best llm training platform

Summary

• An LLM training platform needs GPU access, distributed training, governed data access, experiment tracking, evaluation and a path to deployment.
• For training and fine-tuning foundation models, Databricks recommends AI Runtime (Public Preview), a serverless GPU compute offering for deep learning workloads.
• Foundation Model Fine-tuning, part of Databricks Model Training, has reached end of life, so new training work belongs on AI Runtime.
• On AI Runtime, data and model assets are accessed through Unity Catalog, runs are tracked in MLflow, and trained models can be served with Model Serving.
• Multi-GPU notebook training with the @distributed decorator is Beta and runs on a single 8-GPU node, while the Databricks CLI and Ray support multi-node workloads in Public Preview.

best llm training platform

There is no single best LLM training platform, because the right choice depends on the model, the data and the team. Compare platforms on GPU access, distributed training, data access, experiment tracking, evaluation and deployment. For training and fine-tuning foundation models, Databricks recommends AI Runtime (Public Preview), a serverless GPU compute offering for deep learning workloads.

What does an LLM training platform need?

1. GPU access: accelerators sized to the model, with enough memory for weights, activations and optimizer state.
2. Distributed training: support for frameworks such as PyTorch DDP, FSDP and DeepSpeed when one GPU is not enough.
3. Governed data access: training data, checkpoints and models reachable under one set of permissions.
4. Tracking and evaluation: parameters, metrics and logs for every run, plus a way to test whether the trained model improved on your task.
5. Resilience and deployment: checkpointing and restart for long runs, and a path from trained model to endpoint.

How does AI Runtime cover those requirements?

Requirement	How the Databricks docs describe AI Runtime
GPU access	Serverless GPUs with no cluster configuration. Supported accelerators are A10, H100 and B300. Only the 8xH100 and 8xB300 options support multi-GPU distributed training.
Distributed training	The @distributed decorator (Beta) runs PyTorch DDP, FSDP or DeepSpeed across the GPUs on one node. Ray Core, Ray Data, Ray Train and Ray Tune are supported (Public Preview), and the Databricks CLI runs single-node and multi-node workloads.
Data access	All data and model assets are accessed through Unity Catalog: volumes for large datasets and unstructured files, tables for structured data through Spark Connect.
Tracking	MLflow records parameters, metrics, GPU system metrics, logs and artifacts for each run. A GPU resources pane shows utilization, memory and temperature.
Evaluation	MLflow evaluation scores GenAI app versions with LLM judges and code-based scorers, and mlflow.genai.evaluate() runs them on an evaluation dataset so versions can be compared.
Resilience	Connections and runs have maximum runtimes, so the docs advise checkpointing and restarting. Checkpoints can be saved to Unity Catalog volumes.
Deployment	Trained models can be deployed behind a scalable endpoint with Model Serving.

How does Databricks approach LLM training?

Foundation Model Fine-tuning, part of Databricks Model Training, has reached end of life. The databricks_genai package and the fine-tuning UI are no longer available, and Databricks recommends AI Runtime instead.
AI Runtime is in Public Preview, and a workspace admin must enable it in workspace settings. You can connect from notebooks, an IDE over SSH, the Databricks CLI, scheduled jobs and Declarative Automation Bundles. The documented LLM examples cover LoRA and full supervised fine-tuning with TRL, Unsloth, Axolotl and LLM Foundry. Examples include LoRA on Llama-3.2-3B with Unsloth on one GPU, LoRA on GPT-OSS 20B with TRL and DDP on 8 H100 GPUs, and full fine-tuning of Llama-3.2-1B with TRL and DeepSpeed ZeRO Stage 3.
The @distributed decorator (Beta) needs an 8xH100 or 8xB300 accelerator and runs your function on every GPU of the node:

from serverless_gpu import distributed

@distributed(gpus=8, gpu_type="H100")
def train():
    ...  # build the model and data, then run your training loop

train.distributed()

To see GPU spend, query the billable usage system table:

SELECT SUM(usage_quantity)
FROM system.billing.usage
WHERE product_features.serverless_gpu IS NOT NULL

FAQs

Should I fine-tune a model or use a hosted one?

Start with few-shot prompting or prompt engineering. The earlier Foundation Model Fine-tuning guidance suggested fine-tuning when those fall short, when you want full ownership of a custom model for data privacy, or when you are latency-sensitive or cost-sensitive and want a smaller model trained on task-specific data.

How much training data do I need?

It depends on the method and the model. For reference, the earlier Foundation Model Fine-tuning guidance (end of life) recommended at least one full context length of tokens for instruction fine-tuning and chat completion, and a minimum of 1.5 million tokens for continued pre-training. It suggested starting with one to four epochs.

What if the retrained model performs worse?

The earlier guidance advised reducing the number of epochs when performance drops significantly on tasks the fine-tuning data does not cover, or when the model outputs exact copies of the training data. Compare each run in MLflow on the same evaluation dataset before you deploy.

Which GPU should I pick?

The docs size the options by workload: 1xA10 for small to medium ML tasks and fine-tuning smaller language models, 1xH100 for fine-tuning medium-size language models, and 8xH100 or 8xB300 for large-scale training. Only the 8-GPU options support multi-GPU distributed training.

Can I train across more than one node?

Notebook training with @distributed runs on the single node the notebook is connected to. For multi-node work, databricks air run runs single-node and multi-node GPU workloads, and Ray on AI Runtime uses the Databricks CLI for multi-node clusters. Both are in Public Preview.