diff --git a/pyproject.toml b/pyproject.toml
index f5b1a711..c963ea3f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,9 @@ dependencies = [
     "transformers>=4.44.2",
     "datasets>=3.0.0",
     "pydantic_config @ git+https://github.com/samsja/pydantic_config.git@e529c9c",
-    "einops"
+    "einops",
+    "torchdata>=0.8.0",
+    "fsspec[gcs]>=2024.3.1",
 ]
 
 [project.optional-dependencies]
diff --git a/src/zeroband/checkpoint.py b/src/zeroband/checkpoint.py
new file mode 100644
index 00000000..9bc0f003
--- /dev/null
+++ b/src/zeroband/checkpoint.py
@@ -0,0 +1,225 @@
+from dataclasses import dataclass
+import multiprocessing
+import os
+import time
+from typing import Any
+from fsspec.generic import rsync as rsync_fsspec
+import torch
+from torch import nn
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LambdaLR
+from torchdata.stateful_dataloader import StatefulDataLoader
+import torch.distributed.checkpoint as dcp
+from torch.distributed.checkpoint.state_dict import (
+    set_optimizer_state_dict,
+    set_model_state_dict,
+    get_model_state_dict,
+    get_optimizer_state_dict,
+    StateDictOptions,
+)
+from torch.distributed.checkpoint.stateful import Stateful
+from zeroband.utils.logging import get_logger
+import warnings
+import logging
+
+from zeroband.utils.world_info import get_world_info
+
+## code inspired by torchtitan https://github.com/pytorch/torchtitan/blob/main/torchtitan/checkpoint.py
+
+
+@dataclass
+class TrainingProgress(Stateful):
+    total_tokens: int
+    outer_step: int
+    step: int
+
+    def state_dict(self) -> dict[str, Any]:
+        return {"total_tokens": self.total_tokens, "outer_step": self.outer_step, "step": self.step}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        self.total_tokens = state_dict["total_tokens"]
+        self.outer_step = state_dict["outer_step"]
+        self.step = state_dict["step"]
+
+
+class ModelWrapper(Stateful):
+    def __init__(self, model: nn.Module) -> None:
+        self.model = model
+
+    def state_dict(self) -> dict[str, Any]:
+        return get_model_state_dict(self.model)
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        set_model_state_dict(model=self.model, model_state_dict=state_dict, options=StateDictOptions(strict=False))
+
+
+class OptimizerWrapper(Stateful):
+    def __init__(
+        self,
+        model: nn.Module,
+        optim: torch.optim.Optimizer,
+    ) -> None:
+        self.model = model
+        self.optim = optim
+
+    def state_dict(self) -> dict[str, Any]:
+        return get_optimizer_state_dict(
+            model=self.model, optimizers=self.optim, options=StateDictOptions(flatten_optimizer_state_dict=True)
+        )
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        set_optimizer_state_dict(
+            model=self.model, optimizers=self.optim, optim_state_dict=state_dict, options=StateDictOptions(strict=False)
+        )
+
+
+class CkptManager:
+    """Its name CkptManager because I (sami) always misstyped chekcpoint.
+
+    Checkpoint are saved in a folder with the following structure:
+    ckpt_path/
+        step_0/
+            _0_0.pt
+            _1_0.pt
+            ...
+        step_1/
+            ...
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        optimizer: Optimizer,
+        scheduler: LambdaLR,
+        dataloader: StatefulDataLoader,
+        training_progress: TrainingProgress,
+        diloco_offloaded_param_list: list[nn.Parameter] | None,
+        diloco_offloaded_optimizer: Optimizer | None,
+    ):
+        self.model = ModelWrapper(model)
+        self.optimizer = OptimizerWrapper(model, optimizer)
+        self.scheduler = scheduler
+        self.dataloader = dataloader
+        self.training_progress = training_progress
+
+        # states can only be stateful object, hence we need to wrap Model and Optimizer
+        self.states: dict[str, Stateful] = {
+            "model": self.model,
+            "optimizer": self.optimizer,
+            "scheduler": self.scheduler,
+            # "dataloader": self.dataloader, # ignoring dataloader for now as each rank has its own dataloader
+            "training_progress": self.training_progress,
+        }
+
+        assert (diloco_offloaded_param_list is None) == (
+            diloco_offloaded_optimizer is None
+        ), "diloco_offloaded_model and diloco_offloaded_optimizer must be both None or both have values"
+
+        self.diloco_offloaded_optimizer = diloco_offloaded_optimizer  # he we don't use Wrapper because it failed
+        # which might make the ckpt less generic in term of loading from different number of device. FSDP ckpt seems to be a mess tho
+        self.diloco_offloaded_param_list = diloco_offloaded_param_list
+
+        if diloco_offloaded_optimizer is not None:
+            # even if the diloco_offloaded target the cpu list model, we still use the gpu model to load and save state.
+            # main reason is that we actually don't a cpu model but just a list of cpu parameters.
