diff --git a/exla/lib/exla.ex b/exla/lib/exla.ex
index 78c9016361..403c6fbe76 100644
--- a/exla/lib/exla.ex
+++ b/exla/lib/exla.ex
@@ -215,6 +215,21 @@ defmodule EXLA do
   The metadata is:
 
     * `:key` - the compilation key for debugging
+
+  ## Sharding
+
+  EXLA supports sharding, which is a way to partition a computation across multiple devices.
+  There are a number of collective operations that are supported by sharding.
+
+  ### [`all_gather`](https://openxla.org/stablehlo/spec#all_gather)
+
+  #### Options
+
+    * `:all_gather_dim` - the dimension along which to gather
+    * `:replica_groups` - 2D list defining how replicas are grouped
+    * `:use_global_device_ids` - Whether to use global device IDs (default: `false`)
+    * `:channel_id` - Channel ID for communication (optional)
+
   """
 
   @behaviour Nx.Defn.Compiler
diff --git a/exla/lib/exla/defn.ex b/exla/lib/exla/defn.ex
index 0d29dc7bd9..48593c22aa 100644
--- a/exla/lib/exla/defn.ex
+++ b/exla/lib/exla/defn.ex
@@ -1474,6 +1474,27 @@ defmodule EXLA.Defn do
     EXLA.Lib.argsort(state.builder, tensor, dimension, stable, comp, ans.type)
   end
 
+  ## to_operator collective ops
+
+  defp to_operator(:all_gather, [%Value{} = tensor, opts], ans, _state) do
+    all_gather_dim = Keyword.fetch!(opts, :all_gather_dim)
+    replica_groups = Keyword.fetch!(opts, :replica_groups)
+    use_global_device_ids = Keyword.get(opts, :use_global_device_ids, false)
+
+    # We might want to surface all_gather as an operation that takes a container of operands instead of a single one.
+    [result] =
+      Value.all_gather(
+        [tensor],
+        expr_to_typespec(ans),
+        all_gather_dim,
+        replica_groups,
+        use_global_device_ids,
+        opts[:channel_id]
+      )
+
+    result
+  end
+
   defp fft(exla_op, [%Value{} = tensor, opts], %{type: type} = ans, state) do
     n = opts[:length]
     axis = opts[:axis]
diff --git a/exla/lib/exla/mlir/value.ex b/exla/lib/exla/mlir/value.ex
index 393b6d57a8..ec6527e091 100644
--- a/exla/lib/exla/mlir/value.ex
+++ b/exla/lib/exla/mlir/value.ex
@@ -64,6 +64,36 @@ defmodule EXLA.MLIR.Value do
     end
   end
 
+  def all_gather(
+        [%Value{function: func} | _] = operands,
+        typespec,
+        all_gather_dim,
+        replica_groups,
+        use_global_device_ids,
+        channel_id \\ nil
+      ) do
+    result_types = typespecs_to_mlir_types([typespec])
+
+    num_groups = length(replica_groups)
+    group_size = if num_groups > 0, do: length(hd(replica_groups)), else: 0
+    flat_groups = List.flatten(replica_groups)
+
+    attributes = [
+      all_gather_dim: attr_i64(all_gather_dim),
+      replica_groups: attr_dense_elements(flat_groups, {:s, 64}, {num_groups, group_size}),
+      use_global_device_ids: attr_boolean(use_global_device_ids)
+    ]
+
+    attributes =
+      if channel_id do
+        Keyword.put(attributes, :channel_id, attr_i64(channel_id))
+      else
+        attributes
+      end
+
+    op(func, "stablehlo.all_gather", operands, result_types, attributes: attributes)
+  end
+
   defp compare_and_return_bool(func, lhs, rhs, typespec, direction, total_order? \\ false) do
     %{type: lhs_type} = get_typespec(lhs)
     %{type: rhs_type} = get_typespec(rhs)
diff --git a/exla/test/exla/defn/sharding_test.exs b/exla/test/exla/defn/sharding_test.exs
index 0cb30efa24..e11bab0981 100644
--- a/exla/test/exla/defn/sharding_test.exs
+++ b/exla/test/exla/defn/sharding_test.exs
@@ -290,9 +290,6 @@ defmodule EXLA.Defn.ShardingTest do
         %{0 => [0]}
       ]
 
