diff --git a/docs/source/api_ref_models.rst b/docs/source/api_ref_models.rst
index e658de7294..645e7f2c19 100644
--- a/docs/source/api_ref_models.rst
+++ b/docs/source/api_ref_models.rst
@@ -6,6 +6,31 @@ torchtune.models
 
 .. currentmodule:: torchtune.models
 
+llama3.3
+--------
+
+Text-only models from the 3.3 version of `Llama3 family <https://llama.meta.com/llama3/>`_.
+
+Important: You need to request access on `Hugging Face <https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct>`__ before downloading it.
+
+To download the Llama-3.3-70B-Instruct model:
+
+.. code-block:: bash
+
+    tune download meta-llama/Llama-3.3-70B-Instruct --ignore-patterns "original/consolidated.00.pth" --hf-token <HF_TOKEN>
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+
+    llama3_3.llama3_3_70b
+    llama3_3.lora_llama3_3_70b
+    llama3_3.qlora_llama3_3_70b
+
+.. note::
+
+    The Llama3.3 tokenizer reuses the :class:`~torchtune.models.llama3.llama3_tokenizer` class.
+
 llama3.2
 --------
 
diff --git a/recipes/configs/llama3_3/70B_full.yaml b/recipes/configs/llama3_3/70B_full.yaml
new file mode 100644
index 0000000000..8f96a5fbd7
--- /dev/null
+++ b/recipes/configs/llama3_3/70B_full.yaml
@@ -0,0 +1,138 @@
+# Config for multi-device full finetuning in full_finetune_distributed.py
+# using a Llama3.3 70B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-3.3-70B-Instruct --ignore-patterns "original/consolidated*"
+#
+# To launch on 8 devices, run the following command from root:
+#   tune run --nproc_per_node 8 full_finetune_distributed --config llama3_3/70B_full
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 8 full_finetune_distributed --config llama3_3/70B_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config is only tested on an 8xA100 machine.
+#
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Llama-3.3-70B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+# Dataset
+dataset:
+  _component_: torchtune.datasets.alpaca_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_3.llama3_3_70b
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-3.3-70B-Instruct/
+  checkpoint_files: [
+    model-00001-of-00030.safetensors,
+    model-00002-of-00030.safetensors,
+    model-00003-of-00030.safetensors,
+    model-00004-of-00030.safetensors,
+    model-00005-of-00030.safetensors,
+    model-00006-of-00030.safetensors,
+    model-00007-of-00030.safetensors,
+    model-00008-of-00030.safetensors,
+    model-00009-of-00030.safetensors,
+    model-00010-of-00030.safetensors,
+    model-00011-of-00030.safetensors,
+    model-00012-of-00030.safetensors,
+    model-00013-of-00030.safetensors,
+    model-00014-of-00030.safetensors,
+    model-00015-of-00030.safetensors,
+    model-00016-of-00030.safetensors,
+    model-00017-of-00030.safetensors,
+    model-00018-of-00030.safetensors,
+    model-00019-of-00030.safetensors,
+    model-00020-of-00030.safetensors,
+    model-00021-of-00030.safetensors,
+    model-00022-of-00030.safetensors,
+    model-00023-of-00030.safetensors,
+    model-00024-of-00030.safetensors,
+    model-00025-of-00030.safetensors,
+    model-00026-of-00030.safetensors,
+    model-00027-of-00030.safetensors,
+    model-00028-of-00030.safetensors,
+    model-00029-of-00030.safetensors,
+    model-00030-of-00030.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-3.3-70B-Instruct/
+  model_type: LLAMA3
+resume_from_checkpoint: False
+
+# Fine-tuning arguments
+batch_size: 2
+epochs: 1
+
+optimizer:
+  _component_: torch.optim.AdamW
+  lr: 2e-5
+  # Note: highly recommended to use fused=True optimizer flag
+  # with CPU offload for faster optimizer step.
+  fused: True
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+
+
+# Training env
+device: cuda
+
+# Memory management
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+custom_sharded_layers: ['tok_embeddings', 'output']  # Layers to shard separately (useful for large vocab size models). Lower Memory, but lower speed.
