
         case GGML_TYPE_IQ4_KSS: break;
+        case GGML_TYPE_IQ4_NL_X4: break;
         case GGML_TYPE_Q4_0_4_4:
         case GGML_TYPE_Q4_0_4_8:
             {
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 39218ff4..c975212e 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1245,6 +1245,23 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_IQ4_NL_X4] = {
+        .type_name                = "iq4_nl_x4",
+        .blck_size                = QK4_NL,
+        .type_size                = sizeof(block_iq4_nl),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq4_nl_x4,
+        .from_float               = quantize_row_iq4_nl_x4,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq4_nl_x4_ref,
+        .vec_dot                  = vec_dot_iq4_nl_x4_q8_0,
+#if GGML_USE_IQK_MULMAT && defined __AVX2__
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+#else
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+#endif
+        .nrows                    = 1,
+        .row_meta_size            = 0,
+    },
 };
 
 // For internal test use
@@ -3903,6 +3920,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ1_BN:        wtype = GGML_TYPE_IQ1_BN;   break;
         case GGML_FTYPE_MOSTLY_IQ2_BN:        wtype = GGML_TYPE_IQ2_BN;   break;
         case GGML_FTYPE_MOSTLY_IQ4_NL:        wtype = GGML_TYPE_IQ4_NL;   break;
+        case GGML_FTYPE_MOSTLY_IQ4_NL_X4:     wtype = GGML_TYPE_IQ4_NL_X4;break;
         case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ4_KS:        wtype = GGML_TYPE_IQ4_KS;   break;
         case GGML_FTYPE_MOSTLY_IQ4_KSS:       wtype = GGML_TYPE_IQ4_KSS;  break;
@@ -10426,6 +10444,7 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -10868,6 +10887,7 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -11007,6 +11027,7 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -14192,6 +14213,7 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -14571,6 +14593,7 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -14844,6 +14867,7 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -15444,6 +15468,7 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ1_BN:
         case GGML_TYPE_IQ2_BN:
         case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_NL_X4:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
@@ -22270,6 +22295,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ1_BN:  result = quantize_iq1_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_BN:  result = quantize_iq2_bn (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ4_NL_X4: result = quantize_iq4_nl_x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_KS:  result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index d7682e54..cfda9e18 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -23,6 +23,7 @@
 #include "ggml-impl.h"
 #include "ggml-quants.h"
 #include "iqk_mul_mat.h"
+#include "iqk_quantize.h"
 
 #define GGML_COMMON_IMPL_C
 #include "ggml-common.h"
@@ -87,6 +88,16 @@ struct DataInfo {
     inline void store(int ix, int iy, float result) const {
         *(dst_row(iy) + ix) = result;
     }
+#ifdef __AVX__
+    inline void store(int ix, int iy, __m128 result) const {
+        _mm_storeu_ps(dst_row(iy) + ix, result);
+    }
+#endif
+#ifdef __ARM_NEON
+    inline void store(int ix, int iy, float32x4_t result) const {
+        vst1q_f32(dst_row(iy) + ix, result);
+    }
+#endif
     inline float * dst_row(int iy) const {
         if (!row_mapping) return s + (cur_y + iy)*bs;
         int i12 = row_mapping[cur_y + iy].i2;
@@ -2068,6 +2079,112 @@ static void mul_mat_qX_K_q8_K_T(int n, const void * vx, size_t bx, const DataInf
 