+            self.states["diloco_optimizer"] = self.diloco_offloaded_optimizer
+
+        self._logger = get_logger()
+
+        self.async_save_process: list[multiprocessing.Process] = []
+
+    def save(self, ckpt_path: str, remote_ckpt_path: str | None) -> None:
+        """
+        Each rank will save the right shard of the model and optimizer.
+
+        Saving is done inplace
+        """
+
+        time_start = time.perf_counter()
+        world_info = get_world_info()
+
+        ckpt_path = os.path.join(ckpt_path, f"step_{self.training_progress.step}")
+        if self.diloco_offloaded_optimizer:
+            # here we save model and offloaded optimizer on each diloco rank even tho they are the same
+            # this is done for two reasons:
+            #   * if the nodes don't share a filesystem nor a remote path, they still save all of the data
+            #   * its easier to implement and avoid race condition on the shared data.
+            ckpt_path = os.path.join(ckpt_path, f"diloco_{world_info.diloco_rank}")
+
+        catch_warning = self._logger.getEffectiveLevel() <= logging.INFO
+
+        with warnings.catch_warnings():
+            # pytorch has an annoying warning when saving the optimizer state https://github.com/pytorch/pytorch/issues/136907
+            # we can ignore it if we are not logging in DEBUG mode
+            if catch_warning:
+                warnings.simplefilter("ignore")
+
+            dcp.save(self.states, checkpoint_id=ckpt_path)
+
+            ## the next part is a fix so that each rank save a different dataloader rank. It not efficient because it reads the state two times from disk
+            with open(os.path.join(ckpt_path, f"__{world_info.local_rank}_0.pt"), "wb") as f:
+                torch.save({"data_loader": self.dataloader.state_dict()}, f)
+
+        self._logger.info(f"Saved checkpoint to {ckpt_path} in {time.perf_counter() - time_start} seconds")
+
+        if remote_ckpt_path is not None:
+            self._async_save_remote(ckpt_path, remote_ckpt_path)
+
+    def _async_save_remote(self, ckpt_path: str, remote_ckpt_path: str):
+        """asyncronously rsync a ckpt folder to a remote location. Using fsspec to handle remote cloud storage without to install
+        specific libraries (e.g. s3fs)
+        """
+
+        def rsync():
+            time_start = time.perf_counter()
+            self._logger.info(f"start pushing {ckpt_path} to {remote_ckpt_path} asynchronously")
+            rsync_fsspec(ckpt_path, destination=remote_ckpt_path)
+            self._logger.info(
+                f"finish pushing {ckpt_path} to {remote_ckpt_path} in {time.perf_counter() - time_start} seconds"
+            )
+
+        processes = multiprocessing.Process(target=rsync, daemon=True)
+        processes.start()
+
+        self.async_save_process.append(processes)
+
+    def wait_async_save_process(self):
+        """
+        wait for all async save process to finish
+        """
+        for process in self.async_save_process:
+            process.join()
+
+    def _del__(self):
+        self.wait_async_save_process()
+
+    def load(self, resume_ckpt_path: str) -> None:
+        """
+        loading should be done after fsdp wrap and optimizer init.
+        Each rank will load the right shard of the model and optimizer.
+        All rank will load the global states (scheduler, step, total_tokens, dataloader).
+
+        `resume_ckpt_path` should point to a specific step and not to the base ckpt folder. Example: `ckpt_path/step_100`
+
+        Loading is done inplace
+        """
+        time_start = time.perf_counter()
+
+        world_info = get_world_info()
+        if self.diloco_offloaded_param_list is not None:
+            resume_ckpt_path = os.path.join(resume_ckpt_path, f"diloco_{world_info.diloco_rank}")
+
+        self.states = dcp.load(self.states, checkpoint_id=resume_ckpt_path)
+
+        # since we don't load the param list from the state dict as its the same as the model one we just copy
+        if self.diloco_offloaded_param_list is not None:
+            for param_offloaded, param_model in zip(self.diloco_offloaded_param_list, self.model.model.parameters()):
+                param_offloaded.data.copy_(param_model.data)
+
+        ## the next part is a fix so that each rank save a different dataloader rank. It not efficient because it reads the state two times from disk
+        with open(os.path.join(resume_ckpt_path, f"__{world_info.local_rank}_0.pt"), "rb") as f:
+            rank_state_dict = torch.load(f)
+
+        self.dataloader.load_state_dict(rank_state_dict["data_loader"])
+
+        self._logger.info(f"Loaded checkpoint from {resume_ckpt_path} in {time.perf_counter() - time_start} seconds")
diff --git a/src/zeroband/data.py b/src/zeroband/data.py
index 61a1a986..1a093d1c 100644
--- a/src/zeroband/data.py
+++ b/src/zeroband/data.py
@@ -4,6 +4,7 @@
 import torch
 from torch.utils.data import DataLoader
 from torch.utils.data import IterableDataset
+from torchdata.stateful_dataloader import StatefulDataLoader
 
