diff --git a/README.md b/README.md
index aad1066c..5fb8705a 100644
--- a/README.md
+++ b/README.md
@@ -43,7 +43,7 @@ vector_size = 2^20
 tile_size = 16
 
 blocks = cld(vector_size, tile_size)
-grid = (blocks, 1, 1)       
+grid = (blocks, 1, 1)
 
 a, b = CUDA.rand(Float32, vector_size), CUDA.rand(Float32, vector_size)
 c = CUDA.zeros(Float32, vector_size)
@@ -232,7 +232,6 @@ uses standard Julia syntax and is overlaid on `Base`.
 
 cuTile.jl follows Julia conventions, which differ from the Python API in several ways:
 
-
 ### Kernel definition syntax
 
 Kernels don't need a decorator, but do have to return `nothing`:
@@ -511,6 +510,36 @@ ct.store(arr, (i, j), t)
 ```
 
 
+## Host-level operations
+
+cuTile.jl also provides a limited set of host-level APIs to use cuTile without
+writing custom kernels. For example, for element-wise operations on `CuArray`s,
+cuTile can automatically generate and launch a fused kernel using Julia's
+broadcast machinery:
+
+```julia
+using CUDA
+import cuTile as ct
+
+A = CUDA.rand(Float32, 1024)
+B = CUDA.rand(Float32, 1024)
+C = CUDA.zeros(Float32, 1024)
+
+# Wrap arrays in Tiled() to route through cuTile
+ct.Tiled(C) .= ct.Tiled(A) .+ ct.Tiled(B)
+
+# Or use the @. macro for convenience
+ct.@. C = A + sin(B)
+
+# Allocating form (returns a new CuArray)
+D = ct.@. A + B
+```
+
+The entire broadcast expression is fused into a single cuTile kernel. Tile sizes
+are automatically chosen based on array dimensions (power-of-2, budget-based).
+Works with 1D through N-dimensional arrays.
+
+
 ## Acknowledgments
 
 cuTile.jl is inspired by [cuTile-Python](https://github.com/NVIDIA/cutile-python/),
diff --git a/ext/CUDAExt.jl b/ext/CUDAExt.jl
index 0b80c06a..6814b878 100644
--- a/ext/CUDAExt.jl
+++ b/ext/CUDAExt.jl
@@ -9,9 +9,12 @@ using CompilerCaching: CacheView, method_instance, results
 
 import Core.Compiler as CC
 
-using CUDA: CuModule, CuFunction, cudacall, device, capability
+using CUDA: CuArray, CuModule, CuFunction, cudacall, device, capability
 using CUDA_Compiler_jll
 
+import Base.Broadcast: BroadcastStyle
+import CUDA: CuArrayStyle
+
 public launch
 
 function run_and_collect(cmd)
@@ -255,4 +258,7 @@ Other values pass through unchanged.
 to_tile_arg(x) = x
 to_tile_arg(arr::AbstractArray) = TileArray(arr)
 
+# Tiled Broadcast — TiledStyle wins over CuArrayStyle
+BroadcastStyle(::cuTile.TiledStyle{N}, ::CuArrayStyle{M}) where {N,M} = cuTile.TiledStyle{max(N,M)}()
+
 end
diff --git a/src/broadcast.jl b/src/broadcast.jl
new file mode 100644
index 00000000..12c84e8a
--- /dev/null
+++ b/src/broadcast.jl
@@ -0,0 +1,204 @@
+import Base.Broadcast: BroadcastStyle, Broadcasted
+
+#=============================================================================
+ Tiled wrapper — routes broadcast expressions through cuTile kernels
+=============================================================================#
+
+"""
+    Tiled(x)
+
+Wrapper that routes broadcast expressions through cuTile kernels.
+
+    Tiled(B) .= A .+ A
+
+Uses Julia's `Base.Broadcast` fusion machinery to build a `Broadcasted` tree,
+then dispatches to a generic cuTile kernel that evaluates the tree on tiles.
+"""
+struct Tiled{A <: AbstractArray}
+    parent::A
+end
+Tiled(x) = x  # passthrough for non-arrays (Numbers, etc.)
+Base.parent(t::Tiled) = t.parent
+Base.axes(t::Tiled) = axes(parent(t))
+Base.size(t::Tiled) = size(parent(t))
+Base.ndims(::Tiled{A}) where A = ndims(A)
+Base.eltype(::Tiled{A}) where A = eltype(A)
+Base.Broadcast.broadcastable(t::Tiled) = t
+
+# Walk dotted AST, wrap value-position leaves in Tiled()
+_wrap_tiled(x) = x  # literals pass through
+_wrap_tiled(s::Symbol) = :($Tiled($s))
+function _wrap_tiled(ex::Expr)
+    if ex.head === :.=
+        Expr(:.=, _wrap_tiled(ex.args[1]), _wrap_tiled(ex.args[2]))
+    elseif ex.head === :. && length(ex.args) == 2 &&
+           ex.args[2] isa Expr && ex.args[2].head === :tuple
+        # f.(args...) — wrap args, NOT function position
+        new_args = map(_wrap_tiled, ex.args[2].args)
+        Expr(:., ex.args[1], Expr(:tuple, new_args...))
