diff --git a/docs/assets/fp8_curves.png b/docs/assets/fp8_curves.png
new file mode 100644
index 0000000000..1825877a9e
Binary files /dev/null and b/docs/assets/fp8_curves.png differ
diff --git a/docs/fp8.md b/docs/fp8.md
new file mode 100644
index 0000000000..8d3fe2b82d
--- /dev/null
+++ b/docs/fp8.md
@@ -0,0 +1,39 @@
+# FP8 for NeMo-RL
+
+This module provides a suite of tools to enable FP8 quantization for large language models. This module is still in developement. Currently we support FP8 generation, using Deepseek style FP8 (sub channel scaling).
+
+NeMo-RL monkey patches several vLLM functions to enable FP8 generations for reinforcement learning. The `init_fp8` function patches key `vLLM` components when initialized:
+1.  **`RayDistributedExecutor`**: For multi-GPU inference, the executor is patched to ensure that every worker process applies the same FP8 patches before model initialization.
+2.  **Quantization Utilities**: Functions within `vllm.model_executor.layers.quantization` are replaced with versions that support power-of-2 scaling and other custom features.
+3.  **Weight Loading**: A custom `load_weights` function handles the on-the-fly quantization of model weights from a higher-precision format to FP8 with the correct scaling factors.
+
+---
+
+## Usage
+
+FP8 generations are recommended to be configured with the following settings:
+
+   ```
+    loss_fn:
+        # importance sampling helps improve stability
+        use_importance_sampling_correction: true
+
+    policy:
+        generation:
+            vllm_cfg:
+                precision: 'fp8'
+                # DeepGemm is much more performant than vLLM's default cutlass fp8 subchannel scaling kernels
+                use_deep_gemm: true
+                # Keeping the first and last three layers in bf16 reduces the multi-token error without
+                # a signficant effect to performance
+                num_last_layers_in_bf16: 3
+                num_first_layers_in_bf16: 1
+                # Use FP32 scaling factors. Rounding scaling factors to the nearest pow2 may improve quantization 
+                # fidelity however this feature is still under research.
+                use_weight_pow2_scale: False
+                use_activation_pow2_scale: False
+```
+
+## Accuracy
+
+We observe on the Llama 8b recipe a ~5% accuracy loss is incurred with FP8 generations. Convergence is still under active research and FP8 generations should be used with caution. We are investigating ways to close the accuracy gap and further improve performance. 
diff --git a/docs/index.md b/docs/index.md
index e39423a23c..75c85cbfde 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -49,6 +49,7 @@ testing.md
 documentation.md
 debugging.md
 nsys-profiling.md
+fp8.md
 guides/use-custom-vllm.md
 apidocs/index.rst
 ```
diff --git a/examples/configs/grpo_math_1B.yaml b/examples/configs/grpo_math_1B.yaml
index b797afee17..b7906c934c 100644
--- a/examples/configs/grpo_math_1B.yaml
+++ b/examples/configs/grpo_math_1B.yaml
@@ -174,6 +174,9 @@ policy:
       gpu_memory_utilization: 0.6
       max_model_len: ${policy.max_total_sequence_length}
       enforce_eager: False
+      use_deep_gemm: False
+      num_last_layers_in_bf16: 0
+      num_first_layers_in_bf16: 0
     colocated:
       # true: generation shares training GPUs
       # false: uses dedicated generation resources
diff --git a/examples/configs/grpo_math_8B_megatron_fp8.yaml b/examples/configs/grpo_math_8B_megatron_fp8.yaml
new file mode 100644
index 0000000000..c171bba100
--- /dev/null
+++ b/examples/configs/grpo_math_8B_megatron_fp8.yaml
@@ -0,0 +1,13 @@
+# GRPO Algorithm Configuration
+defaults: "grpo_math_8B_megatron.yaml"
+
+loss_fn:
+  use_importance_sampling_correction: true
+
+policy:
+  generation:
+    vllm_cfg:
+      precision: 'fp8'
+      use_deep_gemm: true
+      num_last_layers_in_bf16: 0
+      num_first_layers_in_bf16: 0
diff --git a/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.yaml b/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.yaml
new file mode 100644
index 0000000000..9a62f9008a
--- /dev/null
+++ b/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.yaml
@@ -0,0 +1,161 @@
+grpo:
+  num_prompts_per_step: 64
+  num_generations_per_prompt: 32
+  max_rollout_turns: 1
+  max_num_steps: 500
+  normalize_rewards: true
+  use_leave_one_out_baseline: true
+  val_period: 10
+  val_at_start: false
+  max_val_samples: 256
+  val_batch_size: 256
+  seed: 42
+loss_fn:
+  reference_policy_kl_penalty: 0.01
+  ratio_clip_min: 0.2
+  ratio_clip_max: 0.2
+  ratio_clip_c: null
+  use_on_policy_kl_approximation: false
+  use_importance_sampling_correction: True
+  token_level_loss: true
+checkpointing:
+  enabled: true
+  checkpoint_dir: results/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8
+  metric_name: val_reward
+  higher_is_better: true
+  keep_top_k: 3
+  save_period: 10
+  checkpoint_must_save_by: null
+policy:
+  model_name: meta-llama/Llama-3.1-8B-Instruct
+  tokenizer:
+    name: meta-llama/Llama-3.1-8B-Instruct
+  train_global_batch_size: 512
+  train_micro_batch_size: 1
+  generation_batch_size: 32
+  logprob_batch_size: 2
+  max_total_sequence_length: 4096
+  precision: bfloat16
+  make_sequence_length_divisible_by: 1
+  max_grad_norm: 1
+
+  dtensor_cfg:
+    enabled: False
+
+  dynamic_batching:
+    enabled: False
+
+  sequence_packing:
+    enabled: True
+    train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}}
+    logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}}
+    algorithm: "modified_first_fit_decreasing"
+    sequence_length_round: 64
+
+  megatron_cfg:
+    enabled: True
+    empty_unused_memory_level: 1
+    converter_type: "LlamaForCausalLM"
+    tensor_model_parallel_size: 1
+    pipeline_model_parallel_size: 2
+    context_parallel_size: 1
+    expert_tensor_parallel_size: 1
+    expert_model_parallel_size: 1
+    sequence_parallel: False
+    pipeline_dtype: ${policy.precision}
+    num_layers_in_first_pipeline_stage: null
+    num_layers_in_last_pipeline_stage: null
+    freeze_moe_router: True
+    moe_router_dtype: "fp64"
+    moe_router_load_balancing_type: "none" # "seq_aux_loss" causes logprob error divergence for grpo
+    moe_router_bias_update_rate: 0.0 # by default, disable bias updates for grpo
+    apply_rope_fusion: True
+    activation_checkpointing: True
+    defer_fp32_logits: True
+
+    optimizer:
+      optimizer: "adam"
+      lr: 5.0e-7
+      min_lr: 5.0e-8
+      weight_decay: 0.0
+      bf16: True
+      fp16: False
+      params_dtype: "float32"
+
+      adam_beta1: 0.9
+      adam_beta2: 0.999
+      adam_eps: 1e-8
+
+      use_distributed_optimizer: True
+      use_precision_aware_optimizer: True
+
+      clip_grad: ${policy.max_grad_norm}
+
+    scheduler:
+      start_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay}
+      end_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay}
+      weight_decay_incr_style: "constant"
+      lr_decay_style: "constant"
+      lr_decay_iters: null
+      lr_warmup_iters: 2
+      lr_warmup_init: 5.0e-8
+
+    distributed_data_parallel_config:
+      grad_reduce_in_fp32: False
+      overlap_grad_reduce: True
+      overlap_param_gather: True
+      average_in_collective: True
+      use_custom_fsdp: False
+      data_parallel_sharding_strategy: "optim_grads_params"
+
+  generation:
+    backend: vllm
+    max_new_tokens: 4096
+    temperature: 1
+    top_p: 1
+    top_k: null
+    stop_token_ids:
+      - 128009
+    stop_strings: null
+    vllm_cfg:
+      async_engine: false