 from datasets import load_dataset
 from datasets.distributed import split_dataset_by_node
@@ -79,7 +80,7 @@ def tokenize_function(data):
 
     data_collator = collate_causal_mask(max_seq_length=seq_length, pad_id=tokenizer.pad_token_id, ignore_index=-100)
 
-    return DataLoader(
+    return StatefulDataLoader(
         train_dataset,
         collate_fn=data_collator,
         batch_size=batch_size,
diff --git a/src/zeroband/train.py b/src/zeroband/train.py
index 0d93d57a..9879a17e 100644
--- a/src/zeroband/train.py
+++ b/src/zeroband/train.py
@@ -26,6 +26,7 @@
 from zeroband.models.llama import get_model
 from zeroband.utils.world_info import get_world_info
 from zeroband.utils.logging import get_logger
+from zeroband.checkpoint import CkptManager, TrainingProgress
 
 
 class DataConfig(BaseConfig):
@@ -53,6 +54,13 @@ class TrainConfig(BaseConfig):
     log_model_hash: bool = False
 
 
+class CkptConfig(BaseConfig):
+    path: str
+    interval: int
+
+    remote_path: str | None = None  # could be a s3 path
+
+
 class Config(BaseConfig):
     # main config
     name_model: Literal["debugmodel", "150M", "271M", "1B", "7B", "13B", "26B", "70B"] = "150M"
@@ -67,6 +75,9 @@ class Config(BaseConfig):
     optim: OptimConfig = OptimConfig()
     train: TrainConfig
 
+    ckpt: CkptConfig | None = None
+    resume: str | None = None
+
 
 def train(config: Config):
     sharding_strategy = get_sharding_strategy(config.train.sharding_strategy)
@@ -78,6 +89,11 @@ def train(config: Config):
     assert batch_size % config.train.micro_bs == 0
     gradient_accumulation_steps = batch_size // config.train.micro_bs
 
+    if config.ckpt is not None and config.ckpt.interval is not None and config.diloco is not None:
+        assert (
+            config.ckpt.interval % config.diloco.inner_steps == 0
+        ), "ckpt interval must be a multiple of diloco inner steps as we only save at the end of an outer step"
+
     tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)
     tokenizer.pad_token = "</s>"  # todo(sami): remove padding tokens once we have context stuffing
 
@@ -128,10 +144,7 @@ def train(config: Config):
         use_orig_params=True,
         process_group=elastic_device_mesh.local_pg if config.diloco is not None else None,
     )
-
-    if config.train.torch_compile:
-        model = torch.compile(model)
-    logger.debug("model compiled and fsdped")
+    logger.debug("model fsdped")
 
     # Setup optimizers
     inner_optimizer = torch.optim.AdamW(
@@ -150,6 +163,27 @@ def train(config: Config):
         num_training_steps=config.optim.total_steps,
     )
 
+    training_progress = TrainingProgress(total_tokens=0, outer_step=0, step=0)
+
+    ckpt_manager = CkptManager(
+        model=model,
+        optimizer=inner_optimizer,
+        scheduler=scheduler,
+        dataloader=train_dataloader,
+        training_progress=training_progress,
+        diloco_offloaded_optimizer=diloco.outer_optimizer if config.diloco is not None else None,
+        diloco_offloaded_param_list=diloco.param_list_cpu if config.diloco is not None else None,
+    )
+
+    if config.train.torch_compile:
+        # we need to compile AFTER creating the CKPT manager, DON'T ASK ME WHY
+        model = torch.compile(model)
+        logger.debug("model compiled")
+
+    if config.resume is not None:
+        # all is inplace
+        ckpt_manager.load(resume_ckpt_path=config.resume)
+
     model.train()
 
     if world_info.rank == 0:
@@ -158,7 +192,6 @@ def train(config: Config):
 
     train_dataloader_iterator = iter(train_dataloader)
 