-      # Output: shard dim 0 on axis 0, dim 1 on axis 1 (like x)
-      output_shardings = [%{0 => [0], 1 => [1]}]
-
       # For mesh {2, 2}, we have 4 partitions
       # x: {8, 2} sharded [[0], [1]] -> each partition gets {4, 1}
       # y: {8, 1} sharded [[0], []] -> each partition gets {4, 1}
@@ -333,8 +330,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       expected_mlir = """
@@ -353,10 +349,7 @@ defmodule EXLA.Defn.ShardingTest do
       assert expected_mlir == result.mlir_module
 
       results =
-        EXLA.shard_jit(fun, mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
-        ).(args)
+        EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
 
       assert length(results) == 4
 
@@ -427,7 +420,6 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2, 2}}
       # Only one sharding spec for two arguments
       input_shardings = [%{0 => [0]}]
-      output_shardings = [%{}]
 
       # For mesh {2, 2}, we have 4 partitions
       # Each partition has 2 inputs
@@ -438,8 +430,7 @@ defmodule EXLA.Defn.ShardingTest do
                    fn ->
                      EXLA.to_mlir_module(fun, args,
                        mesh: mesh,
-                       input_shardings: input_shardings,
-                       output_shardings: output_shardings
+                       input_shardings: input_shardings
                      )
                    end
     end
@@ -452,16 +443,12 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2, 2}}
       # Mesh has 2 axes (0 and 1), but we reference axis 2
       input_shardings = [%{0 => [2]}]
-      output_shardings = [%{}]
 
       # For mesh {2, 2}, we have 4 partitions
       args = List.duplicate([Nx.iota({4, 2})], 4)
 
       assert_raise ArgumentError, fn ->
-        EXLA.shard_jit(fun, mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
-        ).(args)
+        EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
       end
     end
 
@@ -471,7 +458,6 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2, 2}}
       # Axis 0 used for both dimensions
       input_shardings = [%{0 => [0], 1 => [0]}]
-      output_shardings = [%{}]
 
       # For mesh {2, 2}, we have 4 partitions
       args = List.duplicate([Nx.iota({4, 1})], 4)
@@ -479,10 +465,7 @@ defmodule EXLA.Defn.ShardingTest do
       assert_raise ArgumentError,
                    ~r/axis 0 was used twice in the same input sharding/,
                    fn ->
-                     EXLA.shard_jit(fun, mesh,
-                       input_shardings: input_shardings,
-                       output_shardings: output_shardings
-                     ).(args)
+                     EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
                    end
     end
 
@@ -492,16 +475,12 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2, 2}}
       # Tensor is rank 2, but sharding spec has 3 dimensions
       input_shardings = [%{0 => [0], 1 => [1], 2 => []}]
-      output_shardings = [%{}]
 
       # For mesh {2, 2}, we have 4 partitions
       args = List.duplicate([Nx.iota({4, 2})], 4)
 
       assert_raise ArgumentError, fn ->
-        EXLA.shard_jit(fun, mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
-        ).(args)
+        EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
       end
     end
 
@@ -511,17 +490,13 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2}}
       # Tensor is rank 2, but -3 is out of bounds (only -1 and -2 are valid)
       input_shardings = [%{-3 => [0]}]
-      output_shardings = [%{}]
 
       args = List.duplicate([Nx.iota({4, 2})], 2)
 
       assert_raise ArgumentError,
                    ~r/given axis \(-3\) invalid for shape with rank 2/,
                    fn ->
-                     EXLA.shard_jit(fun, mesh,
-                       input_shardings: input_shardings,
-                       output_shardings: output_shardings
-                     ).(args)
+                     EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
                    end
     end
 