+      precision: 'fp8'
+      tensor_parallel_size: 1
+      pipeline_parallel_size: 1
+      gpu_memory_utilization: 0.6
+      max_model_len: 4096
+      enforce_eager: False
+      use_deep_gemm: true
+      num_last_layers_in_bf16: 0
+      num_first_layers_in_bf16: 0
+    colocated:
+      enabled: true
+      resources:
+        gpus_per_node: null
+        num_nodes: null
+data:
+  max_input_seq_length: 4096
+  prompt_file: examples/prompts/cot.txt
+  system_prompt_file: null
+  dataset_name: OpenMathInstruct-2
+  shuffle: true
+env:
+  math:
+    num_workers: 8
+logger:
+  log_dir: logs/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8
+  num_val_samples_to_print: 0
+  wandb_enabled: true
+  tensorboard_enabled: true
+  mlflow_enabled: false
+  monitor_gpus: true
+  wandb:
+    project: nemo-rl
+    name: grpo-llama3.1-8b-instruct-1n8g-megatron-fp8
+  tensorboard: {}
+  gpu_monitoring:
+    collection_interval: 10
+    flush_interval: 10
+cluster:
+  gpus_per_node: 8
+  num_nodes: 4
diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py
index 637a5f8faf..90454bbab9 100644
--- a/nemo_rl/algorithms/grpo.py
+++ b/nemo_rl/algorithms/grpo.py
@@ -319,6 +319,11 @@ def setup(
         )
     elif backend == "vllm":
         generation_config = cast(VllmConfig, generation_config)
+        if generation_config["vllm_cfg"]["precision"] == "fp8":
+            assert loss_config["use_importance_sampling_correction"] is True, (
+                "Importance sampling must be enabled for vLLM FP8 generation for good convergence!"
+            )
+
         policy_generation = VllmGeneration(
             cluster=inference_cluster, config=generation_config
         )
diff --git a/nemo_rl/models/generation/fp8.py b/nemo_rl/models/generation/fp8.py
new file mode 100644
index 0000000000..cb983617d6
--- /dev/null
+++ b/nemo_rl/models/generation/fp8.py
@@ -0,0 +1,574 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from dataclasses import dataclass, field
+from unittest.mock import patch
+
+import ray
+import torch
+from accelerate import init_empty_weights
+from transformers import AutoConfig, AutoModel
+from vllm.model_executor.layers.linear import LinearBase
+from vllm.triton_utils import tl, triton
+from vllm.v1.engine.core import EngineCoreProc
+from vllm.v1.engine.utils import CoreEngineProcManager
+
+FP8_BLOCK_QUANT_KWARGS = {
+    "activation_scheme": "dynamic",
+    "fmt": "e4m3",
+    "quant_method": "fp8",
+    "weight_block_size": [128, 128],
+}
+
+
+@dataclass(frozen=True)
+class FP8Config:
+    use_weight_pow2_scale: bool = False
+    use_activation_pow2_scale: bool = False
+    num_first_layers_in_bf16: int = 0
+    num_last_layers_in_bf16: int = 0
+    model_parallel_size: int = None
+
+
+@dataclass()
+class FP8State:
+    # A cache of fp8 parameter names, we can check this cache to see if a
+    # param name corresponds to a fp8 weight
+    seen_params: set = field(default_factory=lambda: set())
+    fp8_param_names: set = field(default_factory=lambda: set())
+    vllm_patches: list = field(default_factory=lambda: [])
+
+
+# Global FP8 config that can be accessed by patched vLLM functions
+# initialized by 'init_fp8_cfg()'
+global_fp8_config: FP8Config = None
+# Global FP8 state that holds runtime fp8 objects
+fp8_state: FP8State = FP8State()
+
+fp8_patches_applied = False
+
+original_run_engine_core = EngineCoreProc.run_engine_core
+original_init = CoreEngineProcManager.__init__
+
+
+def my_init(*args, **kwargs):
+    kwargs["vllm_config"].nrl_fp8_cfg = global_fp8_config
+    return original_init(*args, **kwargs)
+
+
+def my_run_engine_core(*args, **kwargs):
+    fp8_cfg = kwargs["vllm_config"].nrl_fp8_cfg
+    del kwargs["vllm_config"].nrl_fp8_cfg
+    monkey_patch_vllm_ray_executor(fp8_cfg)
+    return original_run_engine_core(*args, **kwargs)
+
+
+def monkey_patch_vllm_ray_executor(fp8_config):
+    if fp8_config.model_parallel_size > 1:
+        # we patch vllm's _run_workers so that before vllm initalizes the model on each rank, we execute
+        # a ray remote that patches each worker with the required fp8 vllm patches
+        from vllm.v1.executor.ray_distributed_executor import RayDistributedExecutor
+
+        original_run_workers = RayDistributedExecutor._run_workers
+
+        def patched_run_workers(self, *args, **kwargs):
+            global fp8_patches_applied
+            if not fp8_patches_applied:
+                futures = [
+                    worker.execute_method.remote(apply_fp8_patches, fp8_config)
+                    for worker in self.workers
+                ]
+                [ray.get(future) for future in futures]
+                fp8_patches_applied = True
+
+            return original_run_workers(self, *args, **kwargs)
+
+        RayDistributedExecutor._run_workers = patched_run_workers
+    else:
+        # for single gpu there is no ray, so just call the patches
+        apply_fp8_patches(None, fp8_config)
+
+        global fp8_patches_applied
+        fp8_patches_applied = True
+
+
+def apply_fp8_patches(self, fp8_config):
+    global global_fp8_config, fp8_patches_applied
+    assert not fp8_patches_applied
+
+    global_fp8_config = fp8_config
+
+    # This patch is used to support torch.compile with vllm parameter subclasses, such as
+    # PerTensorScaleParameter. Because we need weight loaders to update fp8 weights each
+    # refit, we patch fp8 parameters to have a reference to their weight loader. Eventually
+    # with pytorch 2.8, parameter subclassing with torch.compile will be natively supported, in
+    # which this patch can be removed.
+    func1_path = "vllm.model_executor.layers.quantization.fp8.Fp8LinearMethod.process_weights_after_loading"
+    patcher1 = patch(func1_path, process_weights_after_loading)
+    fp8_state.vllm_patches.append(patcher1)
+    # These patches add support for pow2, e8 dynamic activation scalings factors which are believed to have higher
+    # SNR compared to plain fp32 scaling factors. This feature is still under active research.
+    if global_fp8_config.use_activation_pow2_scale:
+        func2_path = "vllm.model_executor.layers.quantization.utils.fp8_utils.per_token_group_quant_fp8"
+        func3_path = "vllm.model_executor.layers.quantization.utils.fp8_utils._per_token_group_quant_fp8"
+        func4_path = "vllm.model_executor.layers.quantization.utils.fp8_utils._per_token_group_quant_fp8_colmajor"
+        patcher2 = patch(func2_path, per_token_group_quant_fp8)
+        patcher3 = patch(func3_path, _per_token_group_quant_fp8)
+        patcher4 = patch(func4_path, _per_token_group_quant_fp8_colmajor)
+        fp8_state.vllm_patches.append(patcher2, patcher3, patcher4)
+
+    for p in fp8_state.vllm_patches:
+        p.start()
+
+    fp8_patches_applied = True
+
+
+def init_fp8(vllm_cfg, model_name, model_parallel_size):
+    config = AutoConfig.from_pretrained(model_name)
+    if hasattr(config, "num_experts"):
+        assert config.num_experts == 0, (
+            "FP8 generation for MoE models is currently not supported"
+        )
+
+    global global_fp8_config
+    global_fp8_config = FP8Config(
+        use_weight_pow2_scale=vllm_cfg.get("pow2_weight_scaling_factors", False),
+        use_activation_pow2_scale=vllm_cfg.get(
+            "pow2_activation_scaling_factors", False
+        ),
+        num_first_layers_in_bf16=vllm_cfg.get("num_first_layers_in_bf16", 0),
+        num_last_layers_in_bf16=vllm_cfg.get("num_last_layers_in_bf16", 0),
+        model_parallel_size=model_parallel_size,
+    )
+
+    if vllm_cfg.get("use_deep_gemm", False):