 #endif  // Zen4 or vanilla AVX2
 
+#ifdef HAVE_FANCY_SIMD
+template <int nrc_y>
+static void mul_mat_iq4_nl_x4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%8 == 0);
+    Q8<nrc_y, block_q8_1_x4> q8(info);
+    auto m4 = _mm512_set1_epi8(0xf);
+    auto values = load_iq4nl_values_512();
+    int nb = n / QK4_NL;
+    GGML_ASSERT(nb%4 == 0);
+    __m512  acc[2*nrc_y] = {};
+    __m512i qx[4];
+    for (int ix = 0; ix < nrc_x; ix += 8) {
+        const block_iq4_nl_x4 * iq4l = (const block_iq4_nl_x4 *)((const char *)vx + (ix+0)*bx);
+        const block_iq4_nl_x4 * iq4h = (const block_iq4_nl_x4 *)((const char *)vx + (ix+4)*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4l[4*ib4+k].d));
+                auto scales1 = _mm256_set_m128(scales128, scales128);
+                scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4h[4*ib4+k].d));
+                auto scales2 = _mm256_set_m128(scales128, scales128);
+                auto scales = _mm512_insertf32x8(_mm512_castps256_ps512(scales1), scales2, 1);
+                auto scales_m = _mm512_mul_ps(scales, _mm512_set1_ps(-64.f));
+                auto bits1 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[4*ib4+k].qs+0)),
+                                                                       _mm256_loadu_si256((const __m256i *)iq4h[4*ib4+k].qs+0), 1);
+                auto bits2 = _mm512_inserti32x8(_mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)iq4l[4*ib4+k].qs+1)),
+                                                                       _mm256_loadu_si256((const __m256i *)iq4h[4*ib4+k].qs+1), 1);
+                qx[0] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits1, m4));
+                qx[1] = _mm512_shuffle_epi8(values, _mm512_and_si512(bits2, m4));
+                qx[2] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits1, 4), m4));
+                qx[3] = _mm512_shuffle_epi8(values, _mm512_and_si512(_mm512_srli_epi16(bits2, 4), m4));
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y8 = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
+                    auto y = _mm512_inserti32x8(_mm512_castsi256_si512(y8), y8, 1);
+                    auto sumi = _mm512_setzero_si512();
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[0], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x00)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[1], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0x55)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[2], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xaa)));
+                    sumi = _mm512_dpbusd_epi32(sumi, qx[3], _mm512_shuffle_epi32(y, _MM_PERM_ENUM(0xff)));
+                    auto dy = _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k]));
+                    acc[2*iy+0] = _mm512_fmadd_ps(_mm512_mul_ps(scales, dy), _mm512_cvtepi32_ps(sumi), acc[2*iy+0]);
+                    acc[2*iy+1] = _mm512_fmadd_ps(scales_m, _mm512_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[2*iy+1]);
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            auto sum512 = _mm512_add_ps(acc[2*iy+0], acc[2*iy+1]);
+            acc[2*iy+0] = acc[2*iy+1] = _mm512_setzero_ps();
+            auto sum1 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 0), _mm512_extractf32x4_ps(sum512, 1));
+            auto sum2 = _mm_add_ps(_mm512_extractf32x4_ps(sum512, 2), _mm512_extractf32x4_ps(sum512, 3));
+            info.store(ix+0, iy, sum1);
+            info.store(ix+4, iy, sum2);
+        }
+    }
+}
+#else
+template <int nrc_y>
+static void mul_mat_iq4_nl_x4_q8_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<nrc_y, block_q8_1_x4> q8(info);