+fsdp_cpu_offload: True
+compile: False  # pytorch compile, set to true for better perf/memory
+optimizer_in_bwd: False  # True saves memory. Requires gradient_accumulation_steps=1
+
+# Reduced precision
+dtype: bf16
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+output_dir: /tmp/full-llama3_3-finetune
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
diff --git a/recipes/configs/llama3_3/70B_lora.yaml b/recipes/configs/llama3_3/70B_lora.yaml
new file mode 100644
index 0000000000..901c700c22
--- /dev/null
+++ b/recipes/configs/llama3_3/70B_lora.yaml
@@ -0,0 +1,132 @@
+# Config for multi-device LoRA in lora_finetune_distributed.py
+# using a Llama3.3 70B model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-3.3-70B-Instruct --ignore-patterns "original/consolidated*"
+#
+# This config needs 8 GPUs to run
+#   tune run --nproc_per_node 8 lora_finetune_distributed --config llama3_3/70B_lora
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_3.lora_llama3_3_70b
+  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: False
+  lora_rank: 16  # higher increases accuracy and memory
+  lora_alpha: 32  # usually alpha=2*rank
+  lora_dropout: 0.0
+
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Llama-3.3-70B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-3.3-70B-Instruct/
+  checkpoint_files: [
+    model-00001-of-00030.safetensors,
+    model-00002-of-00030.safetensors,
+    model-00003-of-00030.safetensors,
+    model-00004-of-00030.safetensors,
+    model-00005-of-00030.safetensors,
+    model-00006-of-00030.safetensors,
+    model-00007-of-00030.safetensors,
+    model-00008-of-00030.safetensors,
+    model-00009-of-00030.safetensors,
+    model-00010-of-00030.safetensors,
+    model-00011-of-00030.safetensors,
+    model-00012-of-00030.safetensors,
+    model-00013-of-00030.safetensors,
+    model-00014-of-00030.safetensors,
+    model-00015-of-00030.safetensors,
+    model-00016-of-00030.safetensors,
+    model-00017-of-00030.safetensors,
+    model-00018-of-00030.safetensors,
+    model-00019-of-00030.safetensors,
+    model-00020-of-00030.safetensors,
+    model-00021-of-00030.safetensors,
+    model-00022-of-00030.safetensors,
+    model-00023-of-00030.safetensors,
+    model-00024-of-00030.safetensors,
+    model-00025-of-00030.safetensors,
+    model-00026-of-00030.safetensors,
+    model-00027-of-00030.safetensors,
+    model-00028-of-00030.safetensors,
+    model-00029-of-00030.safetensors,
+    model-00030-of-00030.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-3.3-70B-Instruct/
+  model_type: LLAMA3
+resume_from_checkpoint: False
+save_adapter_weights_only: True # Set to false to save the whole model + adapter merged
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Logging
+output_dir: /tmp/lora-llama3_3-finetune-output
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+# custom_sharded_layers: ['tok_embeddings', 'output']  # Layers to shard separately (useful for large vocab size models). Lower Memory, but lower speed.
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
diff --git a/recipes/configs/llama3_3/70B_qlora.yaml b/recipes/configs/llama3_3/70B_qlora.yaml
new file mode 100644
index 0000000000..e25b196927
--- /dev/null
+++ b/recipes/configs/llama3_3/70B_qlora.yaml
@@ -0,0 +1,132 @@
+# Config for multi-device LoRA in lora_finetune_distributed.py
+# using a Llama3.3 70B model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-3.3-70B-Instruct --ignore-patterns "original/consolidated*"
+#
+# This config needs 8 GPUs to run
+#   tune run --nproc_per_node 8 lora_finetune_distributed --config llama3_3/70B_lora
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_3.qlora_llama3_3_70b
+  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: False
+  lora_rank: 16  # higher increases accuracy and memory
+  lora_alpha: 32  # usually alpha=2*rank
+  lora_dropout: 0.0
+
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Llama-3.3-70B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-3.3-70B-Instruct/
+  checkpoint_files: [
+    model-00001-of-00030.safetensors,
+    model-00002-of-00030.safetensors,
+    model-00003-of-00030.safetensors,
+    model-00004-of-00030.safetensors,
+    model-00005-of-00030.safetensors,
+    model-00006-of-00030.safetensors,
+    model-00007-of-00030.safetensors,
+    model-00008-of-00030.safetensors,
+    model-00009-of-00030.safetensors,
+    model-00010-of-00030.safetensors,
+    model-00011-of-00030.safetensors,
+    model-00012-of-00030.safetensors,
+    model-00013-of-00030.safetensors,
+    model-00014-of-00030.safetensors,
+    model-00015-of-00030.safetensors,
+    model-00016-of-00030.safetensors,
+    model-00017-of-00030.safetensors,
+    model-00018-of-00030.safetensors,
+    model-00019-of-00030.safetensors,
+    model-00020-of-00030.safetensors,
+    model-00021-of-00030.safetensors,
+    model-00022-of-00030.safetensors,
+    model-00023-of-00030.safetensors,
+    model-00024-of-00030.safetensors,
+    model-00025-of-00030.safetensors,
+    model-00026-of-00030.safetensors,
+    model-00027-of-00030.safetensors,
+    model-00028-of-00030.safetensors,
+    model-00029-of-00030.safetensors,
+    model-00030-of-00030.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-3.3-70B-Instruct/
+  model_type: LLAMA3
+resume_from_checkpoint: False
+save_adapter_weights_only: True # Set to false to save the whole model + adapter merged
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 1  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Logging
+output_dir: /tmp/lora-llama3_3-finetune-output
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: True  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+# custom_sharded_layers: ['tok_embeddings', 'output']  # Layers to shard separately (useful for large vocab size models). Lower Memory, but lower speed.