@@ -531,17 +506,13 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2}}
       # Tensor is rank 2, but axis 3 is out of bounds
       input_shardings = [%{3 => [0]}]
-      output_shardings = [%{}]
 
       args = List.duplicate([Nx.iota({4, 2})], 2)
 
       assert_raise ArgumentError,
                    ~r/given axis \(3\) invalid for shape with rank 2/,
                    fn ->
-                     EXLA.shard_jit(fun, mesh,
-                       input_shardings: input_shardings,
-                       output_shardings: output_shardings
-                     ).(args)
+                     EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
                    end
     end
   end
@@ -582,9 +553,6 @@ defmodule EXLA.Defn.ShardingTest do
       # All dimensions replicated
       input_shardings = [%{}]
 
-      # Output: replicated (all-gathered) across all devices
-      output_shardings = [%{}]
-
       # For mesh {2, 2}, we have 4 partitions
       # Input fully replicated -> each partition gets full {8, 4}
       args = List.duplicate([Nx.iota({8, 4})], 4)
@@ -592,8 +560,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       assert is_binary(result.mlir_module)
@@ -606,9 +573,6 @@ defmodule EXLA.Defn.ShardingTest do
       # Scalar has no dimensions to shard
       input_shardings = [%{}]
 
-      # Output: replicated (all-gathered) across all devices
-      output_shardings = [%{}]
-
       # For mesh {2}, we have 2 partitions
       # Scalar is replicated across all partitions
       args = List.duplicate([Nx.tensor(5.0)], 2)
@@ -616,8 +580,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       assert is_binary(result.mlir_module)
@@ -637,9 +600,6 @@ defmodule EXLA.Defn.ShardingTest do
         %{}
       ]
 
-      # Outputs: both replicated (tuple with two elements)
-      output_shardings = [%{}, %{}]
-
       # For mesh {2, 2}, we have 4 partitions
       # x: {8, 4} sharded [[0], [1]] -> each partition gets {4, 2}
       # y: {8, 4} sharded [[0], []] -> each partition gets {4, 4}
@@ -658,8 +618,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       assert is_binary(result.mlir_module)
@@ -673,9 +632,6 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "test_mesh", shape: {2, 2}}
       input_shardings = [%{0 => [0]}]
 
-      # Output: replicated (all-gathered) across all devices
-      output_shardings = [%{}]
-
       # For mesh {2, 2}, we have 4 partitions
       # Input sharded [[0], []] -> each partition gets {4, 2}
       args = List.duplicate([Nx.iota({4, 2})], 4)
@@ -683,8 +639,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       mlir = result.mlir_module
@@ -702,9 +657,6 @@ defmodule EXLA.Defn.ShardingTest do
       mesh = %Mesh{name: "mesh", shape: {2, 2}}
       input_shardings = [%{0 => [0], 1 => [1]}]
 
-      # Output: replicated (all-gathered) across all devices
-      output_shardings = [%{}]
-
       # For mesh {2, 2}, we have 4 partitions
       # Input sharded [[0], [1]] -> each partition gets {4, 1}
       args = List.duplicate([Nx.iota({4, 1})], 4)
@@ -712,8 +664,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       mlir = result.mlir_module
@@ -732,9 +683,6 @@ defmodule EXLA.Defn.ShardingTest do
       # Use named dimensions instead of indices
       input_shardings = [%{:batch => [0]}]
 
-      # Output: replicated (all-gathered) across all devices
-      output_shardings = [%{}]
-
       # For mesh {2}, we have 2 partitions
       # Named dimension :batch should map to first dimension (index 0)
       args = List.duplicate([Nx.iota({4, 2}, names: [:batch, :features])], 2)
@@ -742,8 +690,7 @@ defmodule EXLA.Defn.ShardingTest do
       result =
         EXLA.to_mlir_module(fun, args,
           mesh: mesh,
-          input_shardings: input_shardings,
-          output_shardings: output_shardings
+          input_shardings: input_shardings
         )
 