+    auto m4 = _mm256_set1_epi8(0xf);
+    auto m1 = _mm256_set1_epi16(1);
+    auto values = load_iq4nl_values_256();
+    int nb = n / QK4_NL;
+    GGML_ASSERT(nb%4 == 0);
+    __m256 acc[nrc_y] = {};
+    //__m256 acc[2*nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales128 = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq4[4*ib4+k].d));
+                auto scales = _mm256_set_m128(scales128, scales128);
+                auto scales_m = _mm256_mul_ps(scales, _mm256_set1_ps(-64.f));
+                auto bits1 = _mm256_loadu_si256((const __m256i *)iq4[4*ib4+k].qs+0);
+                auto bits2 = _mm256_loadu_si256((const __m256i *)iq4[4*ib4+k].qs+1);
+                auto q1 = _mm256_shuffle_epi8(values, _mm256_and_si256(bits1, m4));
+                auto q2 = _mm256_shuffle_epi8(values, _mm256_and_si256(bits2, m4));
+                auto q3 = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits1, 4), m4));
+                auto q4 = _mm256_shuffle_epi8(values, _mm256_and_si256(_mm256_srli_epi16(bits2, 4), m4));
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ib4].qs+k);
+                    auto sumi1 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q1, _mm256_shuffle_epi32(y, 0x00))),
+                                                  _mm256_madd_epi16(m1, _mm256_maddubs_epi16(q2, _mm256_shuffle_epi32(y, 0x55))));
+                    auto sumi2 = _mm256_add_epi32(_mm256_madd_epi16(m1, _mm256_maddubs_epi16(q3, _mm256_shuffle_epi32(y, 0xaa))),
+                                                  _mm256_madd_epi16(m1, _mm256_maddubs_epi16(q4, _mm256_shuffle_epi32(y, 0xff))));
+                    auto d4d8 = _mm256_mul_ps(scales, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
+                    acc[iy] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[iy]);
+                    acc[iy] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[iy]);
+                    //acc[2*iy+0] = _mm256_fmadd_ps(d4d8, _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), acc[2*iy+0]);
+                    //acc[2*iy+1] = _mm256_fmadd_ps(scales_m, _mm256_set1_ps(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k+4])), acc[2*iy+1]);
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            //auto sum256 = _mm256_add_ps(acc[2*iy+0], acc[2*iy+1]);
+            //acc[2*iy+0] = acc[2*iy+1] = _mm256_setzero_ps();
+            //auto sum = _mm_add_ps(_mm256_castps256_ps128(sum256), _mm256_extractf128_ps(sum256, 1));
+            //info.store(ix, iy, sum);
+            auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
+            info.store(ix, iy, sum);
+            acc[iy] = _mm256_setzero_ps();
+        }
+    }
+}
+#endif
+
 template <typename Bits>
 inline void multiply_add_1(int j, const Bits& bits, const __m256i * scales, const __m256i * q8, __m256i * sumi) {
     if (j == 0) {
@@ -4025,6 +4142,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
             MulMat::set_functions<IQ4_NL_Unpacker>(mm);
             expected_typeB = GGML_TYPE_Q8_1;
             break;
+        case GGML_TYPE_IQ4_NL_X4:
+            assert (ne00 % QK4_NL == 0);
+            mm.funcs[0] = mul_mat_iq4_nl_x4_q8_1<1>;
+            mm.funcs[1] = mul_mat_iq4_nl_x4_q8_1<2>;
+            mm.funcs[2] = mul_mat_iq4_nl_x4_q8_1<3>;
+            mm.funcs[3] = mul_mat_iq4_nl_x4_q8_1<4>;
+            mm.funcs[4] = mul_mat_iq4_nl_x4_q8_1<5>;
+            mm.funcs[5] = mul_mat_iq4_nl_x4_q8_1<6>;
+            mm.funcs[6] = mul_mat_iq4_nl_x4_q8_1<7>;
+            mm.funcs[7] = mul_mat_iq4_nl_x4_q8_1<8>;
+            expected_typeB = GGML_TYPE_Q8_1;
+            break;
 