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
diff --git a/torchtune/_recipe_registry.py b/torchtune/_recipe_registry.py
index 2a4bf25a8b..efa0175d23 100644
--- a/torchtune/_recipe_registry.py
+++ b/torchtune/_recipe_registry.py
@@ -106,6 +106,7 @@ class Recipe:
             Config(name="llama3_2/3B_full", file_path="llama3_2/3B_full.yaml"),
             Config(name="llama3/70B_full", file_path="llama3/70B_full.yaml"),
             Config(name="llama3_1/70B_full", file_path="llama3_1/70B_full.yaml"),
+            Config(name="llama3_3/70B_full", file_path="llama3_3/70B_full.yaml"),
             Config(name="mistral/7B_full", file_path="mistral/7B_full.yaml"),
             Config(name="gemma/2B_full", file_path="gemma/2B_full.yaml"),
             Config(name="gemma/7B_full", file_path="gemma/7B_full.yaml"),
@@ -353,6 +354,8 @@ class Recipe:
             Config(name="llama3/8B_dora", file_path="llama3/8B_dora.yaml"),
             Config(name="llama3/70B_lora", file_path="llama3/70B_lora.yaml"),
             Config(name="llama3_1/70B_lora", file_path="llama3_1/70B_lora.yaml"),
+            Config(name="llama3_3/70B_lora", file_path="llama3_3/70B_lora.yaml"),
+            Config(name="llama3_3/70B_qlora", file_path="llama3_3/70B_qlora.yaml"),
             Config(name="llama3/8B_lora", file_path="llama3/8B_lora.yaml"),
             Config(name="llama3_1/8B_lora", file_path="llama3_1/8B_lora.yaml"),
             Config(name="llama3_2/1B_lora", file_path="llama3_2/1B_lora.yaml"),
diff --git a/torchtune/models/llama3_3/__init__.py b/torchtune/models/llama3_3/__init__.py
new file mode 100644
index 0000000000..cd5ac4d306
--- /dev/null
+++ b/torchtune/models/llama3_3/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from ._model_builders import llama3_3_70b, lora_llama3_3_70b, qlora_llama3_3_70b  # noqa
+
+__all__ = [
+    "llama3_3_70b",
+    "lora_llama3_3_70b",
+    "qlora_llama3_3_70b",
+]
diff --git a/torchtune/models/llama3_3/_model_builders.py b/torchtune/models/llama3_3/_model_builders.py
new file mode 100644
index 0000000000..a55973e136
--- /dev/null
+++ b/torchtune/models/llama3_3/_model_builders.py
@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from torchtune.models.llama3_1._model_builders import (
+    llama3_1_70b,
+    lora_llama3_1_70b,
+    qlora_llama3_1_70b,
+)
+
+"""
+Model builders build specific instantiations using component builders. The Llama3.3 model
+builders all call the Llama3.1 models as they're identical models apart from the checkpoints.
+"""
+
+llama3_3_70b = llama3_1_70b
+
+llama3_3_70b.__doc__ = """
+Builder for creating a Llama3.3 model initialized w/ the default 70B parameter values.
+Please see `llama3_1_70b` for full API arguments.
+"""
+
+lora_llama3_3_70b = lora_llama3_1_70b
+
+lora_llama3_3_70b.__doc__ = """
+Builder for creating a Llama3.3 70B model with LoRA enabled.
+Please see `lora_llama3_1_70b` for full API arguments.
+"""
+
+qlora_llama3_3_70b = qlora_llama3_1_70b
+
+qlora_llama3_1_70b.__doc__ = """
+Builder for creating a Llama3.3 70B model with QLoRA enabled. Base model weights in linear layers
+that LoRA is applied to are quantized per the QLoRA paper: https://arxiv.org/abs/2305.14314.
+Please see `lora_llama3_1_70b` for full API arguments.
+"""