Supported models

Consider these factors when choosing a base model:

Training mode: Every model supports low-rank adapter (LoRA) training. Not every model supports full-weight training. Check the Training modes column in the catalog, or list models programmatically.
Context length: The trainer’s maximum sequence length differs between LoRA and full-weight training.
GPU type: Post-training currently runs only on B200 GPUs (B200-SXM). Support for B300 and GB300 GPUs will be added soon.
Adapter capacity: Each resource caps how many LoRA sessions it accepts at once, which matters if you plan to train several adapters together.

Start with Qwen/Qwen3.8-27B to get your training loop working, then scale up once it runs.

The post-training service currently only supports text models. To fine-tune on images, use vision fine-tuning.

Catalog

This table lists the public B200 configurations and syncs daily from /v1/rl/supported-models. For the live catalog, list the models programmatically.

Model	API ID	GPU types	Training modes	Max context (LoRA)	Max context (full)	Max LoRA rank	Generator context	LoRA sessions per resource
Qwen3.6 35B A3B	`Qwen/Qwen3.6-35B-A3B`	`B200-SXM`	LoRA	262,144	Not supported	64	262,144	5
Qwen3.8 27B	`Qwen/Qwen3.8-27B`	`B200-SXM`	LoRA, full	262,144	131,072	64	262,144	5
Kimi K3	`moonshotai/Kimi-K3`	`B200-SXM`	LoRA	65,536	Not supported	32	69,632	3
GLM 5.3	`zai-org/GLM-5.3`	`B200-SXM`	LoRA	159,744	Not supported	32	163,840	3
MiniMax M3	`MiniMaxAI/MiniMax-M3`	`B200-SXM`	LoRA	262,144	Not supported	32	262,144	3

How to read the columns:

Training modes: Every model lists LoRA. Models that also list full support full-weight training. For the others, use lora_enabled=True.
Max context: The longest sequence (prompt plus response) that one forward_backward sample can hold.
Generator context: The longest sequence (prompt plus completion) that a sample call can handle. Where it’s larger than Max context (LoRA), keep GRPO rollouts within the trainer’s limit, because forward_backward rejects longer samples.
LoRA sessions per resource: The number of adapter sessions one resource accepts at once. The service catalog sets this limit per model and per GPU type. The API doesn’t return it yet, so this column is maintained by hand. To train several adapters on one resource, see Train multiple adapters on one base model.

Single-session variants

The following models also support single-session variants for better throughput. These variants support LoRA training only and accept a single LoRA adapter, so each resource can run only one training session at a time.

Qwen3.6 35B A3B: Qwen/Qwen3.6-35B-A3B-single-session.
Qwen3.8 27B: Qwen/Qwen3.8-27B-single-session.

List supported models programmatically

To list the live B200 configurations:

from together import Together

client = Together()

for model in client.post_training.supported_models.get().data:
    for config in model.compute_configs or []:
        print(model.base_model)
        trainer = config.trainer_config
        generator = config.generator_config
        modes = []
        if trainer is not None and trainer.lora is not None:
            modes.append(f"lora (ctx {trainer.lora.max_seq_length})")
        if trainer is not None and trainer.full is not None:
            modes.append(f"full (ctx {trainer.full.max_seq_length})")
        if generator is not None:
            modes.append(f"sampling (ctx {generator.context_length})")
        print(f"  {config.gpu_type}: {', '.join(modes)}")

The output has one block per model, like this:

Qwen/Qwen3.6-35B-A3B
  B200-SXM: lora (ctx 262144), sampling (ctx 262144)

To run a similar listing from a cookbook checkout, use uv run python scripts/supported_models.py.

Choose a GPU type

Set gpu_type="B200-SXM" inside ComputeConfig when you create a resource. The trainer and generator always run on the same GPU type. Check the model’s row in the catalog for the training modes available on B200.

Request a model

To request a model or config that isn’t in the catalog, contact your Together AI account team.

GET STARTED

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INFERENCE APIS

DEDICATED MODEL INFERENCE

DEDICATED CONTAINER INFERENCE

POST-TRAINING

FINE-TUNING

GPU CLUSTERS

CODE EXECUTION

ADMINISTRATION

Catalog

Single-session variants

List supported models programmatically

Choose a GPU type

Request a model

​Catalog

​Single-session variants

​List supported models programmatically

​Choose a GPU type

​Request a model

Catalog

Single-session variants

List supported models programmatically

Choose a GPU type

Request a model