         default:
             return false;
@@ -6427,6 +6556,96 @@ static void mul_mat_iq2bn_q8_K64(int n, const void * vx, size_t bx, const DataIn
     }
 }
 
+template <int nrc_y>
+void mul_mat_iq4_nl_x4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<nrc_y, block_q8_0_x4> q8(info);
+    auto m4 = vdupq_n_u8(0xf);
+    auto values = vld1q_s8(iq4k_values);
+    int nb = n / QK4_NL;
+    GGML_ASSERT(nb%4 == 0);
+    int8x16_t qx[8];
+    float32x4_t acc[nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            for (int k = 0; k < 4; ++k) {
+                auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d));
+                auto bits   = vld1q_u8_x4(iq4[4*ib4+k].qs);
+                qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4));   //  0...3 from the 4 rows
+                qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4));   // 16..19
+                qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4));   //  4...7
+                qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4));   // 20..23
+                qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4));  //  8..11
+                qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4));  // 24..27
+                qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4));  // 12..15
+                qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4));  // 28..31
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = vld1q_s8_x2(q8.y[iy][ib4].qs+32*k);
+                    auto sumi = vdupq_n_s32(0);
+                    sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
+                    sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
+                    sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
+                    sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
+                    sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
+                    sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
+                    sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
+                    sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
+                    auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
+                    acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            info.store(ix, iy, acc[iy]);
+            acc[iy] = vdupq_n_f32(0.f);
+        }
+    }
+}
+
+void mul_mat_iq4_nl_x4_q8_0_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<1, block_q8_0_x4> q8(info);
+    auto m4 = vdupq_n_u8(0xf);
+    auto values = vld1q_s8(iq4k_values);
+    int nb = n / QK4_NL;
+    GGML_ASSERT(nb%4 == 0);
+    int8x16_t qx[8];
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        auto acc = vdupq_n_f32(0.f);
+        const block_iq4_nl_x4 * iq4 = (const block_iq4_nl_x4 *)((const char *)vx + ix*bx);
+        for (int ib4 = 0; ib4 < nb/4; ++ib4) {
+            auto y1 = vld1q_s8_x4(q8.y[0][ib4].qs);
+            auto y2 = vld1q_s8_x4(q8.y[0][ib4].qs+64);
+            for (int k = 0; k < 4; ++k) {
+                auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq4[4*ib4+k].d));
+                auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[0][ib4].d[k])));
+                auto sumi = vdupq_n_s32(0);
+                const auto yval = k < 2 ? y1.val + 2*k : y2.val + 2*(k-2);
+                auto bits   = vld1q_u8_x4(iq4[4*ib4+k].qs);
+                qx[0] = vqtbl1q_s8(values, vandq_u8(bits.val[0], m4));   //  0...3 from the 4 rows
+                qx[1] = vqtbl1q_s8(values, vandq_u8(bits.val[1], m4));   // 16..19
+                sumi = vdotq_laneq_s32(sumi, qx[0], yval[0], 0);
+                sumi = vdotq_laneq_s32(sumi, qx[1], yval[1], 0);
+                qx[2] = vqtbl1q_s8(values, vandq_u8(bits.val[2], m4));   //  4...7
+                qx[3] = vqtbl1q_s8(values, vandq_u8(bits.val[3], m4));   // 20..23
+                sumi = vdotq_laneq_s32(sumi, qx[2], yval[0], 1);
+                sumi = vdotq_laneq_s32(sumi, qx[3], yval[1], 1);
+                qx[4] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[0], 4));  //  8..11
+                qx[5] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[1], 4));  // 24..27
+                sumi = vdotq_laneq_s32(sumi, qx[4], yval[0], 2);
+                sumi = vdotq_laneq_s32(sumi, qx[5], yval[1], 2);
+                qx[6] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[2], 4));  // 12..15
+                qx[7] = vqtbl1q_s8(values, vshrq_n_u8(bits.val[3], 4));  // 28..31
+                sumi = vdotq_laneq_s32(sumi, qx[6], yval[0], 3);
+                sumi = vdotq_laneq_s32(sumi, qx[7], yval[1], 3);
+                acc = vfmaq_f32(acc, d4d8, vcvtq_f32_s32(sumi));
+            }
+        }
+        info.store(ix, 0, acc);
+    }
+}
+
 template <typename Dequantizer> void MulMat::set_functions(MulMat& m) {
     if constexpr (std::is_same_v<Dequantizer, DequantizerQ40> || std::is_same_v<Dequantizer, DequantizerQ50> ||
                   std::is_same_v<Dequantizer, DequantizerQ80> || std::is_same_v<Dequantizer, DequantizerIQ4NL> ||
@@ -6596,6 +6815,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
             MulMat::set_functions<DequantizerIQ4NL>(m);
             expected_Btype = GGML_TYPE_Q8_0;
             break;
+        case GGML_TYPE_IQ4_NL_X4:
+            m.funcs[0] = mul_mat_iq4_nl_x4_q8_0_1;
+            m.funcs[1] = mul_mat_iq4_nl_x4_q8_0<2>;
+            m.funcs[2] = mul_mat_iq4_nl_x4_q8_0<3>;
+            m.funcs[3] = mul_mat_iq4_nl_x4_q8_0<4>;
+            m.funcs[4] = mul_mat_iq4_nl_x4_q8_0<5>;
+            m.funcs[5] = mul_mat_iq4_nl_x4_q8_0<6>;
+            m.funcs[6] = mul_mat_iq4_nl_x4_q8_0<7>;
+            m.funcs[7] = mul_mat_iq4_nl_x4_q8_0<8>;
+            expected_Btype = GGML_TYPE_Q8_0;
+            break;
         default:
             return false;
     }
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index b9d48237..88b5628c 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -669,12 +669,12 @@ void quantize_row_iq2_k_impl(const float * x, void * vy, int n_per_row, const fl
 }
 }
 