+    else
+        Expr(ex.head, map(_wrap_tiled, ex.args)...)
+    end
+end
+
+"""
+    @. expr
+
+Like `Base.@.` but wraps every value-position leaf in `Tiled()`, routing
+the broadcast through cuTile kernels.
+
+    using cuTile; const ct = cuTile
+    ct.@. C = A + sin(B)
+    # equivalent to: Tiled(C) .= Tiled(A) .+ sin.(Tiled(B))
+"""
+macro __dot__(ex)
+    esc(_wrap_tiled(Base.Broadcast.__dot__(ex)))
+end
+
+#=============================================================================
+ TiledStyle — routes broadcast through cuTile kernels
+=============================================================================#
+
+struct TiledStyle{N} <: BroadcastStyle end
+TiledStyle{M}(::Val{N}) where {N,M} = TiledStyle{N}()
+
+BroadcastStyle(::Type{<:Tiled{A}}) where A = TiledStyle{ndims(A)}()
+
+# TiledStyle wins over DefaultArrayStyle
+BroadcastStyle(::TiledStyle{N}, ::Base.Broadcast.DefaultArrayStyle{M}) where {N,M} = TiledStyle{max(N,M)}()
+BroadcastStyle(::TiledStyle{N}, ::TiledStyle{M}) where {N,M} = TiledStyle{max(N,M)}()
+
+#=============================================================================
+ materialize! and copy — dispatch to _tiled_broadcast!
+=============================================================================#
+
+function Base.Broadcast.materialize!(dest::Tiled, bc::Broadcasted)
+    _tiled_broadcast!(parent(dest), bc)
+    return dest
+end
+
+function Base.copy(bc::Broadcasted{TiledStyle{N}}) where N
+    arr = @something _find_tiled_array(bc) error("tiled broadcast requires at least one Tiled() argument")
+    ElType = Base.Broadcast.combine_eltypes(bc.f, bc.args)
+    dest = similar(arr, ElType, axes(bc))
+    _tiled_broadcast!(dest, bc)
+    return dest
+end
+
+"""Find the first underlying array from a Tiled leaf in a Broadcasted tree."""
+_find_tiled_array(t::Tiled) = parent(t)
+_find_tiled_array(x) = nothing
+function _find_tiled_array(bc::Broadcasted)
+    for arg in bc.args
+        arr = _find_tiled_array(arg)
+        arr !== nothing && return arr
+    end
+    return nothing
+end
+
+#=============================================================================
+ _tiled_broadcast! — generic AbstractArray implementation
+=============================================================================#
+
+function _tiled_broadcast!(dest::AbstractArray{T,N}, bc::Broadcasted) where {T, N}
+    dest_ta = TileArray(dest)
+    tiled_bc = _to_tiled_bc(bc)
+
+    ts = _compute_tile_sizes(size(dest))
+    grid = ntuple(i -> cld(size(dest, i), ts[i]), N)
+
+    launch_grid = N <= 3 ? grid : (grid[1], grid[2], prod(grid[i] for i in 3:N))
+    overflow = N > 3 ? grid[3:end] : ()
+
+    launch(_tiled_bc_kernel, launch_grid, dest_ta, tiled_bc,
+           Constant(ts), Constant(overflow))
+end
+
+#=============================================================================
+ Generic tree walk — convert leaves to TileArrays
+=============================================================================#
+
+_to_tiled_bc(t::Tiled) = TileArray(parent(t))
+_to_tiled_bc(arr::AbstractArray) = TileArray(arr)
+_to_tiled_bc(x::Number) = x
+_to_tiled_bc(x) = x  # fallback for other types
+function _to_tiled_bc(bc::Broadcasted)
+    new_args = map(_to_tiled_bc, bc.args)
+    Broadcasted{Nothing}(bc.f, new_args, nothing)
+end
+
+#=============================================================================
+ Broadcast kernel — evaluates Broadcasted tree on tiles
+=============================================================================#
+
+@generated function _tiled_bc_kernel(dest::TileArray{T, N}, bc, tile_size, overflow_grids) where {T, N}
+    body = Expr[]
+    bid_vars = [Symbol("bid_$d") for d in 1:N]
+
+    if N <= 3
+        for d in 1:N
+            push!(body, :($(bid_vars[d]) = cuTile.bid($d)))
+        end
+    else
+        push!(body, :($(bid_vars[1]) = cuTile.bid(1)))
+        push!(body, :($(bid_vars[2]) = cuTile.bid(2)))
+        push!(body, :(_rem = cuTile.bid(3) - Int32(1)))
+        for d in 3:N
+            if d < N
+                push!(body, :($(bid_vars[d]) = rem(_rem, Int32(overflow_grids[$(d-2)])) + Int32(1)))
+                push!(body, :(_rem = fld(_rem, Int32(overflow_grids[$(d-2)]))))
+            else
+                push!(body, :($(bid_vars[d]) = _rem + Int32(1)))
+            end
+        end
+    end
+
+    idx = N == 1 ? bid_vars[1] : Expr(:tuple, bid_vars...)