+        os.environ["VLLM_USE_DEEP_GEMM"] = "1"
+
+    if vllm_cfg["async_engine"]:
+        # for async engine, vllm spawns a process for each DP, so we patch
+        # vllm so that upon spawning the thread it applies our FP8 patches
+        EngineCoreProc.run_engine_core = my_run_engine_core
+        CoreEngineProcManager.__init__ = my_init
+    else:
+        # if not async, just directly monkey patch the ray executor
+        monkey_patch_vllm_ray_executor(global_fp8_config)
+
+    # create fp8 kwargs for vllm's LLM(...)
+    num_first_layers_in_bf16 = vllm_cfg.get("num_first_layers_in_bf16", 0)
+    num_last_layers_in_bf16 = vllm_cfg.get("num_last_layers_in_bf16", 0)
+    fp8_block_quant_kwargs = dict(FP8_BLOCK_QUANT_KWARGS)
+
+    if num_first_layers_in_bf16 > 0 or num_last_layers_in_bf16 > 0:
+        with init_empty_weights():
+            model = AutoModel.from_config(config)
+        param_names = [name for name, _ in model.named_parameters()]
+
+        bf16_params = []
+        if num_first_layers_in_bf16 > 0:
+            layers = [l for l in range(num_first_layers_in_bf16)]
+            bf16_params.append(_get_params_in_layers(param_names, layers))
+
+        if num_last_layers_in_bf16 > 0:
+            layers = [
+                l
+                for l in range(
+                    config.num_hidden_layers - num_last_layers_in_bf16,
+                    config.num_hidden_layers,
+                )
+            ]
+            bf16_params.append(_get_params_in_layers(param_names, layers))
+
+        fp8_block_quant_kwargs["ignored_layers"] = bf16_params
+
+    vllm_kwargs = {
+        "quantization": "fp8",
+        "hf_overrides": {"quantization_config": fp8_block_quant_kwargs},
+    }
+    return vllm_kwargs
+
+
+def is_fp8_model(vllm_config):
+    from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+
+    if hasattr(vllm_config, "quant_config") and isinstance(
+        vllm_config.quant_config, Fp8Config
+    ):
+        assert vllm_config.quant_config.weight_block_size is not None, (
+            "Only block scaling is currently supported in NeMo-RL!"
+        )
+        return True
+
+    return False
+
+
+def _get_params_in_layers(param_names, layers):
+    layer_templates = []
+    for i in layers:
+        # Prefixes used by huggingface model transformer layers.
+        # We'll use these to match against the parameter names to determine
+        # which layer the parameter is in.
+        layer_templates.extend(
+            [
+                f"transformer.h.{i}.",
+                f"layers.{i}.",
+                f"layer.{i}.",
+            ]
+        )
+    prefixes = [p for p in layer_templates if any(p in n for n in param_names)]
+    if len(prefixes) == 0:
+        raise ValueError(f"Could not identify layers {layers} for model.")
+
+    params = []
+    for name in param_names:
+        if (
+            any(p in name for p in prefixes)
+            and "bias" not in name
+            and "layernorm" not in name
+        ):
+            # Convert the param name into vllm's module name
+            # Vllm wraps the model with an extra 'model'
+            params.append(f"model.{name}".removesuffix(".weight"))
+    return params
+
+
+def _get_module_from_param_name(model, name: str):
+    # Split the name into parts (e.g., 'layers', '0', 'self_attn', 'q_proj', 'weight')
+    # The module path is all but the last part (the parameter's own name)
+    path_parts = name.split(".")
+    module_path = path_parts[:-1]
+    # Replace with the fused model name
+    packed_modules_mapping = model.packed_modules_mapping
+    reversed_mapping = {
+        original_name: fused_name
+        for fused_name, original_names_list in packed_modules_mapping.items()
+        for original_name in original_names_list
+    }
+    if module_path[-1] in reversed_mapping.keys():
+        module_path[-1] = reversed_mapping[module_path[-1]]
+
+    current_module = model
+    try:
+        # Traverse the model hierarchy
+        for part in module_path:
+            if isinstance(current_module, torch.nn.ModuleList):
+                current_module = current_module[int(part)]
+            else:
+                current_module = getattr(current_module, part)
+    except (AttributeError, IndexError, ValueError) as e:
+        print(f"Warning: Could not find module for parameter '{name}'. Error: {e}")
+    return current_module
+
+
+def _is_fp8_weight(name, model):
+    if name not in fp8_state.seen_params:
+        fp8_state.seen_params.add(name)
+        # Filter out bias params
+        if name.endswith("weight"):
+            module = _get_module_from_param_name(model, name)
+            # We currently only quantize linear layers
+            if (
+                isinstance(module, LinearBase)
+                and module.weight.dtype == torch.float8_e4m3fn
+            ):
+                fp8_state.fp8_param_names.add(name)
+    return name in fp8_state.fp8_param_names
+
+
+def load_weights(weights, model_runner):
+    weights_quantized = []
+    model = model_runner.model
+
+    for k, v in weights:
+        if not _is_fp8_weight(k, model):
+            weights_quantized.append((k, v))
+            continue
+        # Cast the weight into fp8 and its scale factor
+        param_lp, param_scale = cast_tensor_to_fp8_blockwise(
+            v.to(torch.float),
+            weight_block_size=FP8_BLOCK_QUANT_KWARGS["weight_block_size"],
+        )
+        param_scale = torch.squeeze(param_scale, dim=-1)
+        weights_quantized.append([k, param_lp])
+        weights_quantized.append([k + "_scale_inv", param_scale])
+    # Monkey patch the param class to their subclass, as certain models
+    # will check the param type to call the proper weightloader
+    for name, param in model.named_parameters():
+        if hasattr(param, "subclass_type"):
+            param.orig_type = param.__class__
+            param.__class__ = param.subclass_type
+    # Finally load the weights into vllm
+    model.load_weights(weights_quantized)
+    # Undo the type change above to the original type
+    for name, param in model.named_parameters():
+        if hasattr(param, "subclass_type"):
+            param.__class__ = param.orig_type
+
+
+def cast_tensor_to_fp8_blockwise(
+    data_hp,
+    weight_block_size,
+):
+    assert len(data_hp.shape) == 2, "Only 2d input tensor is supported"
+
+    block_size1 = weight_block_size[1]
+    block_size0 = weight_block_size[0]
+    assert data_hp.shape[1] % block_size1 == 0, (
+        f"data_hp.shape[1] {data_hp.shape[1]}  must be a multiple of block_size1: {block_size1}."
+    )
+    assert data_hp.shape[0] % block_size0 == 0, (
+        f"data_hp.shape[0] {data_hp.shape[0]} must be a multiple of block_size0: {block_size0}."
+    )
+
+    # FP8
+    max_dtype = torch.finfo(torch.float8_e4m3fn).max
+
+    original_shape = data_hp.shape
+    blk_m, blk_n = data_hp.shape[0] // block_size0, data_hp.shape[1] // block_size1
+
+    assert block_size1 == block_size0
+    data_hp = data_hp.reshape(blk_m, block_size0, blk_n, block_size1)
+
+    # Permute to (BLK_M, BLK_N, BLOCK_SIZE_M, BLOCK_SIZE_N)
+    data_hp = data_hp.permute(0, 2, 1, 3)
+    # Flatten to (BLK_M, BLK_N, BLOCK_SIZE_M * BLOCK_SIZE_N)
+    data_hp = data_hp.to(torch.float32).contiguous().flatten(start_dim=2)
+
+    # Calculate max absolute value per block
+    max_abs = torch.amax(torch.abs(data_hp), dim=-1, keepdim=True)
+    # Calculate descale factor
+    descale = max_abs / max_dtype
+
+    global global_fp8_config
+    if global_fp8_config.use_weight_pow2_scale:
