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title: "Multi-GPU"
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This guide covers advanced training configurations for multi-GPU setups using Axolotl.

## Overview {#sec-overview}

When training on multiple GPUs, Axolotl supports 3 sharding/parallelism strategies. Additionally, you can layer specific optimization features on top of that strategy.

You generally cannot combine these strategies; they are mutually exclusive.

1.  **DeepSpeed**: Powerful optimization library, supports ZeRO stages 1-3.
2.  **FSDP (Fully Sharded Data Parallel)**: PyTorch's native sharding implementation (Recommended).
3.  **DDP (Distributed Data Parallel)**: PyTorch's native parallelism implementation (Default if neither of the above are selected).

These features can often be combined with the strategies above:

*   **Sequence Parallelism**: Splits long sequences across GPUs (Compatible with DDP, DeepSpeed, and FSDP).
*   **FSDP + QLoRA**: Combines 4-bit quantization with FSDP (Specific to FSDP).

## DeepSpeed {#sec-deepspeed}

### Configuration {#sec-deepspeed-config}

Add to your YAML config:

```{.yaml}
deepspeed: deepspeed_configs/zero1.json
```
### Usage {#sec-deepspeed-usage}

```{.bash}
# Fetch deepspeed configs (if not already present)
axolotl fetch deepspeed_configs

# Passing arg via config
axolotl train config.yml

# Passing arg via cli
axolotl train config.yml --deepspeed deepspeed_configs/zero1.json
```

### ZeRO Stages {#sec-zero-stages}

We provide default configurations for:

- ZeRO Stage 1 (`zero1.json`)
- ZeRO Stage 1 with torch compile (`zero1_torch_compile.json`)
- ZeRO Stage 2 (`zero2.json`)
- ZeRO Stage 3 (`zero3.json`)
- ZeRO Stage 3 with bf16 (`zero3_bf16.json`)
- ZeRO Stage 3 with bf16 and CPU offload params(`zero3_bf16_cpuoffload_params.json`)
- ZeRO Stage 3 with bf16 and CPU offload params and optimizer (`zero3_bf16_cpuoffload_all.json`)

::: {.callout-tip}

Choose the configuration that offloads the least amount to memory while still being able to fit on VRAM for best performance.

Start from Stage 1 -> Stage 2 -> Stage 3.

:::

## Fully Sharded Data Parallel (FSDP) {#sec-fsdp}

FSDP allows you to shard model parameters, gradients, and optimizer states across data parallel workers.

::: {.callout-note}

FSDP2 is recommended for new users. FSDP1 is deprecated and will be removed in an upcoming release of Axolotl.

:::

### FSDP + QLoRA {#sec-fsdp-qlora}

For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).

### Migrating from FSDP1 to FSDP2 {#sec-migrate-fsdp1-fsdp2}

To migrate your config from FSDP1 to FSDP2, you must use the `fsdp_version` top-level config field to specify the FSDP version, and
also follow the config field mapping below to update field names.

#### Config mapping

FSDP1 | FSDP2
-------- | --------
fsdp_sharding_strategy | reshard_after_forward
fsdp_backward_prefetch_policy | **REMOVED**
fsdp_backward_prefetch | **REMOVED**
fsdp_forward_prefetch | **REMOVED**
fsdp_sync_module_states | **REMOVED**
fsdp_cpu_ram_efficient_loading | cpu_ram_efficient_loading
fsdp_state_dict_type | state_dict_type
fsdp_use_orig_params | **REMOVED**
fsdp_activation_checkpointing | activation_checkpointing

For more details, please see the migration guide in the [torchtitan repo](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md). In Axolotl,
if you were using the following FSDP1 config:

```{.yaml}
fsdp_version: 1
fsdp_config:
  fsdp_offload_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
```

You can migrate to the following FSDP2 config:

```{.yaml}
fsdp_version: 2
fsdp_config:
  offload_params: false
  cpu_ram_efficient_loading: true
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: Qwen3DecoderLayer
  state_dict_type: FULL_STATE_DICT
  reshard_after_forward: true
```

### FSDP1 (deprecated) {#sec-fsdp-config}

::: {.callout-note}

Using `fsdp` to configure FSDP is deprecated and will be removed in an upcoming release of Axolotl. Please use `fsdp_config` as above instead.

:::

```{.yaml}
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_offload_params: true
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
```


## Sequence parallelism {#sec-sequence-parallelism}

We support sequence parallelism (SP) via the
[ring-flash-attention](https://github.com/zhuzilin/ring-flash-attention) project. This
allows one to split up sequences across GPUs, which is useful in the event that a
single sequence causes OOM errors during model training.

See our [dedicated guide](sequence_parallelism.qmd) for more information.

## Performance Optimization {#sec-performance}

### Liger Kernel Integration {#sec-liger}

Please see [docs](custom_integrations.qmd#liger) for more info.

## Troubleshooting {#sec-troubleshooting}

### NCCL Issues {#sec-nccl}

For NCCL-related problems, see our [NCCL troubleshooting guide](nccl.qmd).

### Common Problems {#sec-common-problems}

::: {.panel-tabset}

## Memory Issues

- Reduce `micro_batch_size`
- Reduce `eval_batch_size`
- Adjust `gradient_accumulation_steps`
- Consider using a higher ZeRO stage

## Training Instability

- Start with DeepSpeed ZeRO-2
- Monitor loss values
- Check learning rates

:::

For more detailed troubleshooting, see our [debugging guide](debugging.qmd).