+    push!(body, :(result = _eval_bc(bc, $idx, tile_size)))
+    push!(body, :(result_converted = convert(cuTile.Tile{$T}, result)))
+    push!(body, :(cuTile.store(dest, $idx, result_converted)))
+    push!(body, :(return))
+    Expr(:block, body...)
+end
+
+#=============================================================================
+ Recursive tree evaluation inside kernel
+=============================================================================#
+
+@inline _eval_bc(arr::TileArray, bid, tile_size) = cuTile.load(arr, bid, tile_size)
+@inline _eval_bc(x::Number, bid, tile_size) = x
+
+@inline function _eval_bc(bc::Broadcasted, bid, tile_size)
+    args = _eval_bc_args(bc.args, bid, tile_size)
+    # Use broadcast to get element-wise semantics (not direct call, which
+    # would dispatch to e.g. matmul for * on tiles)
+    broadcast(bc.f, args...)
+end
+
+@inline _eval_bc_args(::Tuple{}, bid, tile_size) = ()
+@inline _eval_bc_args(args::Tuple, bid, tile_size) =
+    (_eval_bc(args[1], bid, tile_size), _eval_bc_args(Base.tail(args), bid, tile_size)...)
+
+#=============================================================================
+ Tile sizing
+=============================================================================#
+
+"""
+    _compute_tile_sizes(dest_size; budget=4096)
+
+Distribute a total element budget greedily across dimensions, skipping singletons.
+Each tile dimension is a power of 2, capped by the array size in that dimension.
+"""
+function _compute_tile_sizes(dest_size::NTuple{N,Int}; budget::Int=4096) where N
+    ts = ones(Int, N)
+    remaining = budget
+    for i in 1:N
+        s = dest_size[i]
+        s == 1 && continue
+        t = prevpow(2, min(remaining, s))
+        ts[i] = t
+        remaining = remaining ÷ t
+        remaining < 2 && break
+    end
+    return NTuple{N,Int}(ts)
+end
diff --git a/src/cuTile.jl b/src/cuTile.jl
index 2e5fe398..e2d75cf3 100644
--- a/src/cuTile.jl
+++ b/src/cuTile.jl
@@ -38,7 +38,10 @@ include("language/math.jl")
 include("language/operations.jl")
 include("language/atomics.jl")
 
-public launch, ByTarget, @compiler_options
+# Host-level abstractions
+include("broadcast.jl")
+
+public launch, Tiled, ByTarget, @compiler_options, @.
 launch(args...) = error("Please import CUDA.jl before using `cuTile.launch`.")
 
 end # module cuTile
diff --git a/test/execution/atomics.jl b/test/device/atomics.jl
similarity index 100%
rename from test/execution/atomics.jl
rename to test/device/atomics.jl
diff --git a/test/execution/broadcast.jl b/test/device/broadcast.jl
similarity index 100%
rename from test/execution/broadcast.jl
rename to test/device/broadcast.jl
diff --git a/test/execution/control_flow.jl b/test/device/control_flow.jl
similarity index 100%
rename from test/execution/control_flow.jl
rename to test/device/control_flow.jl
diff --git a/test/execution/core.jl b/test/device/core.jl
similarity index 100%
rename from test/execution/core.jl
rename to test/device/core.jl
diff --git a/test/execution/hints.jl b/test/device/hints.jl
similarity index 100%
rename from test/execution/hints.jl
rename to test/device/hints.jl
diff --git a/test/execution/integration.jl b/test/device/integration.jl
similarity index 100%
rename from test/execution/integration.jl
rename to test/device/integration.jl
diff --git a/test/execution/math.jl b/test/device/math.jl
similarity index 100%
rename from test/execution/math.jl
rename to test/device/math.jl
diff --git a/test/execution/reductions.jl b/test/device/reductions.jl
similarity index 100%
rename from test/execution/reductions.jl
rename to test/device/reductions.jl
diff --git a/test/execution/tile.jl b/test/device/tile.jl
similarity index 100%
rename from test/execution/tile.jl
rename to test/device/tile.jl
diff --git a/test/execution/types.jl b/test/device/types.jl
similarity index 100%
rename from test/execution/types.jl
rename to test/device/types.jl
diff --git a/test/host/broadcast.jl b/test/host/broadcast.jl