+        exponent = torch.ceil(torch.log2(descale))
+        # Post process exponent to be in range of -127 to 127 and to be E8M0 biased
+        exponent = torch.clamp(exponent, min=-127, max=127) + 127
+        # Convert to uint8 container
+        exponent = exponent.to(torch.uint8)
+        # Calculate descale_fp to apply to data_hp
+        scale_fp = torch.where(
+            # If exponent is 0, descale_fp is 1.0 rather than 2^127
+            exponent == 0,
+            1.0,
+            torch.exp2(127 - exponent.to(torch.float32)),
+        )
+        descale_fp = torch.reciprocal(scale_fp)
+    else:
+        scale_fp = max_dtype / max_abs
+        scale_fp = torch.where(max_abs == 0, 1.0, scale_fp)
+        # preserve the behavior for 0 amax case
+        scale_fp = torch.where(max_abs == torch.inf, 1.0, scale_fp)
+
+        descale_fp = torch.reciprocal(scale_fp)
+
+    # Scale and saturate cast the data elements to max of target dtype
+    data_lp = torch.clamp(data_hp * scale_fp, min=-1 * max_dtype, max=max_dtype)
+
+    fp_data = data_lp.to(torch.float8_e4m3fn)
+
+    # (BLK_M, BLK_N, BLOCK_SIZE_M * BLOCK_SIZE_N) to (M, N)
+    fp_data = (
+        fp_data.reshape(blk_m, blk_n, block_size0, block_size1)
+        .permute(0, 2, 1, 3)
+        .reshape(original_shape)
+    )
+
+    # Convert to target format, but still in original precision container
+    return fp_data, descale_fp
+
+
+def process_weights_after_loading(self, layer) -> None:
+    from torch.nn import Parameter
+    from vllm.model_executor.parameter import (
+        BlockQuantScaleParameter,
+        ModelWeightParameter,
+    )
+
+    assert self.block_quant and self.quant_config.is_checkpoint_fp8_serialized
+    assert self.quant_config.activation_scheme == "dynamic"
+
+    def _create_param_from_subclass_attributes(custom_param):
+        param = Parameter(custom_param.data, requires_grad=False)
+        base_param_dir = dir(torch.nn.Parameter)
+        custom_param_dir = dir(custom_param)
+        # Find the attributes that are unique to the custom parameter
+        custom_attributes = [
+            attr
+            for attr in custom_param_dir
+            if attr not in base_param_dir and not attr.startswith("__")
+        ]
+        # Set the custom attributes into the base parameter object
+        for attr in custom_attributes:
+            setattr(param, attr, getattr(custom_param, attr))
+
+        param.subclass_type = type(custom_param)
+        return param
+
+    weight = layer.weight.data
+    weight_scale_inv = layer.weight_scale_inv.data
+    weight = self._maybe_pad_weight(weight)
+
+    layer.weight = _create_param_from_subclass_attributes(
+        ModelWeightParameter(
+            data=weight,
+            output_dim=0,
+            input_dim=1,
+            weight_loader=layer.weight.weight_loader,
+        )
+    )
+    layer.weight_scale_inv = _create_param_from_subclass_attributes(
+        BlockQuantScaleParameter(
+            data=weight_scale_inv,
+            output_dim=0,
+            input_dim=1,
+            weight_loader=layer.weight_scale_inv.weight_loader,
+        )
+    )
+
+
+@triton.jit
+def _per_token_group_quant_fp8(
+    # Pointers to inputs and output
+    y_ptr,
+    y_q_ptr,
+    y_s_ptr,
+    group_size,
+    # Num columns of y
+    y_num_columns,
+    y_row_stride,
+    # Avoid to divide zero
+    eps,
+    # Information for float8
+    fp8_min,
+    fp8_max,
+    # Meta-parameters
+    BLOCK: tl.constexpr,
+):
+    groups_per_row = y_num_columns // group_size
+
+    # Map the program id to the row of X and Y it should compute.
+    g_id = tl.program_id(0)
+    row = g_id // groups_per_row
+    row_g_id = g_id % groups_per_row
+
+    y_ptr += (row * y_row_stride) + (row_g_id * group_size)
+    y_q_ptr += g_id * group_size
+    y_s_ptr += g_id
+
+    cols = tl.arange(0, BLOCK)  # N <= BLOCK
+    mask = cols < group_size
+
+    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+    # Quant
+    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
+
+    # pow2_scale
+    inv_scale = fp8_max / _absmax
+    exponent = tl.floor(tl.log2(inv_scale))
+    # exponent is an integer
+    exponent = tl.minimum(exponent, 126.0)
+
+    # after rounding to exponent, round back to floating
+    inv_scale_pow2 = tl.exp2(exponent)
+
+    is_nan = inv_scale_pow2 != inv_scale_pow2
+    is_inf = (inv_scale_pow2 == 1.0 / 0.0) | (inv_scale_pow2 == -1.0 / 0.0)
+
+    # If the value is NaN or infinity, default it to 1.0,
+    # otherwise keep its original value.
+    inv_scale_pow2 = tl.where(is_nan | is_inf, 1.0, inv_scale_pow2)
+    # finally uninverse
+    y_s = 1.0 / inv_scale_pow2
+
+    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
+
+    tl.store(y_q_ptr + cols, y_q, mask=mask)
+    tl.store(y_s_ptr, y_s)
+
+
+@triton.jit
+def _per_token_group_quant_fp8_colmajor(
+    # Pointers to inputs and output
+    y_ptr,
+    y_q_ptr,
+    y_s_ptr,
+    group_size,
+    # Num columns of y
+    y_num_columns,
+    y_row_stride,
+    # Stride from one column to the next of y_s
+    y_s_col_stride,
+    # Avoid to divide zero
+    eps,
+    # Information for float8
+    fp8_min,
+    fp8_max,
+    # Meta-parameters
+    BLOCK: tl.constexpr,
+):
+    groups_per_row = y_num_columns // group_size
+
+    # Map the program id to the row of X and Y it should compute.
+    g_id = tl.program_id(0)
+    row = g_id // groups_per_row
+    row_g_id = g_id % groups_per_row
+
+    y_ptr += (row * y_row_stride) + (row_g_id * group_size)
+    y_q_ptr += g_id * group_size
+
+    # Convert g_id the flattened block coordinate to 2D so we can index
+    # into the output y_scales matrix
+    blocks_per_row = y_num_columns // group_size
+    scale_col = g_id % blocks_per_row
+    scale_row = g_id // blocks_per_row
+    y_s_ptr += scale_col * y_s_col_stride + scale_row
+
+    cols = tl.arange(0, BLOCK)  # group_size <= BLOCK
+    mask = cols < group_size
+
+    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
+
+    # Quant pow2_scale:
+    inv_scale = fp8_max / _absmax
+    # calculate the nearest pow2 integer
+    exponent = tl.floor(tl.log2(inv_scale))
+    exponent = tl.minimum(exponent, 126.0)
+    # round inv_scale to the nearest pow2 with the exp we just calculated
+    inv_scale_pow2 = tl.exp2(exponent)
+    # If the value is NaN or infinity, default it to 1.0,
+    # otherwise keep its original value.
+    is_nan = inv_scale_pow2 != inv_scale_pow2
+    is_inf = (inv_scale_pow2 == float("inf")) | (inv_scale_pow2 == float("-inf"))
+    inv_scale_pow2 = tl.where(is_nan | is_inf, 1.0, inv_scale_pow2)
+    # finally uninverse
+    y_s = 1.0 / inv_scale_pow2
+
+    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
+
+    tl.store(y_q_ptr + cols, y_q, mask=mask)
+    tl.store(y_s_ptr, y_s)
+
+
+def per_token_group_quant_fp8(
+    *args,
+    **kwargs,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    assert global_fp8_config.use_activation_pow2_scale
+    from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+        per_token_group_quant_fp8 as vllm_per_token_group_quant_fp8,
+    )
+
+    return vllm_per_token_group_quant_fp8(*args, **kwargs)
diff --git a/nemo_rl/models/generation/vllm/vllm_backend.py b/nemo_rl/models/generation/vllm/vllm_backend.py
index 92c7916c43..5c3b125514 100644
--- a/nemo_rl/models/generation/vllm/vllm_backend.py
+++ b/nemo_rl/models/generation/vllm/vllm_backend.py
@@ -155,7 +155,14 @@ def update_weights_from_local_ipc_handles(self, local_device_ipc_handles):
                     weights.append((name, tensor))
 