-void quantize_row_iq2_k_ref(const float * GGML_RESTRICT x, block_iq2_k  * GGML_RESTRICT y, int64_t k) {
+void quantize_row_iq2_k_ref(const float * x, block_iq2_k  * y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq2_k(x, (void *)y, 1, k, nullptr);
 }
 
-void quantize_row_iq2_k(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+void quantize_row_iq2_k(const float * x, void * vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq2_k * y = (block_iq2_k *)vy;
     quantize_row_iq2_k_ref(x, y, k);
@@ -692,7 +692,7 @@ size_t quantize_iq2_k(const float * src, void * dst, int64_t nrows, int64_t n_pe
     return nrows * nblock * sizeof(block_iq2_k);
 }
 
-void dequantize_row_iq2_k(const block_iq2_k  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
+void dequantize_row_iq2_k(const block_iq2_k  * x, float * y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -723,7 +723,7 @@ void dequantize_row_iq2_k(const block_iq2_k  * GGML_RESTRICT x, float * GGML_RES
 
 }
 
-void vec_dot_iq2_k_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+void vec_dot_iq2_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
     GGML_UNUSED(nrc);
@@ -967,12 +967,12 @@ void quantize_row_iq2_ks_impl(const float * x, void * vy, int n_per_row, const f
 }
 }
 
-void quantize_row_iq2_ks_ref(const float * GGML_RESTRICT x, block_iq2_ks * GGML_RESTRICT y, int64_t k) {
+void quantize_row_iq2_ks_ref(const float * x, block_iq2_ks * y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq2_ks(x, (void *)y, 1, k, nullptr);
 }
 
-void quantize_row_iq2_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+void quantize_row_iq2_ks(const float * x, void * vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq2_ks * y = (block_iq2_ks *)vy;
     quantize_row_iq2_ks_ref(x, y, k);
@@ -994,7 +994,7 @@ size_t quantize_iq2_ks(const float * src, void * dst, int64_t nrows, int64_t n_p
     return nrows * row_size;
 }
 
-void dequantize_row_iq2_ks(const block_iq2_ks  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
+void dequantize_row_iq2_ks(const block_iq2_ks  * x, float * y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -1334,7 +1334,7 @@ void dequantize_row_iq3_k(const block_iq3_k * x, float * y, int64_t k) {
     }
 }
 
-void vec_dot_iq3_k_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+void vec_dot_iq3_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
     GGML_UNUSED(nrc);
@@ -3119,4 +3119,102 @@ void vec_dot_iq4_kss_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
     GGML_UNUSED(by);
 }
 