       # Should generate valid MLIR with sharding on first dimension
@@ -891,4 +838,118 @@ defmodule EXLA.Defn.ShardingTest do
       end
     end
   end
+
+  describe "all_gather" do
+    @moduletag :multi_device
+    test "in all dims results in the same tensor in all devices" do
+      fun = fn x, y ->
+        Nx.add(x, y)
+        |> Nx.Defn.Kernel.all_gather(all_gather_dim: 0, replica_groups: [[0]])
+        |> Nx.Defn.Kernel.all_gather(all_gather_dim: 1, replica_groups: [[0]])
+      end
+
+      mesh = %Mesh{name: "mesh", shape: {2, 2}}
+      # First arg: 0..15 (8x2), shard dim 0 on mesh axis 0, dim 1 on mesh axis 1
+      # Second arg: 100..115 (8x2), same sharding — makes sharded results easy to read
+      input_shardings = [%{0 => [0], 1 => [1]}, %{0 => [0], 1 => [1]}]
+
+      # For mesh {2, 2}, 4 partitions. Each gets {4, 1}. Full 8x2 row-major: [[0,1],[2,3],...,[14,15]].
+      # Partition (axis_0, axis_1): (0,0)=rows 0-3 col 0, (0,1)=rows 0-3 col 1, (1,0)=rows 4-7 col 0, (1,1)=rows 4-7 col 1.
+      # So partition 0 gets (0,0),(1,0),(2,0),(3,0) = 0,2,4,6; partition 1 gets (0,1),(1,1),... = 1,3,5,7; etc.
+      args = [
+        # partition 0: rows 0–3 col 0 -> 0,2,4,6 and 100,102,104,106
+        [Nx.tensor([[0], [2], [4], [6]]), Nx.tensor([[100], [102], [104], [106]])],
+        # partition 1: rows 0–3 col 1 -> 1,3,5,7 and 101,103,105,107
+        [Nx.tensor([[1], [3], [5], [7]]), Nx.tensor([[101], [103], [105], [107]])],
+        # partition 2: rows 4–7 col 0 -> 8,10,12,14 and 108,110,112,114
+        [Nx.tensor([[8], [10], [12], [14]]), Nx.tensor([[108], [110], [112], [114]])],
+        # partition 3: rows 4–7 col 1 -> 9,11,13,15 and 109,111,113,115
+        [Nx.tensor([[9], [11], [13], [15]]), Nx.tensor([[109], [111], [113], [115]])]
+      ]
+
+      result = EXLA.to_mlir_module(fun, args, mesh: mesh, input_shardings: input_shardings)
+
+      expected_mlir = """
+      module {
+        sdy.mesh @mesh = <["axis_0"=2, "axis_1"=2]>
+        func.func public @main(%arg0: tensor<8x2xi32> {sdy.sharding = #sdy.sharding<@mesh, [{"axis_0", ?}p0, {"axis_1", ?}p0]>}, %arg1: tensor<8x2xi32> {sdy.sharding = #sdy.sharding<@mesh, [{"axis_0", ?}p0, {"axis_1", ?}p0]>}) -> tensor<8x2xi32> {
+          %0 = stablehlo.add %arg0, %arg1 : tensor<8x2xi32>
+          %1 = "stablehlo.all_gather"(%0) <{all_gather_dim = 0 : i64, replica_groups = dense<0> : tensor<1x1xi64>}> : (tensor<8x2xi32>) -> tensor<8x2xi32>
+          %2 = "stablehlo.all_gather"(%1) <{all_gather_dim = 1 : i64, replica_groups = dense<0> : tensor<1x1xi64>}> : (tensor<8x2xi32>) -> tensor<8x2xi32>
+          return %2 : tensor<8x2xi32>
+        }
+      }
+      """
+
+      assert expected_mlir == result.mlir_module
+