             # Load weights into the model
-            self.model_runner.model.load_weights(weights=weights)
+            from nemo_rl.models.generation import fp8
+
+            if fp8.is_fp8_model(self.model_runner.vllm_config):
+                # the fp8 load_weights additionally casts bf16 weights into fp8
+                fp8.load_weights(weights, self.model_runner)
+            else:
+                self.model_runner.model.load_weights(weights=weights)
+
             return True
         except Exception as e:
             print(
@@ -177,7 +184,14 @@ def update_weights_from_collective(self) -> bool:
             for name, (shape, dtype) in self.state_dict_info.items():
                 weight = torch.empty(shape, dtype=dtype, device="cuda")
                 self.model_update_group.broadcast(weight, src=0)
-                self.model_runner.model.load_weights(weights=[(name, weight)])
+
+                from nemo_rl.models.generation import fp8
+
+                if fp8.is_fp8_model(self.model_runner.vllm_config):
+                    # the fp8 load_weights additionally casts bf16 weights into fp8
+                    fp8.load_weights([(name, weight)], self.model_runner)
+                else:
+                    self.model_runner.model.load_weights(weights=[(name, weight)])
         except Exception as e:
             print(
                 f"Error in VllmInternalWorkerExtension.update_weights_from_collective: {e}"
diff --git a/nemo_rl/models/generation/vllm/vllm_worker.py b/nemo_rl/models/generation/vllm/vllm_worker.py
index 25a85543f5..e6e225698b 100644
--- a/nemo_rl/models/generation/vllm/vllm_worker.py
+++ b/nemo_rl/models/generation/vllm/vllm_worker.py
@@ -135,6 +135,7 @@ def __init__(
         self.tensor_parallel_size = self.cfg["vllm_cfg"]["tensor_parallel_size"]
         self.pipeline_parallel_size = self.cfg["vllm_cfg"]["pipeline_parallel_size"]
         self.gpu_memory_utilization = self.cfg["vllm_cfg"]["gpu_memory_utilization"]
+        self.precision = self.cfg["vllm_cfg"]["precision"]
         self.fraction_of_gpus = fraction_of_gpus
         self.is_model_owner = bundle_indices is not None
 