-    outer_step = 0
     num_inner_steps = config.diloco.inner_steps if config.diloco is not None else 1
     perf_counter = PerfCounter(window_size=10)
 
@@ -166,9 +199,9 @@ def train(config: Config):
     while True:
         if num_inner_steps > 1:
             # if we don't use diloco we don't print the outer step logs
-            logger.info(f"outer_step step: {outer_step}")
+            logger.info(f"outer_step step: {training_progress.outer_step}")
 
-        for inner_step in range(num_inner_steps):
+        for _inner_step in range(num_inner_steps):
             loss_batch = 0
 
             for grad_acc_step in range(gradient_accumulation_steps):
@@ -195,22 +228,30 @@ def train(config: Config):
             inner_optimizer.zero_grad()
 
             # logging
-            real_step = outer_step * num_inner_steps + inner_step + 1  # add + 1 because inner_step start at 0
+            training_progress.step += 1
             inner_lr = [group["lr"] for group in inner_optimizer.param_groups][0]
 
             dist.all_reduce(tensor=loss_batch, op=dist.ReduceOp.AVG, group=elastic_device_mesh.local_pg)
             # syncing loss across all data parallel rank within a nodes
 
-            perf_counter.count_tokens(config.data.seq_length * config.optim.batch_size)
+            new_tokens = config.data.seq_length * config.optim.batch_size
+            perf_counter.count_tokens(new_tokens)
+
+            if config.diloco is not None:
+                training_progress.total_tokens += new_tokens
+            else:
+                # we count the total tokens with respect to all diloco workers
+                # might need to tweak this as some worker might fail to join the all reduce later
+                training_progress.total_tokens += new_tokens * elastic_device_mesh.global_pg.size()
 
             metrics = {
                 "Loss": loss_batch.item(),
-                "step": real_step,
+                "step": training_progress.step,
                 "inner_lr": inner_lr,
                 "Perplexity": torch.exp(loss_batch).item(),
-                "total_tokens": real_step * config.optim.batch_size * config.data.seq_length,
+                "total_tokens": training_progress.total_tokens,
             }
-            log = f"step: {real_step}, loss: {loss_batch.item():.4f}"
+            log = f"step: {training_progress.step}, loss: {loss_batch.item():.4f}"
 
             tokens_per_second = perf_counter.get_tokens_per_second()
 
@@ -239,9 +280,17 @@ def train(config: Config):
                 with FSDP.summon_full_params(model):
                     logger.debug("Post diloco model: %s", get_module_signature(model))
 
-        outer_step += 1
+        training_progress.outer_step += 1
 
-        if real_step >= config.optim.total_steps:
+        if (
+            config.ckpt is not None
+            and training_progress.step > 0
+            and training_progress.step % config.ckpt.interval == 0
+        ):
+            # we only allow to checkpoint after a outer step. For non diloco training outer step = 1 anyway
+            ckpt_manager.save(config.ckpt.path, config.ckpt.remote_path)
+
+        if training_progress.step >= config.optim.total_steps:
             # we only allow to break outisde of the inner loop.
             # This avoid ending the training in the middle of a the inner loop
             # Since ckpt strategy and all reduce is done at the outer loop level.
@@ -250,6 +299,9 @@ def train(config: Config):
     if world_info.rank == 0:
         metric_logger.finish()
 
+    ckpt_manager.wait_async_save_process()
+    logger.info("Training finished, exiting ...")
+
 
 if __name__ == "__main__":
     # Allow eager fallback during production so that that the training runs dont die
diff --git a/src/zeroband/utils/world_info.py b/src/zeroband/utils/world_info.py
index 9b73f328..fcca5da2 100644
--- a/src/zeroband/utils/world_info.py
+++ b/src/zeroband/utils/world_info.py
@@ -27,6 +27,10 @@ def __init__(self):
     def __repr__(self):
         return f"WorldInfo(world_size={self.world_size}, rank={self.rank}, local_rank={self.local_rank}, local_world_size={self.local_world_size}, nnodes={self.nnodes}, global_unique_id={self.global_unique_id}, global_addr={self.global_addr}, global_port={self.global_port}, global_world_size={self.global_world_size}, global_rank={self.global_rank})"
 
