diff --git a/llama.cpp b/llama.cpp
index da72bf0a5..0ca439dcd 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3276,6 +3276,17 @@ struct llama_model_loader {
         return cur;
     }
 
+    size_t estimate_tensor_bytes(const std::string & name, const std::vector<int64_t> & ne, bool required = true) const {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+        if (cur == NULL) {
+            return 0;
+        }
+
+        int64_t nbytes = ggml_nbytes(cur);
+        return nbytes;
+    }
+
     struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
         const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
 
@@ -4354,25 +4365,73 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
 static int llm_determine_max_ngl(const llama_model_loader & ml, const llama_model & model, const int main_gpu) {
     const auto & hparams = model.hparams;
 
-    size_t available_gpu_memory = llama_get_available_device_memory(main_gpu);
+    // could become negative - use signed size_t
+    ssize_t available_gpu_memory = llama_get_available_device_memory(main_gpu);
+    int n_layer = hparams.n_layer;
 
-    // TODO: This is a rough, pretty inaccurate estimate, should implement using existing layer size and not guesstimating
-    size_t model_size = ml.n_bytes;
-    int32_t model_layers = hparams.n_layer;
-    size_t memory_per_layer = model_size / model_layers;
+    // "avoid a scenario where an application ooms because llama.cpp only left 5 MB of VRAM" - https://github.com/ggerganov/llama.cpp/pull/6502#discussion_r1555060962
+    available_gpu_memory -= 50 * MiB;
+
+    // determine tensor sizes
+    size_t total_size = 0;
+    size_t extra_size = 0;
+    std::vector<size_t> layer_sizes(n_layer);
+    for (int i = 0; i < ml.n_tensors; ++i) {
+        const struct ggml_tensor * tensor = (const struct ggml_tensor *) ml.get_tensor_meta(i);
+        std::vector<int64_t> tensor_ne = { tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3] };
+        size_t tensor_size = ml.estimate_tensor_bytes(tensor->name, tensor_ne);
+        //LLAMA_LOG_INFO("%s: %-30s: %12ld\n", __func__, tensor->name, tensor_size);
+        // all layer specific tensor names have the prefix "blk."
+        if (strncmp(tensor->name, "blk.", 4) == 0) {
+            int layer_no;
+            sscanf(tensor->name+4, "%d", &layer_no);
+            layer_sizes[layer_no] += tensor_size;
+        } else {
+            extra_size += tensor_size;
+        }
+        total_size += tensor_size;
+    }
 
     // TODO: get buffer size dynamically
-    int32_t buf_size = 400 * MiB;
-    int32_t buf_size_k = 200 * MiB;
-    int32_t buf_size_v = 200 * MiB;
+    size_t buf_size = 400 * MiB;
+    size_t buf_size_k = 200 * MiB;
+    size_t buf_size_v = 200 * MiB;
 
-    int32_t total_buf_size = buf_size + buf_size_k + buf_size_v;
+    ssize_t buffer_size = buf_size + buf_size_k + buf_size_v;
 
-    available_gpu_memory = available_gpu_memory - hparams.n_ctx_train; // context size
-    available_gpu_memory = available_gpu_memory - total_buf_size; // buffer size
+    // can/will be pretty big for large models
+    size_t n_ctx = hparams.n_ctx_train;
 
-    // Calculate the maximum number of layers that can fit into the GPU memory
-    int32_t max_ngl = std::floor(static_cast<float>(available_gpu_memory) / memory_per_layer);
+    size_t ctx_size =
+            ggml_tensor_overhead()*(ml.n_tensors + 1) +
+            ggml_tensor_overhead()*hparams.n_expert*n_layer;
+
+    size_t context_size = n_ctx*ctx_size;
+
+    // Calculate the maximum number of layers that can fit into the available GPU memory
+    int max_ngl = 0;
+    ssize_t used_memory = extra_size+buffer_size+context_size;
+    for (int i = 0; i < n_layer; ++i) {
+        LLAMA_LOG_INFO("%s: layer %2d size:        %12ld\n", __func__, i, layer_sizes[i]);
+        used_memory += layer_sizes[i];
+        if (used_memory > available_gpu_memory) {
+            break;
+        }
+        max_ngl++;
+    }
+
+    LLAMA_LOG_INFO("%s: extra size:           %12ld\n", __func__, extra_size);
+    LLAMA_LOG_INFO("%s: ----------------------------------\n", __func__);
+    LLAMA_LOG_INFO("%s: total size:           %12ld\n", __func__, total_size);
+    LLAMA_LOG_INFO("%s: available_gpu_memory: %12ld\n", __func__, available_gpu_memory);
+    LLAMA_LOG_INFO("%s: buffer size:          %12ld\n", __func__, buffer_size);
+    LLAMA_LOG_INFO("%s: total context size:   %12ld\n", __func__, context_size);
+    LLAMA_LOG_INFO("%s: used memory:          %12ld\n", __func__, used_memory);
+    LLAMA_LOG_INFO("%s: ----------------------------------\n", __func__);
+    LLAMA_LOG_INFO("%s: tokens in context:      %ld\n",    __func__, n_ctx);
+    LLAMA_LOG_INFO("%s: per token context size: %ld\n", __func__, ctx_size);
+    LLAMA_LOG_INFO("%s: layer_count:            %d\n",    __func__, n_layer);
+    LLAMA_LOG_INFO("%s: max_ngl:                %d\n",    __func__, max_ngl);
 
     return max_ngl;
 }
@@ -4394,7 +4453,7 @@ static bool llm_load_tensors(
 
     if (n_gpu_layers == -2) {
         n_gpu_layers = llm_determine_max_ngl(ml, model, main_gpu);
-        LLAMA_LOG_INFO("%s: automatically set n_gpu_layers = %d\n", __func__, n_gpu_layers);
+        LLAMA_LOG_INFO("%s: automatically set n_gpu_layers to %d\n", __func__, n_gpu_layers);
     }
 
     model.split_mode   = split_mode;