Hot Aisle (AMD MI300X)

Hot Aisle is a neocloud offering single- and multi-GPU AMD MI300X VMs. The MI300X has 192 GB of HBM3 memory — more than any NVIDIA single-GPU offering — making it well-suited for large-model finetuning on a single card.

GPU types

GPU type	VRAM	Architecture	GPUs available
`MI300X-192GB`	192 GB	AMD CDNA3 (ROCm 6.4)	1, 2, or 4

Hot Aisle VMs run ROCm, not CUDA. The training stack (torch, transformers, TRL) is installed from the ROCm wheel index at boot. Unsloth, bitsandbytes, xformers, and vLLM are CUDA-only and are not available on this provider. Vanilla TRL training (GRPO, SFT, DPO) is fully supported.

Submitting a job

job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    output_name="qwen-amd-grpo",
    gpu_type="MI300X-192GB",
    gpu_count=1,
    provider="hotaisle",
    hyperparameters={
        "learning_rate": 1e-6,
        "rollouts_per_prompt": 4,
        "max_steps": 50,
    },
)

Performance tuning on MI300X

Three acceleration knobs are available as opt-in hyperparameters. All default off — each one helps some workloads and measurably hurts others, so adopt them based on your workload shape (or benchmark both ways):

Hyperparameter	When it helps	When it hurts
`use_liger`	≥4B models at batch size >1: ~13% faster in our 4B benchmark, and its fused cross-entropy avoids materializing the full-vocab logits tensor — the difference between finishing and OOMing at large batch.	Small models at batch 1 (2.4x slower in our 0.6B benchmark).
`tunableop`	Long runs with steady GEMM shapes (fixed seq length, packing on).	Short or variable-length jobs — tuning happens at runtime, per shape.
`hipblaslt`	Large-GEMM workloads (big models, big batches).	Small GEMMs — 25% slower in our 0.6B batch-1 benchmark.

job = client.training_jobs.create(
    base_model="Qwen/Qwen3-4B",
    dataset_id=dataset.id,
    method="sft_text",
    gpu_type="MI300X-192GB",
    gpu_count=1,
    provider="hotaisle",
    hyperparameters={
        "max_seq_length": 2048,
        "batch_size": 8,
        "use_liger": True,   # the 192 GB play: big batches + fused CE
    },
)

The numbers above come from our benchmark runs of Qwen3 SFT on Capybara (0.6B at batch 1, 4B at batch 8). Your workload’s shape decides which side of each trade-off you land on — when in doubt, run a short max_steps job both ways and compare veri jobs metrics throughput before committing to a long run.

Cold-start behavior

Hot Aisle VMs boot from a stock image and install the ROCm training stack at boot via cloud-init. The first boot includes:

ROCm driver verification (rocm-smi)
PyTorch ROCm wheel installation (torch 2.9.1 from the ROCm 6.4 index)
Training stack install (transformers, accelerate, peft, TRL)
GPU sanity gate (SA5_ROCM_OK — torch must see the AMD GPU)

Cold-start time depends on Hot Aisle’s VM provisioning and network throughput. If the cloud-init path is slow, a prebuilt ROCm worker container may be used in the future to reduce boot time.

Supported training methods

All managed training methods work on Hot Aisle: grpo, sft_text, dpo, and sft_video_gen. The grpo_agentic method is unavailable on all providers (the agentic engine is being rebuilt). Custom scripts (L3) are NVIDIA-only for now — an AMD submit returns a 400.

use_unsloth and load_in_4bit (QLoRA) are not supported on Hot Aisle — both depend on CUDA-only libraries. Set them to false (the default) when using provider="hotaisle".

Get started

Training

Deployments

Evaluations

CLI

Volumes

GPU types

Submitting a job

Performance tuning on MI300X

Cold-start behavior

Supported training methods

Where to go next

All providers

SFT hyperparameters

​GPU types

​Submitting a job

​Performance tuning on MI300X

​Cold-start behavior

​Supported training methods

​Where to go next

All providers

SFT hyperparameters

GPU types

Submitting a job

Performance tuning on MI300X

Cold-start behavior

Supported training methods

Where to go next