+      results = EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
+
+      assert length(results) == 4
+
+      # After all_gather: full first arg 0..15 + full second 100..115 -> 100,102,...,130
+      expected_result =
+        Nx.tensor([
+          [100, 102],
+          [104, 106],
+          [108, 110],
+          [112, 114],
+          [116, 118],
+          [120, 122],
+          [124, 126],
+          [128, 130]
+        ])
+
+      Enum.with_index(results, fn result, i ->
+        assert_equal(result, expected_result)
+        assert result.data.buffer.device_id == i
+      end)
+    end
+
+    @moduletag :multi_device
+    test "can return partially sharded results" do
+      fun = fn x, y ->
+        x
+        |> Nx.Defn.Kernel.all_gather(all_gather_dim: 1, replica_groups: [[0]])
+        |> Nx.add(y)
+      end
+
+      mesh = %Mesh{name: "mesh", shape: {2, 2}}
+      # Inputs sharded on both axes
+      input_shardings = [%{0 => [0], 1 => [1]}, %{0 => [0]}]
+
+      # Logical x: 8x2, y: 8x2. Each partition gets {4, 1} of x and {4, 2} of y
+      args = [
+        [
+          Nx.tensor([[0], [1], [2], [3]]),
+          Nx.tensor([[100, 101], [102, 103], [104, 105], [106, 107]])
+        ],
+        [
+          Nx.tensor([[4], [5], [6], [7]]),
+          Nx.tensor([[100, 101], [102, 103], [104, 105], [106, 107]])
+        ],
+        [
+          Nx.tensor([[8], [9], [10], [11]]),
+          Nx.tensor([[110, 111], [112, 113], [114, 115], [116, 117]])
+        ],
+        [
+          Nx.tensor([[12], [13], [14], [15]]),
+          Nx.tensor([[110, 111], [112, 113], [114, 115], [116, 117]])
+        ]
+      ]
+
+      assert [result0, result1, result2, result3] =
+               EXLA.shard_jit(fun, mesh, input_shardings: input_shardings).(args)
+
+      # After gathering, devices 0 and 1 have the same data as each other, likewise for devices 2 and 3
+      assert_equal(result0, Nx.tensor([[100, 105], [103, 108], [106, 111], [109, 114]]))
+      assert result0.data.buffer.device_id == 0
+      assert_equal(result0, Nx.tensor([[100, 105], [103, 108], [106, 111], [109, 114]]))
+      assert result1.data.buffer.device_id == 1
+      assert_equal(result2, Nx.tensor([[118, 123], [121, 126], [124, 129], [127, 132]]))
+      assert result2.data.buffer.device_id == 2
+      assert_equal(result3, Nx.tensor([[118, 123], [121, 126], [124, 129], [127, 132]]))
+      assert result3.data.buffer.device_id == 3
+    end
+  end
 end
diff --git a/nx/lib/nx/binary_backend.ex b/nx/lib/nx/binary_backend.ex
index cae7c94997..974d558b0d 100644
--- a/nx/lib/nx/binary_backend.ex
+++ b/nx/lib/nx/binary_backend.ex
@@ -522,11 +522,13 @@ defmodule Nx.BinaryBackend do
         right_batch_item_bits = right_batch_item_length * right_size
 
