diff --git a/docs/src/tutorials/autotune.md b/docs/src/tutorials/autotune.md
index d518d57f7..ad87d6354 100644
--- a/docs/src/tutorials/autotune.md
+++ b/docs/src/tutorials/autotune.md
@@ -73,7 +73,7 @@ Control which matrix size ranges to test:
 # :small  - 20×20 to 100×100 (small problems)  
 # :medium - 100×100 to 300×300 (typical problems)
 # :large  - 300×300 to 1000×1000 (larger problems)
-# :big    - 10000×10000 to 100000×100000 (GPU/HPC scale)
+# :big    - 10000×1000 to 20000x20000 (GPU/HPC scale)
 
 # Default: test tiny through large
 results = autotune_setup()  # uses [:tiny, :small, :medium, :large]
@@ -449,4 +449,4 @@ LinearSolveAutotune provides a comprehensive system for benchmarking and optimiz
 - Performance visualization
 - Preference setting for automatic algorithm selection (in development)
 
-By running autotune and optionally sharing your results, you help improve LinearSolve.jl's performance for everyone in the Julia community.
\ No newline at end of file
+By running autotune and optionally sharing your results, you help improve LinearSolve.jl's performance for everyone in the Julia community.
diff --git a/lib/LinearSolveAutotune/src/benchmarking.jl b/lib/LinearSolveAutotune/src/benchmarking.jl
index aa2fcd4d3..f70be4557 100644
--- a/lib/LinearSolveAutotune/src/benchmarking.jl
+++ b/lib/LinearSolveAutotune/src/benchmarking.jl
@@ -187,7 +187,7 @@ Size categories:
 - `:small` - 20:20:100 (for small problems)
 - `:medium` - 100:50:300 (for typical problems)
 - `:large` - 300:100:1000 (for larger problems)
-- `:big` - 10000:1000:100000 (for very large/GPU problems)
+- `:big` - vcat(1000:2000:10000, 10000:5000:20000) (for very large/GPU problems)
 """
 function get_benchmark_sizes(size_categories::Vector{Symbol})
     sizes = Int[]
@@ -202,7 +202,7 @@ function get_benchmark_sizes(size_categories::Vector{Symbol})
         elseif category == :large
             append!(sizes, 300:100:1000)
         elseif category == :big
-            append!(sizes, 10000:1000:100000)
+            append!(sizes, vcat(1000:2000:10000, 10000:5000:20000))
         else
             @warn "Unknown size category: $category. Skipping."
         end
@@ -236,7 +236,7 @@ function categorize_results(df::DataFrame)
         ("small (20-100)", 21:100),
         ("medium (100-300)", 101:300),
         ("large (300-1000)", 301:1000),
-        ("big (10000+)", 10000:typemax(Int))
+        ("big (1000+)", 1000:typemax(Int))
     ]
 
     # Get unique element types