diff --git a/tt-train/sources/examples/nano_gpt/main.cpp b/tt-train/sources/examples/nano_gpt/main.cpp
index a0732c43156..3495584bf6a 100644
--- a/tt-train/sources/examples/nano_gpt/main.cpp
+++ b/tt-train/sources/examples/nano_gpt/main.cpp
@@ -141,6 +141,8 @@ struct TrainingConfig {
     uint32_t max_steps = 5000;
     float learning_rate = 3e-4F;
     float weight_decay = 1e-2F;
+    // works only for AdamW
+    bool use_kahan_summation = false;
     std::string model_path;
     std::string data_path;
     ttml::models::gpt2::TransformerConfig transformer_config;
@@ -157,6 +159,7 @@ TrainingConfig parse_config(const YAML::Node &yaml_config) {
     config.max_steps = training_config["max_steps"].as<uint32_t>();
     config.learning_rate = training_config["learning_rate"].as<float>();
     config.weight_decay = training_config["weight_decay"].as<float>();
+    config.use_kahan_summation = training_config["use_kahan_summation"].as<bool>(config.use_kahan_summation);
     config.model_path = training_config["model_path"].as<std::string>("");
     config.data_path = training_config["data_path"].as<std::string>(std::string(DATA_FOLDER) + "/shakespeare.txt");
     config.transformer_config = ttml::models::gpt2::read_config(training_config["transformer_config"]);
@@ -295,9 +298,11 @@ int main(int argc, char **argv) {
     auto adamw_params = ttml::optimizers::AdamWConfig();
     adamw_params.lr = config.learning_rate;
     adamw_params.weight_decay = config.weight_decay;
+    adamw_params.use_kahan_summation = config.use_kahan_summation;
     fmt::print("AdamW configuration:\n");
     fmt::print("    Learning rate: {}\n", adamw_params.lr);
     fmt::print("    Weight decay: {}\n", adamw_params.weight_decay);
+    fmt::print("    Use Kahan summation: {}\n", adamw_params.use_kahan_summation);
     auto optimizer = ttml::optimizers::AdamW(model->parameters(), adamw_params);
 
     if (!config.model_path.empty() && std::filesystem::exists(config.model_path)) {
diff --git a/tt-train/sources/ttml/optimizers/adamw.cpp b/tt-train/sources/ttml/optimizers/adamw.cpp
index c11724ac17d..8770b9da5a8 100644
--- a/tt-train/sources/ttml/optimizers/adamw.cpp
+++ b/tt-train/sources/ttml/optimizers/adamw.cpp
@@ -16,6 +16,7 @@ namespace {
 
 const std::string kFirstMoment = "first_moment/";
 const std::string kSecondMoment = "second_moment/";
+const std::string kKahanCompensation = "kahan_compensation/";
 