+//
+// ========================================= x4
+//
+void quantize_row_iq4_nl_x4_ref(const float * x, block_iq4_nl_x4  * y, int64_t k) {
+    // we assume we are called with 4 rows
+    quantize_iq4_nl_x4(x, (void *)y, 4, k/4, nullptr);
+}
+
+void quantize_row_iq4_nl_x4(const float * x, void * y, int64_t k) {
+    // we assume we are called with 4 rows
+    quantize_iq4_nl_x4(x, y, 4, k/4, nullptr);
+}
+
+static void repack_iq4_nl(int nrows, int n_per_row, const block_iq4_nl * x, block_iq4_nl_x4 * y) {
+    GGML_ASSERT(nrows%4 == 0);
+    GGML_ASSERT(n_per_row%QK4_NL == 0);
+    int nblock = n_per_row/QK4_NL;
+    const block_iq4_nl * x4[4];
+    for (int row = 0; row < nrows; row += 4) {
+        for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
+        for (int ib = 0; ib < nblock; ++ib) {
+            for (int k = 0; k < 4; ++k) y[ib].d[k] = x4[k][ib].d;
+            for (int k = 0; k < 4; ++k) for (int i = 0; i < 4; ++i) {
+                y[ib].qs[4*k+i+ 0] = (x4[k][ib].qs[i+0] & 0xf) | ((x4[k][ib].qs[i+ 8] & 0x0f) << 4);  //  0....3 +  8...11 from each row
+                y[ib].qs[4*k+i+16] = (x4[k][ib].qs[i+0] >>  4) | ((x4[k][ib].qs[i+ 8] & 0xf0));       // 16...19 + 24...27 from each row
+                y[ib].qs[4*k+i+32] = (x4[k][ib].qs[i+4] & 0xf) | ((x4[k][ib].qs[i+12] & 0x0f) << 4);  //  4....7 + 12...15 from each row
+                y[ib].qs[4*k+i+48] = (x4[k][ib].qs[i+4] >>  4) | ((x4[k][ib].qs[i+12] & 0xf0));       // 20...23 + 28...31 from each row
+            }
+        }
+        x += 4*nblock;
+        y += nblock;
+    }
+}
+
+size_t quantize_iq4_nl_x4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
+    GGML_ASSERT(nrows%4 == 0);
+    auto row_size_nl = ggml_row_size(GGML_TYPE_IQ4_NL, n_per_row);
+    std::vector<char> qtmp(4*row_size_nl);
+    //std::vector<float> check1(4*n_per_row), check2(4*n_per_row);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrows; row += 4) {
+        quantize_iq4_nl(src, qtmp.data(), 4, n_per_row, imatrix);
+        repack_iq4_nl(4, n_per_row, (const block_iq4_nl *)qtmp.data(), (block_iq4_nl_x4 *)qrow);
+        //dequantize_row_iq4_nl_x4((const block_iq4_nl_x4 *)qrow, check1.data(), 4*n_per_row);
+        //dequantize_row_iq4_nl((const block_iq4_nl *)qtmp.data(), check2.data(), 4*n_per_row);
+        //for (int k = 0; k < 4; ++k) {
+        //    auto x1 = check1.data() + k*n_per_row;
+        //    auto x2 = check2.data() + k*n_per_row;
+        //    int nbad = 0;
+        //    for (int j = 0; j < n_per_row; ++j) {
+        //        if (std::abs(x1[j] - x2[j]) > 1e-8) {
+        //            printf("Oops: %g vs %g\n", x1[j], x2[j]);
+        //            if (++nbad > 20) GGML_ABORT("fatal error");
+        //        }
+        //    }
+        //}
+        src += 4*n_per_row;
+        qrow += 4*row_size_nl;
+    }
+    return nrows*row_size_nl;
+}
+
+void dequantize_row_iq4_nl_x4(const block_iq4_nl_x4 * x, float * y, int64_t k) {
+    // we assume we are called with 4 rows
+    int n_per_row = k/4;
+    int nb = n_per_row/QK4_NL;
+    float * yk[4];
+    for (int k = 0; k < 4; ++k) yk[k] = y + k*n_per_row;
+    for (int ib = 0; ib < nb; ++ib) {
+        for (int k = 0; k < 4; ++k) {
+            float scale = GGML_FP16_TO_FP32(x[ib].d[k]);
+            for (int i = 0; i < 4; ++i) {
+                yk[k][QK4_NL*ib+i+ 0] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] & 0xf];
+                yk[k][QK4_NL*ib+i+ 8] = scale * iq4k_values[x[ib].qs[4*k+i+ 0] >>  4];
+                yk[k][QK4_NL*ib+i+16] = scale * iq4k_values[x[ib].qs[4*k+i+16] & 0xf];
+                yk[k][QK4_NL*ib+i+24] = scale * iq4k_values[x[ib].qs[4*k+i+16] >>  4];
+                yk[k][QK4_NL*ib+i+ 4] = scale * iq4k_values[x[ib].qs[4*k+i+32] & 0xf];
+                yk[k][QK4_NL*ib+i+12] = scale * iq4k_values[x[ib].qs[4*k+i+32] >>  4];
+                yk[k][QK4_NL*ib+i+20] = scale * iq4k_values[x[ib].qs[4*k+i+48] & 0xf];
+                yk[k][QK4_NL*ib+i+28] = scale * iq4k_values[x[ib].qs[4*k+i+48] >>  4];
+            }
+        }
+    }
+}
+
+void vec_dot_iq4_nl_x4_q8_0(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
+#if GGML_USE_IQK_MULMAT
+    if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ4_NL_X4, vx, 0, GGML_TYPE_Q8_0, vy, 0, s, 0, 0, 1)) {
+        return;
+    }
+#endif
+    GGML_ASSERT(n%QK4_NL == 0);
+    GGML_ASSERT(nrc == 1);
+    GGML_UNUSED(bs);
+    GGML_UNUSED(bx);
+    GGML_UNUSED(by);
+}
+
 
