Compute model catalog and memory requirements

Supported model configurations

The catalog API is the source for supported model IDs, revisions and limits. These configurations match the current product contract. Check its response and compatible stock when choosing a rental; catalog requirements are not measured customer performance.

The corresponding model repositories are Qwen3.8-27B-4bit, Qwen3.8-27B-8bit and Qwen3.8-27B-bf16. A repository identifies the model files; its availability does not establish inference quality or rental stock.

Use the catalog ID in the rental API

The short ID, such as qwen3.8-27b-4bit, identifies the platform model. The longer mlx-community/... name identifies the repository used by the serving runtime. Keep those fields separate in your integration. Copy the assigned model ID from the customer dashboard and use the documented rental request.

For a reproducible evaluation, also record the repository revision. A repository name can remain unchanged while its files are updated. The catalog includes a 40-character revision hash for each entry. The agent downloads that revision and serves its local snapshot. Preserve the full hash with evaluation results. It identifies intended model files; the node must also report the exact model ready and pass admission checks. Pinned download behavior

Preserve the exact model revision

These are the revisions pinned in the current catalog and agent. Preserve the exact revision returned by the live API with your evaluation. A pinned hash establishes file identity, not answer quality or speed.

Hugging Face supports selecting a repository snapshot by its full commit hash. Saving the hash helps separate a runtime change from a change in the model files. Download revision documentation

Read the model card associated with the revision for intended uses, training context and evaluation limits. Hugging Face's model-card format includes these fields, but their presence and completeness vary by repository. A conversion repository can require checking the original model card as well. Model card documentation

Keep tokenizer and prompt-format changes in the evaluation record too. The same weight revision can produce different behavior when the application changes its system instructions or generation settings.

Quantization is a choice to evaluate

Lower-bit weights reduce the space used to store model parameters. Whether the resulting model is good enough depends on the task. The quantization comparison guide explains how to evaluate the variants before paying for more memory or accepting a lower-memory configuration.

For extraction, check whether the output contains the requested fields and preserves exact numbers. For code, run the generated code against meaningful checks. For an assistant, include questions where an honest uncertainty response is preferable to a plausible answer. Keep the prompts and scoring rules identical between variants.

Context is also a capacity limit

An advertised maximum context is not an instruction to fill every request to that limit. Your prompt, conversation history and requested answer need an appropriate budget. Longer prompts can take more time before the first token and consume more runtime memory.

Build an input-length check into your application. When a document is too large, choose a deliberate strategy such as selecting relevant passages, shortening history or rejecting the request with a clear explanation. Do not let accidental truncation decide what the model reads.

A catalog entry is not a machine listing

Read current eligible machines from the marketplace API after selecting a model at Models. The supported catalog contains these three configurations. Arbitrary repositories, private fine-tunes, multi-Mac deployments, customer containers and training are outside the managed service. A larger chip or a model repository outside this table does not add support.

Check both endpoints again when you are ready to rent. Providers can use this table to check memory and disk before following the setup guide. Renters should compare the model evaluation with the 30-day cost.