@@ -295,6 +296,16 @@ def _patch_vllm_init_workers_ray():
             )
             vllm_kwargs["ray_workers_use_nsight"] = True
 
+        if self.cfg["vllm_cfg"]["precision"] == "fp8":
+            from nemo_rl.models.generation.fp8 import init_fp8
+
+            fp8_kwargs = init_fp8(
+                self.cfg["vllm_cfg"], self.model_name, model_parallel_size
+            )
+            vllm_kwargs.update(fp8_kwargs)
+            # overriden by quant config, however vllm complains if this not passed
+            self.precision = "bfloat16"
+
         llm_kwargs = dict(
             model=self.model_name,
             load_format=load_format,
@@ -305,7 +316,7 @@ def _patch_vllm_init_workers_ray():
             pipeline_parallel_size=self.pipeline_parallel_size,
             gpu_memory_utilization=self.gpu_memory_utilization,
             enable_prefix_caching=torch.cuda.get_device_capability()[0] >= 8,
-            dtype=self.cfg["vllm_cfg"]["precision"],
+            dtype=self.precision,
             seed=seed,
             enforce_eager=self.cfg["vllm_cfg"]["enforce_eager"],
             max_model_len=self.cfg["vllm_cfg"]["max_model_len"],
diff --git a/pyproject.toml b/pyproject.toml
index 2a0421b964..8780797e49 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,8 @@ automodel = [
     "causal-conv1d",
 ]
 vllm = [
+    "cuda-python",
+    "deep_gemm @ git+https://github.com/deepseek-ai/DeepGEMM.git@7b6b5563b9d4c1ae07ffbce7f78ad3ac9204827c",
     "vllm==0.10.0",
     "num2words>=0.5.14",
     # Remove this once https://github.com/NVIDIA-NeMo/RL/issues/501 resolved
diff --git a/tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.sh b/tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.sh
new file mode 100755
index 0000000000..e0b2f928da
--- /dev/null
+++ b/tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd)
+source $SCRIPT_DIR/common.env
+
+# ===== BEGIN CONFIG =====
+NUM_NODES=1
+STEPS_PER_RUN=100
+MAX_STEPS=100
+NUM_RUNS=$(( (MAX_STEPS + STEPS_PER_RUN - 1) / STEPS_PER_RUN ))  # Round up
+NUM_MINUTES=120
+# ===== END CONFIG =====
+
+exit_if_max_steps_reached
+
+# Run the experiment
+cd $PROJECT_ROOT
+uv run examples/run_grpo_math.py \
+    --config $CONFIG_PATH \
+    grpo.max_num_steps=$MAX_STEPS \
+    logger.log_dir=$LOG_DIR \
+    logger.wandb_enabled=True \
+    logger.wandb.project=nemo-rl \
+    logger.wandb.name=$EXP_NAME \
+    logger.monitor_gpus=True \
+    logger.tensorboard_enabled=True \
+    checkpointing.enabled=True \
+    checkpointing.checkpoint_dir=$CKPT_DIR \
+    $@ \
+    2>&1 | tee $RUN_LOG
+
+# Convert tensorboard logs to json
+uv run tests/json_dump_tb_logs.py $LOG_DIR --output_path $JSON_METRICS
+
+# Only run metrics if the target step is reached
+if [[ $(jq 'to_entries | .[] | select(.key == "train/loss") | .value | keys | map(tonumber) | max' $JSON_METRICS) -ge $MAX_STEPS ]]; then
+    uv run tests/check_metrics.py $JSON_METRICS \
+        'mean(data["train/token_mult_prob_error"]) < 1.1' \
+        'data["train/token_mult_prob_error"]["100"] < 1.1'
+fi
+
diff --git a/tests/test_suites/nightly.txt b/tests/test_suites/nightly.txt
index f6a82a8bb7..13d3959324 100644
--- a/tests/test_suites/nightly.txt
+++ b/tests/test_suites/nightly.txt
@@ -25,6 +25,9 @@ tests/test_suites/llm/grpo-deepscaler-1.5b-8K.sh
 # GRPO math test run (32K context mcore)
 tests/test_suites/llm/grpo-math-qwen3-30ba3b-megatron-tp4-32k.sh
 