+    @property
+    def diloco_rank(self):
+        return self.global_rank
+
 
 def get_world_info() -> WorldInfo:
     """
diff --git a/uv.lock b/uv.lock
index f0ec766e..84348844 100644
--- a/uv.lock
+++ b/uv.lock
@@ -140,6 +140,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/21/5b6702a7f963e95456c0de2d495f67bf5fd62840ac655dc451586d23d39a/attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2", size = 63001 },
 ]
 
+[[package]]
+name = "cachetools"
+version = "5.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c3/38/a0f315319737ecf45b4319a8cd1f3a908e29d9277b46942263292115eee7/cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a", size = 27661 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a4/07/14f8ad37f2d12a5ce41206c21820d8cb6561b728e51fad4530dff0552a67/cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292", size = 9524 },
+]
+
 [[package]]
 name = "certifi"
 version = "2024.8.30"
@@ -258,6 +267,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a5/52/45dab187f03d48c765b94db0464f5c10431756e47ae4cc6a8029a7d57a36/datasets-3.0.0-py3-none-any.whl", hash = "sha256:c23fefb6c953dcb1cd5f6deb6c502729c733ef98791e0c3f2d80c7ca2d9a01dd", size = 474265 },
 ]
 
+[[package]]
+name = "decorator"
+version = "5.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/66/0c/8d907af351aa16b42caae42f9d6aa37b900c67308052d10fdce809f8d952/decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330", size = 35016 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d5/50/83c593b07763e1161326b3b8c6686f0f4b0f24d5526546bee538c89837d6/decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186", size = 9073 },
+]
+
 [[package]]
 name = "dill"
 version = "0.3.8"
@@ -379,10 +397,31 @@ wheels = [
 ]
 
 [package.optional-dependencies]
+gcs = [
+    { name = "gcsfs" },
+]
 http = [
     { name = "aiohttp" },
 ]
 
+[[package]]
+name = "gcsfs"
+version = "2024.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "decorator" },
+    { name = "fsspec" },
+    { name = "google-auth" },
+    { name = "google-auth-oauthlib" },
+    { name = "google-cloud-storage" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7f/b1/c5ae16ad1d499f0cf10e3306f717eadae30dba64ec29236077b8fe661e7c/gcsfs-2024.6.1.tar.gz", hash = "sha256:e8858c7a893b2265e9bfce2fe270a024a2e348c74c23528801db388fc0224ed7", size = 79259 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bf/65/f467159d42a2ce4191f1f6ff319e75b5a14ab2cc080062dbf5821a80244c/gcsfs-2024.6.1-py2.py3-none-any.whl", hash = "sha256:13fd18095425e54e248870594fd155812723966b1bda3b102b3a5c44ec436a03", size = 34866 },
+]
+
 [[package]]
 name = "gitdb"
 version = "4.0.11"
@@ -407,6 +446,129 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e9/bd/cc3a402a6439c15c3d4294333e13042b915bbeab54edc457c723931fed3f/GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff", size = 207337 },
 ]
 
+[[package]]
+name = "google-api-core"
+version = "2.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "googleapis-common-protos" },
+    { name = "proto-plus" },
+    { name = "protobuf" },
+    { name = "requests" },
+]
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+
 [[package]]
 name = "tqdm"
 version = "4.66.5"
@@ -1793,10 +2043,12 @@ source = { editable = "." }
 dependencies = [
     { name = "datasets" },
     { name = "einops" },
+    { name = "fsspec", extra = ["gcs"] },
     { name = "numpy" },
     { name = "pydantic-config" },
     { name = "setuptools" },
     { name = "torch" },
+    { name = "torchdata" },
     { name = "transformers" },
 ]
 
@@ -1816,10 +2068,12 @@ dev = [
 requires-dist = [
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     { name = "einops" },
+    { name = "fsspec", extras = ["gcs"], specifier = ">=2024.3.1" },
     { name = "numpy" },
     { name = "pydantic-config", git = "https://github.com/samsja/pydantic_config.git?rev=e529c9c" },
     { name = "setuptools" },
     { name = "torch", specifier = "==2.4.1" },
+    { name = "torchdata", specifier = ">=0.8.0" },
     { name = "transformers", specifier = ">=4.44.2" },
     { name = "wandb", marker = "extra == 'all'" },
 ]