diff --git a/src/mixtures/mixturemodel.jl b/src/mixtures/mixturemodel.jl
index 7f3664290..38e9d59e5 100644
--- a/src/mixtures/mixturemodel.jl
+++ b/src/mixtures/mixturemodel.jl
@@ -477,6 +477,34 @@ rand(rng::AbstractRNG, s::MixtureSampler{Univariate}) =
 rand(rng::AbstractRNG, d::MixtureModel{Univariate}) =
     rand(rng, component(d, rand(rng, d.prior)))
 
+function rand(rng::AbstractRNG, d::MixtureModel{Univariate}, n::Int)
+    counts = rand(rng, Multinomial(n, probs(d.prior)))
+
+    # Find the component with the maximum count to minimize resizing
+    max_count_idx = argmax(counts)
+    max_count = counts[max_count_idx]
+
+    # Sample from the component with maximum count first and use it directly
+    x = rand(rng, component(d, max_count_idx), max_count)
+
+    # Resize to the full size and continue with other components
+    resize!(x, n)
+    offset = max_count
+
+    for i in eachindex(counts)
+        if i != max_count_idx
+            ni = counts[i]
+            if ni > 0
+                c = component(d, i)
+                last_offset = offset + ni - 1
+                rand!(rng, c, @view(x[(begin+offset):(begin+last_offset)]))
+                offset = last_offset + 1
+            end
+        end
+    end
+    return shuffle!(rng, x)
+end
+
 # multivariate mixture sampler for a vector
 _rand!(rng::AbstractRNG, s::MixtureSampler{Multivariate}, x::AbstractVector{<:Real}) =
     @inbounds rand!(rng, s.csamplers[rand(rng, s.psampler)], x)
diff --git a/src/truncate.jl b/src/truncate.jl
index 48d62b015..f0a31ddc6 100644
--- a/src/truncate.jl
+++ b/src/truncate.jl
@@ -233,6 +233,64 @@ function rand(rng::AbstractRNG, d::Truncated)
     end
 end
 
+function rand(rng::AbstractRNG, d::Truncated, n::Int)
+    n == 0 && return rand(d.untruncated, 0)
+
+    d0 = d.untruncated
+    tp = d.tp
+    lower = d.lower
+    upper = d.upper
+
+    # Use the same three regimes as the scalar version
+    if tp > 0.25
+        # Regime 1: Rejection sampling with batch optimization
+        # Get the correct type and memory by sampling from the untruncated distribution
+        samples = rand(rng, d0, n)
+        n_collected = 0
+        max_batch = 0
+        batch_buffer = Vector{eltype(samples)}()
+        while n_collected < n
+            n_remaining = n - n_collected
+            n_expected = n_remaining / tp
+            δn_expected = sqrt(n_remaining * tp * (1 - tp))
+            n_batch_f = n_expected + 3δn_expected
+            n_batch = ceil(Int, n_batch_f)
+            if n_batch > max_batch
+                resize!(batch_buffer, n_batch)
+                max_batch = n_batch
+            end
+            rand!(rng, d0, batch_buffer)
+            for i in 1:n_batch
+                s = batch_buffer[i]
+                if _in_closed_interval(s, lower, upper)
+                    n_collected += 1
+                    samples[n_collected] = s
+                    n_collected == n && break
+                end
+            end
+        end
+        return samples
+    elseif tp > sqrt(eps(typeof(float(tp))))
+        # Regime 2: Quantile-based sampling
+        # Sample one value first to determine the correct type
+        sample_type = typeof(quantile(d0, d.lcdf + rand(rng) * d.tp))
+        samples = Vector{sample_type}(undef, n)
+        for i in 1:n
+            samples[i] = quantile(d0, d.lcdf + rand(rng) * d.tp)
+        end
+        return samples
+    else
+        # Regime 3: Log-space computation
+        # Sample one value first to determine the correct type
+        sample_type = typeof(invlogcdf(d0, logaddexp(d.loglcdf, d.logtp - randexp(rng))))
+        samples = Vector{sample_type}(undef, n)
+        for i in 1:n
+            samples[i] = invlogcdf(d0, logaddexp(d.loglcdf, d.logtp - randexp(rng)))
+        end
+        return samples
+    end
+end
+
 ## show
 
 function show(io::IO, d::Truncated)