+# FP8
+tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8.sh
+
 #######
 # SFT #
 #######
diff --git a/tests/unit/models/generation/test_vllm_generation.py b/tests/unit/models/generation/test_vllm_generation.py
index c2c60086b9..75f796960d 100644
--- a/tests/unit/models/generation/test_vllm_generation.py
+++ b/tests/unit/models/generation/test_vllm_generation.py
@@ -613,7 +613,7 @@ def configure_worker_fixed_seed(num_gpus, bundle_indices=None):
 
 
 async def run_hf_train_process(
-    lm_policy, vllm_policy, tokenizer, async_engine, colocated
+    lm_policy, vllm_policy, tokenizer, async_engine, colocated, vllm_precision
 ):
     """Validates that the two policies can work together.
 
@@ -719,7 +719,14 @@ async def run_hf_train_process(
         )
 
         print(f"Average probability multiplicative error: {avg_prob_mult_error}")
-        assert avg_prob_mult_error <= 1.043, "vLLM and HF logprobs should closely match"
+        if vllm_precision == "fp8":
+            assert avg_prob_mult_error <= 1.080, (
+                "vLLM and HF logprobs should closely match"
+            )
+        else:
+            assert avg_prob_mult_error <= 1.043, (
+                "vLLM and HF logprobs should closely match"
+            )
 
         # Step 2: Prepare simplified training data (smaller and with padding removed to prevent OOM)
         # Use a very small sequence for training to ensure it works
@@ -786,16 +793,33 @@ async def run_hf_train_process(
 @pytest.mark.timeout(300)
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
-    ("async_engine", "cpu_offload"), [(True, False), (False, True)]
+    ("async_engine", "cpu_offload", "vllm_precision"),
+    [
+        (True, False, "bfloat16"),
+        (False, True, "bfloat16"),
+        (True, False, "fp8"),
+        (False, True, "fp8"),
+    ],
 )
 async def test_vllm_generation_with_hf_training_colocated(
-    cluster, tokenizer, async_engine, cpu_offload
+    cluster, tokenizer, async_engine, cpu_offload, vllm_precision
 ):
     """This test validates that DTensor policy can work together with colocated vLLM policy."""
+
+    # Skip the fp8 tests if the GPU is not H100 or newer (compute capability < 9.0)
+    if vllm_precision == "fp8":
+        major_capability, _ = torch.cuda.get_device_capability()
+        if major_capability < 9:
+            pytest.skip(
+                f"Skipping FP8 test. GPU compute capability {major_capability}.0 is < 9.0 (H100 required)."
+            )
+
     # Create VllmGeneration Policy
     print("Creating vLLM policy...")
     vllm_config = deepcopy(basic_vllm_test_config)
     vllm_config["vllm_cfg"]["async_engine"] = async_engine
+    vllm_config["vllm_cfg"]["precision"] = vllm_precision
+
     vllm_config = configure_generation_config(vllm_config, tokenizer)
     vllm_policy = VllmGeneration(cluster, vllm_config)
     vllm_policy.finish_generation()
@@ -813,17 +837,33 @@ async def test_vllm_generation_with_hf_training_colocated(
     vllm_policy.prepare_refit_info(state_dict_info)
 
