diff --git a/dorado/nn/MetalCRFModel.cpp b/dorado/nn/MetalCRFModel.cpp
index 9232c46f5..a18913d24 100644
--- a/dorado/nn/MetalCRFModel.cpp
+++ b/dorado/nn/MetalCRFModel.cpp
@@ -634,7 +634,7 @@ class MetalCaller {
             spdlog::debug("Physical memory available {} GB", physical_memory / (size_t{1} << 30));
 
             // Constrain the maximum batch size to use about half physical memory for decode buffers,
-            // with neural network GPU buffers and CPU buffers are assumed to occupy a subset of the
+            // with neural network GPU buffers and CPU buffers assumed to occupy a subset of the
             // remaining memory.  This generally constrains the batch size to use fewer than
             // the maximum GPU cores when running sup models on systems with a large GPU core
             // to system memory ratio.
@@ -645,25 +645,29 @@ class MetalCaller {
                      static_cast<size_t>(m_states) * sizeof(int16_t) +  // Posts
                      static_cast<size_t>(m_states) * sizeof(float));    // Back guides.
             spdlog::debug("decode_buffer_size_per_elem {}", decode_buffer_size_per_elem);
-            const int max_batch_size = std::min(
+            const int max_batch_size = std::clamp(
                     utils::pad_to(physical_memory / (2 * decode_buffer_size_per_elem),
                                   static_cast<size_t>(MTL_CORE_BATCH_SIZE)),
+                    static_cast<size_t>(MTL_CORE_BATCH_SIZE),
                     static_cast<size_t>(MTL_CORE_BATCH_SIZE * get_mtl_device_core_count()));
             spdlog::debug("max_batch_size {}", max_batch_size);
 
-            // Always try natural batch sizes for 1 GPU core and maximum we think is viable,
-            // which absent memory limits will be the full GPU core count.
-            const int min_batch_size = MTL_CORE_BATCH_SIZE;
-            std::set<int> test_batch_sizes{min_batch_size, max_batch_size};
+            // Subject to the above memory constraint, impose a minimum batch size
+            // that will use 1/4 of GPU cores for LSTM execution.
+            const int min_batch_size =
+                    std::min(MTL_CORE_BATCH_SIZE * get_mtl_device_core_count() / 4, max_batch_size);
+            spdlog::debug("min_batch_size {}", min_batch_size);
+
+            std::set<int> test_batch_sizes{max_batch_size};
 
             // Add some batch sizes evenly distributed in between.
-            const int kNumIntermediateSizes = 16;
+            const int kNumSmallerSizes = 16;
             const float test_size_increment = static_cast<float>(max_batch_size - min_batch_size) /
-                                              static_cast<float>(kNumIntermediateSizes + 1);
-            for (int i = 0; i < kNumIntermediateSizes; ++i) {
-                const int test_batch_size = utils::pad_to(
-                        static_cast<int>(static_cast<float>(i + 1) * test_size_increment),
-                        MTL_CORE_BATCH_SIZE);
+                                              static_cast<float>(kNumSmallerSizes);
+            for (int i = 0; i < kNumSmallerSizes; ++i) {
+                const int test_batch_size =
+                        utils::pad_to(min_batch_size + static_cast<size_t>(i * test_size_increment),
+                                      static_cast<size_t>(MTL_CORE_BATCH_SIZE));
                 test_batch_sizes.insert(test_batch_size);
             }