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Choose local, self-hosted, or hosted LLM inference by starting with your workload’s non-negotiable data and connectivity requirements, then checking which option can meet its quality, latency, capacity, and cost targets. Local inference can keep model execution within an approved environment and work offline, but puts hardware and maintenance responsibilities on you. A hosted API reduces infrastructure work but sends requests to a provider, so its specific data controls and terms matter. A governed hybrid setup can combine the two.
At a glance
| Name | Best for | Key spec | Platforms | Checked |
|---|---|---|---|---|
| On-device/local | Workloads requiring offline use or inference inside an approved environment | Inference runs on the user’s machine; hardware, updates, and security are operator responsibilities | User’s machine | Oct 2026 |
| Self-hosted in your cloud account | Teams that want more control over deployment than a provider’s model API | Can use an open-weight model, but still runs in a data center | Your cloud account, such as an EC2 or SageMaker deployment | Oct 2026 |
| Hosted model API | Teams seeking provider-managed model serving and scalable compute | Requests go to a provider; data handling depends on that provider, endpoint, and account settings | Provider API | Oct 2026 |
What do “local,” “self-hosted,” and “hosted” mean?
These labels describe where inference runs and who operates the serving environment. They are not interchangeable: a model with downloadable weights can still be hosted in a data center, and “local” does not automatically mean every part of an application’s data path stays on the device.
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On-device or local
The model executes on the user’s own computer. This can support offline use and keep inference within an approved environment, but the operator must provide suitable hardware, maintain the runtime and its dependencies, and protect the machine, logs, and data. Microsoft’s local-versus-cloud guidance puts the maintenance obligation plainly: “The user is responsible for maintaining the system and installing updates.”
Self-hosted in your cloud account
You run model serving in infrastructure under your organization’s account rather than calling a provider’s model API. AWS describes EC2 and SageMaker deployment options, for example. This gives the organization more control over the deployment, but does not make inference on-device: it still runs in a data center, and you need to account for the infrastructure and operational work.
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Hosted model API
A provider operates model serving and your application sends requests to its endpoint. That can remove the need to provision and maintain inference hardware yourself, but prompts and related request data cross to the provider. Review the exact service’s data controls, terms, region, and endpoint behavior rather than assuming that every hosted API handles data alike.
How should I decide between local and hosted inference?
Set hard constraints first, then compare the viable options against the same tasks and quality bar. There is no universal winner: a decision that works for short classification requests may fail for long-context reasoning or peak concurrent demand.
- Define the permitted workload. List representative tasks, such as short extraction or classification, long-context work, and code or reasoning if relevant. Include the expected peak number of simultaneous requests.
- Set acceptance thresholds. Record the minimum acceptable answer quality, time to first token, and completed-answer latency. Keep the threshold consistent across options so a cheap answer that fails the task is not treated as equivalent to a successful one.
- Write down hard data and connectivity constraints. Specify whether prompts or attachments may leave an approved boundary, whether offline operation is required, and any geographic or data-handling restrictions.
- Check capacity and operations. Establish whether you can supply and administer local or self-hosted infrastructure, or whether provider-managed serving better fits your team’s capacity.
- Evaluate each viable deployment under ordinary and peak demand. Use the same prompt suite and include failure behavior: what happens if the network, local runtime, or selected model is unavailable?
- Compare total cost for successful work. Forecast recurring and operational costs for the same workload and service level, and compare cost per completed task that meets the quality threshold.
Microsoft suggests bounded tasks as possible candidates for local models and more demanding reasoning or long-document scenarios as possible candidates for cloud models. Treat that as a starting hypothesis, not a result for your particular workload: the model and deployment still need to meet your stated quality and service requirements.
Does local inference keep prompts private?
Local inference can reduce the need to send prompts to an external model API, but privacy depends on the entire data path and the controls around it. Check prompts, outputs, attachments, logs, caches, model downloads, and backups—not just where the model executes.
- Identify what data each part of the application stores or transmits, including diagnostic and access logs.
- Determine who can access the device or serving environment, how access is authenticated, and how long records are retained.
- For hosted services, check the specific provider’s settings, endpoint, region, and contract. Policies can differ across services and endpoints.
- For local and self-hosted systems, account for operator access, machine security, patching, and any external connections made by the surrounding application.