     # Test
-    await run_hf_train_process(lm_policy, vllm_policy, tokenizer, async_engine, True)
+    await run_hf_train_process(
+        lm_policy, vllm_policy, tokenizer, async_engine, True, vllm_precision
+    )
 
 
 @pytest.mark.timeout(300)
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
-    ("async_engine", "cpu_offload"), [(True, False), (False, True)]
+    ("async_engine", "cpu_offload", "vllm_precision"),
+    [
+        (True, False, "bfloat16"),
+        (False, True, "bfloat16"),
+        (True, False, "fp8"),
+        (False, True, "fp8"),
+    ],
 )
 async def test_vllm_generation_with_hf_training_non_colocated(
-    policy_cluster_separate, tokenizer, async_engine, cpu_offload
+    policy_cluster_separate, tokenizer, async_engine, cpu_offload, vllm_precision
 ):
+    # Skip the fp8 tests if the GPU is not H100 or newer (compute capability < 9.0)
+    if vllm_precision == "fp8":
+        major_capability, _ = torch.cuda.get_device_capability()
+        if major_capability < 9:
+            pytest.skip(
+                f"Skipping FP8 test. GPU compute capability {major_capability}.0 is < 9.0 (H100 required)."
+            )
+
     """This test validates that DTensor policy can work together with non-colocated vLLM policy."""
     generation_cluster_separate = get_generation_cluster_separate(1)
 
@@ -831,6 +871,7 @@ async def test_vllm_generation_with_hf_training_non_colocated(
     print("Creating vLLM policy...")
     vllm_config = deepcopy(basic_vllm_test_config)
     vllm_config["vllm_cfg"]["async_engine"] = async_engine
+    vllm_config["vllm_cfg"]["precision"] = vllm_precision
     vllm_config["colocated"]["enabled"] = False
     vllm_config = configure_generation_config(vllm_config, tokenizer)
     vllm_policy = VllmGeneration(generation_cluster_separate, vllm_config)
@@ -856,7 +897,9 @@ async def test_vllm_generation_with_hf_training_non_colocated(
     vllm_policy.prepare_refit_info(state_dict_info)
 
     # Test
-    await run_hf_train_process(lm_policy, vllm_policy, tokenizer, async_engine, False)
+    await run_hf_train_process(
+        lm_policy, vllm_policy, tokenizer, async_engine, False, vllm_precision
+    )
 
 
 def test_vllm_policy_tensor_parallel(cluster, tokenizer):
@@ -953,16 +996,27 @@ def test_vllm_generate_text(cluster, tokenizer):
 
 @pytest.mark.timeout(180)
 @pytest.mark.parametrize("tensor_parallel_size", [1, 2])
+@pytest.mark.parametrize("vllm_precision", ["bfloat16", "fp8"])
 def test_vllm_weight_update_and_prefix_cache_reset(
-    cluster, tokenizer, tensor_parallel_size
+    cluster, tokenizer, tensor_parallel_size, vllm_precision
 ):
     """Test that the vLLM prefix cache is correctly reset when weights change."""
+
+    if vllm_precision == "fp8":
+        major_capability, _ = torch.cuda.get_device_capability()
+        if major_capability < 9:
+            pytest.skip(
+                f"Skipping FP8 test. GPU compute capability {major_capability}.0 is < 9.0 (H100 required)."
+            )
+
     from nemo_rl.models.policy.lm_policy import Policy
 
     # Create configs
     vllm_config = deepcopy(basic_vllm_test_config)
     vllm_config = configure_generation_config(vllm_config, tokenizer, is_eval=True)
     vllm_config["vllm_cfg"]["tensor_parallel_size"] = tensor_parallel_size
+    vllm_config["vllm_cfg"]["precision"] = vllm_precision
+
     if tensor_parallel_size > 1:
         vllm_config["vllm_kwargs"] = {"distributed_executor_backend": "ray"}
 
@@ -1331,14 +1385,23 @@ async def test_vllm_refit_non_colocated_update_weights(
 
 @pytest.mark.timeout(360)
 @pytest.mark.parametrize("tensor_parallel_size", [1, 2])
+@pytest.mark.parametrize("vllm_precision", ["bfloat16", "fp8"])
 def test_vllm_generation_with_megatron_training(
-    cluster, tokenizer, tensor_parallel_size
+    cluster, tokenizer, tensor_parallel_size, vllm_precision
 ):
     """Test that uses vLLM for generation and Megatron policy for training and logprob computation.
 
     This test validates that vLLM and Megatron policies can work together.
     """
 
+    # Skip the fp8 tests if the GPU is not H100 or newer (compute capability < 9.0)
+    if vllm_precision == "fp8":
+        major_capability, _ = torch.cuda.get_device_capability()
+        if major_capability < 9:
+            pytest.skip(
+                f"Skipping FP8 test. GPU compute capability {major_capability}.0 is < 9.0 (H100 required)."
+            )
+
     if cluster.num_gpus_per_node < tensor_parallel_size:
         pytest.skip(f"Need at least {tensor_parallel_size} GPUs for this test")
 
@@ -1353,6 +1416,7 @@ def test_vllm_generation_with_megatron_training(
     vllm_config["model_name"] = model_name
     vllm_config["tokenizer"]["name"] = model_name
     vllm_config["vllm_cfg"]["async_engine"] = False
+    vllm_config["vllm_cfg"]["precision"] = vllm_precision
     vllm_config = configure_generation_config(vllm_config, test_tokenizer)
 
     # Megatron config with same model
diff --git a/uv.lock b/uv.lock
index a169678e99..6dac98a2d3 100644
--- a/uv.lock
+++ b/uv.lock
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