Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesYou can adapt Modal’s documented vLLM deployment workflow to serve a Magistral checkpoint, but the available official examples do not provide a verified, end-to-end Magistral-on-Modal recipe. Start with the exact checkpoint’s model card for its vLLM flags, then configure the Modal image, GPU, caches and web endpoint for that model and verify readiness with a real request.
Choose the exact Magistral checkpoint first
“Magistral” and “Ministral” are different model families. Modal’s search-visible Ministral 3 walkthrough is useful for platform patterns, but it is not a Magistral deployment guide. Mistral’s catalogue lists Magistral Small 1.2 and Magistral Medium 1.2 under its 25.09 versions, identifies Small 1.2 as open, and marks earlier versions as legacy or deprecated. Choose and name a specific checkpoint rather than relying on the family name alone: Mistral’s model catalogue.
The command below comes from the Magistral-Small-2507 model card. It is a checkpoint-specific example, not a confirmed command for Magistral Small 1.2, Medium 1.2 or another later release:
vllm serve mistralai/Magistral-Small-2507
--reasoning-parser mistral
--tokenizer_mode mistral
--config_format mistral
--load_format mistral
--tool-call-parser mistral
--enable-auto-tool-choice
--tensor-parallel-size 2The card recommends installing the latest vLLM code using pre-release wheels and says this should automatically install mistral_common >= 1.8.2. Confirm the selected checkpoint’s current card and vLLM release guidance before pinning dependencies; do not assume those instructions or flags apply unchanged to another model version.
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Adapt Modal’s vLLM deployment workflow
Modal’s general vLLM example shows the platform mechanics: package vLLM in a Modal image, deploy the app with modal deploy <script>.py, and use the resulting URL as an OpenAI-compatible API endpoint. Its sample uses Gemma, not Magistral, so take the serving configuration from the selected Magistral model card rather than copying Gemma’s model settings.
Modal’s separate Ministral 3 example illustrates additional patterns: pinning vLLM and dependencies, downloading Hugging Face weights, persisting Hugging Face and vLLM compilation caches in Modal Volumes, selecting a GPU and optionally using CPU/GPU memory snapshots. These are platform ideas to evaluate, not validated Magistral settings. Snapshotting can reduce startup time but adds complexity.
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Implementation sequence
- Set the model and runtime. Use the exact checkpoint identifier and its current vLLM instructions. Match the tokenizer, config, load, reasoning and tool-call settings to that card; do not substitute a Ministral checkpoint or borrow its configuration.
- Build the Modal image. Install a compatible vLLM version and the checkpoint’s required dependencies. If following the 2507 card’s pre-release-wheel guidance, verify the wheel and
mistral_commonrequirements for the runtime you intend to deploy. - Configure model storage and caches. Decide whether to download weights at startup or retain them in a persistent Modal Volume. Consider a separate persistent compilation cache. Check storage permissions, cache paths and behavior across container restarts.
- Select and validate GPU capacity. Set GPU type, GPU count and tensor parallelism to values supported by the chosen checkpoint and runtime. The cited 2507 card’s tensor-parallel setting is two, but the available sources do not establish a Modal GPU type, memory requirement or successful configuration for Magistral.
- Expose the server and deploy. Apply Modal’s documented app and web-server pattern, then run
modal deploy <script>.py. Use the deployed URL with an OpenAI-compatible client as shown in Modal’s example. - Check readiness with a representative request. A deployed app URL does not by itself show that a model replica is loaded and ready. Test the health-check path and a representative generation request before directing callers to the endpoint. Modal’s general example includes a local entrypoint for health checking and sending a request.
No source cited here establishes Magistral inference latency, throughput, cost, or a tested Modal GPU configuration. Treat package compatibility, memory capacity, startup behavior and snapshot support as items to verify in your own deployment, not as settled results.
Handle cold or inactive servers
Modal’s general vLLM example warns that requests can receive 503 Service Unavailable when the server has no active containers. Distinguish an app that has been deployed from a warm replica that is ready to serve. Use the readiness behavior and retry strategy appropriate to the Modal serving primitive you choose; do not treat every 503 as proof that the model configuration is invalid.
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When a managed Modal endpoint may fit better
Modal documents two managed inference options in its Endpoints documentation. Compare them with a custom vLLM app against your actual requirements:
| Option | Capacity and scaling | Billing basis | Model considerations |
|---|---|---|---|
| Shared Endpoint | Managed inference; capacity details are not stated on the cited page. | Per token. | The cited page does not confirm a one-click Magistral checkpoint. |
| Dedicated Endpoint | Isolated capacity with configurable autoscaling, including scale-to-zero. | Compute resources. | Custom weights are supported, but a particular Magistral configuration is not confirmed. |
| Custom vLLM app | You configure and operate the serving app using Modal’s deployment patterns. | Not stated here; check Modal’s current pricing for the chosen resources. | Lets you apply checkpoint-specific vLLM flags, subject to runtime and hardware validation. |
Choose based on checkpoint availability and custom-weight support, configuration control, capacity isolation and scaling behavior, and billing basis. Without current prices and measurements from your own workload, there is no basis to claim one option will cost less.
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