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index 50c425af..7942cc04 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -63,6 +63,12 @@ void   vec_dot_iq2_ks_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void
 
 void iqk_quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 
+void   quantize_row_iq4_nl_x4_ref(const float * GGML_RESTRICT x, block_iq4_nl_x4  * GGML_RESTRICT y, int64_t k);
+void   quantize_row_iq4_nl_x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_iq4_nl_x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void   dequantize_row_iq4_nl_x4(const block_iq4_nl_x4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void   vec_dot_iq4_nl_x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/include/llama.h b/include/llama.h
index 965e5f50..f94dcb1a 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -179,6 +179,8 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ3_KL        = 146, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ2_KS        = 147, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ4_KSS       = 148, // except 1d tensors
+                                                //
+        LLAMA_FTYPE_MOSTLY_IQ4_NL_X4     = 225, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
diff --git a/src/llama.cpp b/src/llama.cpp
index 61448319..6eac67b6 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3849,6 +3849,7 @@ struct llama_model_loader {
                 case GGML_TYPE_IQ1_BN:  ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN;  break;
                 case GGML_TYPE_IQ2_BN:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN;  break;
                 case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
+                case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;break;
                 case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
                 case GGML_TYPE_IQ4_KS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS;  break;
                 case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
@@ -4553,6 +4554,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ4_KS:   return "IQ4_KS - 4.25 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ4_KSS:  return "IQ4_KSS - 4.0 bpw";
@@ -15766,6 +15768,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                      new_type == GGML_TYPE_Q4_0_8_8) {
                 new_type = GGML_TYPE_Q4_0;
             }
+            else if (new_type == GGML_TYPE_IQ4_NL_X4) {
+                new_type = GGML_TYPE_IQ4_NL;
+            }
         }
     } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
                ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M ||
@@ -15827,7 +15832,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
-        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4 ||
                   ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && qs.model.hparams.n_gqa() >= 2) {
             new_type = GGML_TYPE_IQ5_K;
         }
@@ -15840,7 +15845,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
             if (qs.model.hparams.n_vocab >= 127999 && (qs.model.type == MODEL_8B || qs.model.type == MODEL_70B))
                 new_type = GGML_TYPE_Q6_K;
-        } 
+        }
         if (qs.model.type == MODEL_70B) {
             // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
             // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
@@ -15857,6 +15862,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_IQ3_S  ) new_type = GGML_TYPE_Q4_K;
             else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
             else if (new_type == GGML_TYPE_IQ4_NL) new_type = GGML_TYPE_Q5_K;
+            else if (new_type == GGML_TYPE_IQ4_NL_X4) new_type = GGML_TYPE_Q5_K;
             else if (new_type == GGML_TYPE_Q5_K) new_type = GGML_TYPE_Q6_K;
         }
         ++qs.i_attention_wv;
@@ -15919,8 +15925,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                 if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
             }
         }
-        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
-                    ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && !qs.has_imatrix) {
+        else if (i_layer < n_layer/8 && !qs.has_imatrix &&
+                (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
+                 ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4)) {
             new_type = GGML_TYPE_Q5_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
@@ -15943,7 +15950,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                     ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
                     ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S   ||
                     ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K   ||
-                    ftype == LLAMA_FTYPE_MOSTLY_IQ2_K  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K) {
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ2_K  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_X4) {
                     new_type = GGML_TYPE_Q5_K;
                 }
             } else {
@@ -16152,6 +16159,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_IQ1_BN:  default_type = GGML_TYPE_IQ1_BN;  break;
         case LLAMA_FTYPE_MOSTLY_IQ2_BN:  default_type = GGML_TYPE_IQ2_BN;  break;
         case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;break;
         case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
         case LLAMA_FTYPE_MOSTLY_IQ4_KS:  default_type = GGML_TYPE_IQ4_KS;  break;
         case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
@@ -16509,6 +16517,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
                 else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
             }
+            if (new_type == GGML_TYPE_IQ4_NL_X4) {
+                if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_NL;
+                else chunk_size_multiplier = 4;
+            }
 
             LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
             fflush(stdout);