         <<_::bitstring-size(^left_offset_bits),
-          left_batch_item_binary::bitstring-size(^left_batch_item_bits), _::bitstring>> =
+          left_batch_item_binary::bitstring-size(^left_batch_item_bits),
+          _::bitstring>> =
           left_binary
 
         <<_::bitstring-size(^right_offset_bits),
-          right_batch_item_binary::bitstring-size(^right_batch_item_bits), _::bitstring>> =
+          right_batch_item_binary::bitstring-size(^right_batch_item_bits),
+          _::bitstring>> =
           right_binary
 
         bin_dot(
@@ -1756,7 +1758,8 @@ defmodule Nx.BinaryBackend do
             before_slice_size = current - previous
 
             <<before_offset::bitstring-size(^before_slice_size),
-              current_bitstring::bitstring-size(^target_chunk), to_traverse::bitstring>> =
+              current_bitstring::bitstring-size(^target_chunk),
+              to_traverse::bitstring>> =
               to_traverse
 
             updated_elements =
diff --git a/nx/lib/nx/defn/expr.ex b/nx/lib/nx/defn/expr.ex
index 899b430da4..e753b9aebb 100644
--- a/nx/lib/nx/defn/expr.ex
+++ b/nx/lib/nx/defn/expr.ex
@@ -1166,6 +1166,11 @@ defmodule Nx.Defn.Expr do
     expr(out, context, :gather, [tensor, indices, opts])
   end
 
+  def all_gather(tensor, opts) do
+    {[expr], context} = to_exprs([tensor])
+    expr(expr, context, :all_gather, [expr, opts])
+  end
+
   @impl true
   def reverse(out, tensor, axes) do
     tensor = to_expr(tensor)
diff --git a/nx/lib/nx/defn/kernel.ex b/nx/lib/nx/defn/kernel.ex
index ab913ab61f..4530d79e16 100644
--- a/nx/lib/nx/defn/kernel.ex
+++ b/nx/lib/nx/defn/kernel.ex
@@ -1669,6 +1669,19 @@ defmodule Nx.Defn.Kernel do
     end
   end
 
+  @doc """
+  Gathers tensors along a specified axis across an `Nx.Mesh`.
+
+  Requires a backend that supports collective operations.
+
+  ## Options
+
+  Refer to the chosen backend/compiler documentation for supported options.
+  """
+  def all_gather(tensor, opts) do
+    Nx.Defn.Expr.all_gather(tensor, opts)
+  end
+
   @definitions (Module.definitions_in(__MODULE__, :def) ++
                   Module.definitions_in(__MODULE__, :defmacro)) --
                  [
diff --git a/nx/lib/nx/floating.ex b/nx/lib/nx/floating.ex
index 4ded248c91..54cec81378 100644
--- a/nx/lib/nx/floating.ex
+++ b/nx/lib/nx/floating.ex
@@ -209,8 +209,16 @@ defmodule Nx.Floating do
   # E4M3FN: 1 sign, 4 exponent (bias 7), 3 mantissa
   # Max value: 448.0 (0x7E), Min value: -448.0 (0xFE)
   def dump_f8_e4m3fn(0), do: <<0b0000_0000>>
-  def dump_f8_e4m3fn(+0.0), do: <<0b0000_0000>>
-  def dump_f8_e4m3fn(-0.0), do: <<0b1000_0000>>
+
+  if +0.0 === -0.0 do
+    # OTP versions <= 28.0 have a bug where +0.0 === -0.0,
+    # so we need to special-case it to avoid compiler errors
+    # related to the +0.0 clause shadowing the -0.0 clause
+    def dump_f8_e4m3fn(x) when x == 0.0, do: <<0b0000_0000>>
+  else
+    def dump_f8_e4m3fn(+0.0), do: <<0b0000_0000>>
+    def dump_f8_e4m3fn(-0.0), do: <<0b1000_0000>>
+  end
 