new file mode 100644
index 00000000..64a625cc
--- /dev/null
+++ b/test/host/broadcast.jl
@@ -0,0 +1,115 @@
+using CUDA
+
+@testset "Tiled broadcast" begin
+    @testset "1D element-wise" begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        B = CUDA.rand(Float32, n)
+        C = CUDA.zeros(Float32, n)
+        ct.Tiled(C) .= ct.Tiled(A) .+ ct.Tiled(B)
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "fused multi-op" begin
+        n = 1024
+        A = CUDA.rand(Float32, n) .+ 0.1f0
+        C = CUDA.zeros(Float32, n)
+        ct.Tiled(C) .= ct.Tiled(A) .+ ct.Tiled(A) .* sin.(ct.Tiled(A))
+        @test Array(C) ≈ Array(A) .+ Array(A) .* sin.(Array(A)) rtol=1e-5
+    end
+
+    @testset "scalar broadcast" begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        C = CUDA.zeros(Float32, n)
+        ct.Tiled(C) .= ct.Tiled(A) .+ 1.0f0
+        @test Array(C) ≈ Array(A) .+ 1.0f0
+    end
+
+    @testset "2D element-wise" begin
+        m, n = 128, 256
+        A = CUDA.rand(Float32, m, n)
+        B = CUDA.rand(Float32, m, n)
+        C = CUDA.zeros(Float32, m, n)
+        ct.Tiled(C) .= ct.Tiled(A) .+ ct.Tiled(B)
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "3D element-wise" begin
+        A = CUDA.rand(Float32, 64, 64, 4)
+        B = CUDA.rand(Float32, 64, 64, 4)
+        C = CUDA.zeros(Float32, 64, 64, 4)
+        ct.Tiled(C) .= ct.Tiled(A) .+ ct.Tiled(B)
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "ct.@. expands to Tiled" begin
+        ex = @macroexpand ct.@. C = A + B
+        # The macro should produce Tiled() wrapping, not plain dotted calls
+        @test occursin("Tiled", string(ex))
+    end
+
+    @testset "ct.@. in-place" begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        B = CUDA.rand(Float32, n)
+        C = CUDA.zeros(Float32, n)
+        ct.@. C = A + B
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "ct.@. with function" begin
+        n = 1024
+        A = CUDA.rand(Float32, n) .+ 0.1f0
+        C = CUDA.zeros(Float32, n)
+        ct.@. C = A + sin(A)
+        @test Array(C) ≈ Array(A) .+ sin.(Array(A)) rtol=1e-5
+    end
+
+    @testset "ct.@. with scalar" begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        C = CUDA.zeros(Float32, n)
+        ct.@. C = A + 2.0f0
+        @test Array(C) ≈ Array(A) .+ 2.0f0
+    end
+
+    @testset "allocating copy" begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        B = CUDA.rand(Float32, n)
+        C = ct.Tiled(A) .+ ct.Tiled(B)
+        @test C isa CuArray
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "allocating ct.@." begin
+        n = 1024
+        A = CUDA.rand(Float32, n)
+        B = CUDA.rand(Float32, n)
+        C = ct.@. A + B
+        @test C isa CuArray
+        @test Array(C) ≈ Array(A) .+ Array(B)
+    end
+
+    @testset "leading singleton dim" begin
+        A = CUDA.rand(Float32, 1, 1024)
+        B = similar(A)
+        ct.Tiled(B) .= ct.Tiled(A) .+ 1.0f0
+        @test Array(B) ≈ Array(A) .+ 1.0f0
+    end
+
+    @testset "double leading singleton" begin
+        A = CUDA.rand(Float32, 1, 1, 512)
+        B = similar(A)
+        ct.Tiled(B) .= ct.Tiled(A) .* 2.0f0
+        @test Array(B) ≈ Array(A) .* 2.0f0
+    end
+
+    @testset "small leading dim" begin
+        A = CUDA.rand(Float32, 4, 1024)
+        B = similar(A)
+        ct.Tiled(B) .= ct.Tiled(A) .+ ct.Tiled(A)
+        @test Array(B) ≈ 2 .* Array(A)
+    end
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index 9330bab9..7e8a444d 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -43,7 +43,7 @@ args = parse_args(ARGS)
 if filter_tests!(testsuite, args)
     cuda_functional = CUDA.functional()
     filter!(testsuite) do (test, _)
-        if startswith(test, "execution/") || startswith(test, "examples/")
+        if startswith(test, "device/") || startswith(test, "host/") || startswith(test, "examples/")
             return cuda_functional
         else
             return true