 }  // namespace
 
@@ -23,6 +24,10 @@ namespace ttml::optimizers {
 
 MorehAdamW::MorehAdamW(autograd::NamedParameters parameters, const AdamWConfig& config) :
     OptimizerBase(std::move(parameters)), m_config(config) {
+    if (m_config.use_kahan_summation) {
+        throw std::runtime_error("MorehAdamW: Kahan summation is not supported. Use default AdamW instead.");
+    }
+
     for (const auto& [key, tensor_ptr] : m_parameters) {
         if (tensor_ptr->get_requires_grad()) {
             m_first_moment.emplace(
@@ -137,6 +142,13 @@ AdamW::AdamW(autograd::NamedParameters parameters, const AdamWConfig& config) :
                 autograd::create_tensor(
                     core::zeros_like(tensor_ptr->get_value(autograd::PreferredPrecision::FULL)),
                     /* requires_grad */ false));
+            if (m_config.use_kahan_summation) {
+                m_kahan_compensation.emplace(
+                    key,
+                    autograd::create_tensor(
+                        core::zeros_like(tensor_ptr->get_value(autograd::PreferredPrecision::FULL)),
+                        /* requires_grad */ false));
+            }
         }
     }
 }
@@ -188,11 +200,29 @@ void AdamW::step() {
         // weights -= lr * first_moment_hat / (sqrt(second_moment_hat) + epsilon)
         first_moment_ptr->set_value(first_moment);
         second_moment_ptr->set_value(second_moment);
-        tensor_ptr->set_value(ttnn::subtract(
-            tensor_ptr->get_value(autograd::PreferredPrecision::FULL),
-            ttnn_fixed::divide(
-                ttnn::multiply(first_moment_hat, m_config.lr),
-                ttnn::add(ttnn::sqrt(second_moment_hat), m_config.epsilon))));
+
+        auto update_tensor = ttnn_fixed::divide(
+            ttnn::multiply(first_moment_hat, -m_config.lr), ttnn::add(ttnn::sqrt(second_moment_hat), m_config.epsilon));
+
+        if (!m_config.use_kahan_summation) {
+            tensor_ptr->set_value(ttnn::add(tensor_ptr->get_value(autograd::PreferredPrecision::FULL), update_tensor));
+        } else {
+            auto value_tensor = tensor_ptr->get_value(autograd::PreferredPrecision::FULL);
+
+            const auto& kahan_compensation_ptr = m_kahan_compensation.at(key);
+            // A running compensation for lost low-order bits
+            auto compensation_tensor = kahan_compensation_ptr->get_value(autograd::PreferredPrecision::FULL);
+            // Adjust the update with the compensation
+            auto adjusted_update = ttnn::subtract(update_tensor, compensation_tensor);
+            // Update the value with the adjusted update
+            auto result = ttnn::add(value_tensor, adjusted_update);
+            // (result - value_tensor) cancels the high-order part of adjusted_update;
+            // subtracting adjusted_update recovers negative (low part of adjusted_update)
+            compensation_tensor = ttnn::subtract(ttnn::subtract(result, value_tensor), adjusted_update);
+
+            tensor_ptr->set_value(result);
+            kahan_compensation_ptr->set_value(compensation_tensor);
+        }
     }
 }
 
@@ -206,6 +236,10 @@ void AdamW::step() {
         state_dict.emplace(kSecondMoment + key, second_moment);
     }
 
+    for (const auto& [key, kahan_compensation] : m_kahan_compensation) {
+        state_dict.emplace(kKahanCompensation + key, kahan_compensation);
+    }
+
     return state_dict;
 }
 
@@ -215,6 +249,8 @@ void AdamW::set_state_dict(const autograd::NamedParameters& dict) {
             m_first_moment[key.substr(kFirstMoment.size())] = tensor;
         } else if (key.starts_with(kSecondMoment)) {
             m_second_moment[key.substr(kSecondMoment.size())] = tensor;
+        } else if (key.starts_with(kKahanCompensation)) {
+            m_kahan_compensation[key.substr(kKahanCompensation.size())] = tensor;
         } else {
             throw std::runtime_error(fmt::format("AdamW: Invalid key in state dict. Key = {}", key));
         }
diff --git a/tt-train/sources/ttml/optimizers/adamw.hpp b/tt-train/sources/ttml/optimizers/adamw.hpp
index 001b3e5c683..da3847f66db 100644
--- a/tt-train/sources/ttml/optimizers/adamw.hpp
+++ b/tt-train/sources/ttml/optimizers/adamw.hpp
@@ -16,6 +16,9 @@ struct AdamWConfig {
     float epsilon{1e-8F};
     float weight_decay{0.01F};
     // TODO: add amsgrad
+
+    // flag to enable kahan summation to reduce floating point errors
+    bool use_kahan_summation{false};
 };
 
 class MorehAdamW : public OptimizerBase {
@@ -58,6 +61,7 @@ class AdamW : public OptimizerBase {
     AdamWConfig m_config;
     autograd::NamedParameters m_first_moment;
     autograd::NamedParameters m_second_moment;
+    autograd::NamedParameters m_kahan_compensation;
 };
 
 }  // namespace ttml::optimizers