   def dump_f8_e4m3fn(x) when is_number(x) do
     # Clamp to E4M3FN range and convert
diff --git a/nx/test/nx/defn/composite_test.exs b/nx/test/nx/defn/composite_test.exs
index 2618e29170..6dc8fc1d52 100644
--- a/nx/test/nx/defn/composite_test.exs
+++ b/nx/test/nx/defn/composite_test.exs
@@ -24,7 +24,8 @@ defmodule Nx.Defn.CompositeTest do
                 Nx.tensor(1),
                 Nx.tensor(3, type: {:c, 64}),
                 Nx.tensor(4, type: {:c, 64})
-              }, Nx.tensor(2, type: {:c, 64})} ==
+              },
+              Nx.tensor(2, type: {:c, 64})} ==
                Composite.traverse(
                  {1, Complex.new(2), Nx.tensor(3)},
                  0,
diff --git a/nx/test/nx/defn_test.exs b/nx/test/nx/defn_test.exs
index 62993b07a3..d15951ce71 100644
--- a/nx/test/nx/defn_test.exs
+++ b/nx/test/nx/defn_test.exs
@@ -2952,4 +2952,30 @@ defmodule Nx.DefnTest do
       assert vectorized_metadata_tuple(x, z) == vec_nonvec_result
     end
   end
+
+  describe "sharding" do
+    defn all_gather_test(tensor) do
+      Nx.Defn.Kernel.all_gather(tensor, all_gather_dim: 0, replica_groups: [[0]])
+    end
+
+    test "all_gather produces correct expr format for compiler" do
+      # Uses debug_expr to inspect the expression without compiling.
+      # Guarantees the format passed to compilers (e.g. EXLA) stays stable.
+      assert %T{data: %Expr{op: :all_gather, args: [tensor, opts]}} =
+               Nx.Defn.debug_expr(&all_gather_test/1).(Nx.tensor([1, 2, 3, 4]))
+
+      assert %T{data: %Expr{op: :parameter, args: [0]}} = tensor
+
+      # Compilers expect opts with :all_gather_dim and :replica_groups
+      assert opts[:all_gather_dim] == 0
+      assert opts[:replica_groups] == [[0]]
+    end
+
+    @tag compiler: Evaluator
+    test "all_gather works" do
+      assert_raise UndefinedFunctionError, fn ->
+        all_gather_test(Nx.tensor([1, 2, 3, 4]))
+      end
+    end
+  end
 end
diff --git a/nx/test/nx/floating_test.exs b/nx/test/nx/floating_test.exs
index 7e0a088fe0..4720c94d0d 100644
--- a/nx/test/nx/floating_test.exs
+++ b/nx/test/nx/floating_test.exs
@@ -156,7 +156,7 @@ defmodule Nx.FloatingTest do
       {0x7F, :nan},
       # Negative values (sign bit = 1)
       # Denormalized (exponent = 0): value = -mantissa/8 * 2^-6
-      {0x80, -0.0},
+      if(+0.0 === -0.0, do: {0x00, -0.0}, else: {0x80, -0.0}),
       {0x81, -0.001953125},
       {0x82, -0.00390625},
       {0x83, -0.005859375},
@@ -349,7 +349,12 @@ defmodule Nx.FloatingTest do
     test "pretty printing" do
       # Zeroes
       assert Nx.tensor([0.0], type: :f8) |> inspect() =~ "[0.0]"
-      assert Nx.tensor([-0.0], type: :f8) |> inspect() =~ "[-0.0]"
+
+      if +0.0 === -0.0 do
+        assert Nx.tensor([-0.0], type: :f8) |> inspect() =~ "[0.0]"
+      else
+        assert Nx.tensor([-0.0], type: :f8) |> inspect() =~ "[-0.0]"
+      end
 
       # Infinity
       assert Nx.tensor([:infinity], type: :f8) |> inspect() =~ "[Inf]"
@@ -399,7 +404,12 @@ defmodule Nx.FloatingTest do
     test "pretty printing" do
       # Zeroes
       assert Nx.tensor([0.0], type: :bf16) |> inspect() =~ "[0.0]"
-      assert Nx.tensor([-0.0], type: :bf16) |> inspect() =~ "[-0.0]"
+
+      if +0.0 === -0.0 do
+        assert Nx.tensor([-0.0], type: :bf16) |> inspect() =~ "[0.0]"
+      else
+        assert Nx.tensor([-0.0], type: :bf16) |> inspect() =~ "[-0.0]"
+      end
 
       # Infinity
       assert Nx.tensor([:infinity], type: :bf16) |> inspect() =~ "[Inf]"