For one specific hosted example, OpenAI’s API data-controls documentation says API data is not used to train or improve models unless the customer opts in. The same documentation describes default abuse-monitoring logs that may contain customer content and are retained for up to 30 days, subject to documented exceptions. It also distinguishes endpoint-specific application state and describes controls that require approval. “Not used for training” therefore does not mean “never stored,” and OpenAI’s stated policy should not be generalized to other providers.
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Which option is faster or more reliable?
Neither local nor hosted inference is always faster. A hosted request includes a network round trip, while local performance depends on the available compute, memory headroom, and how many requests compete for it.
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Compare the same prompts under ordinary and peak conditions. Record time to first token and total completion time, along with queuing and failures. A local machine may avoid the inference network round trip but queue requests or slow down when memory is constrained. A cloud deployment may scale without a local GPU purchase, but it depends on the network and provider availability. The right comparison is the service level your workload needs, not the assumption that one location is inherently quicker.
Plan what happens when a dependency fails. If the local runtime is unavailable, decide whether work pauses or moves to another approved route. If the network or hosted provider is unavailable, determine whether a local route can handle the task. Any fallback that sends content to a different provider should be visible and governed by policy or consent; do not silently route sensitive requests elsewhere.
How do I estimate local versus hosted LLM cost?
There is no defensible universal break-even point. Compare projected provider charges with the full cost of operating local or self-hosted inference for the same successful workload and service level.
Hosted API costs
Estimate monthly request volume multiplied by average input and output tokens, then apply the chosen provider’s current pricing. Check how cached input is treated, whether processing modes differ in price, whether data-residency requirements add charges, and whether any other resources are billed separately. Pricing pages can change, so use the live price for the precise endpoint and mode rather than relying on an old per-token quote.
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Local and self-hosted costs
Include hardware purchase or rented compute, hardware amortization, electricity at intended utilization, storage, cooling where material, administration, upgrades, and the cost of meeting reliability requirements. A machine that can load a model is not automatically sufficient for production: context length, concurrency, and required response times also affect capacity.
AWS’s October 27, 2025 public-sector guidance recommends considering traffic, seasonality, latency, availability, hardware amortization, power, and operations in a break-even assessment. Whichever route you consider, divide the cost by tasks that complete successfully and meet the quality threshold. Comparing a low-cost failed local response with a successful hosted response produces a misleading figure.
What hardware and model details matter for local LLMs?
Choose the model and workload before buying hardware. Model format, runtime and device support, memory available for the model and its context, and concurrent requests all affect feasibility; a model-memory figure alone does not guarantee production capacity.
Check the model before the machine
Verify the exact model’s license, revision, file format, and usage terms. OpenAI’s August 5, 2025 announcement describes gpt-oss-20b as an Apache-2.0 open-weight model that can run within 16 GB of memory; its model card gives a memory description specific to the MXFP4 format. That example shows that some open-weight models can fit modest memory budgets, but it does not establish that every model has the same license or requirement—or that 16 GB guarantees enough room for a chosen context length, concurrent users, or a production service.
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Ollama documents a default context length of 4,096 tokens that can be changed. This is a runtime default, not the maximum context supported by every model. Its documentation also notes that parallel requests increase context-related memory allocation. Leave room for the workload rather than treating the model’s basic memory figure as the whole system requirement.
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Confirm that the selected runtime supports your model and device, and verify where inference actually runs. Ollama’s ollama ps command reports whether a loaded model is on GPU, CPU, or both. That can help distinguish a compatible-looking setup from one that is using the accelerator as intended.
Use hardware examples conditionally
NVIDIA’s manufacturer specification lists 32 GB of GDDR7 memory and 575 W rated total graphics power for the GeForce RTX 5090. Ollama lists the card among its supported NVIDIA GPUs. These facts make it a specific hardware example, not a default recommendation or assurance that any model and workload will fit or perform adequately. NVIDIA lists the Founders Edition card length as 304 mm; partner-card dimensions can differ. Rated graphics power is not a measured electricity bill, so account for actual utilization and local electricity costs. Existing RAM or SSD upgrades may be more relevant if memory or model-file storage is the real constraint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What security responsibilities come with running a model yourself?
Control over the machine or account is not, by itself, a security control. Protect the serving interface, credentials, logs, dependencies, and boundaries between users or tenants according to the actual deployment.
- Restrict network exposure to the addresses and users that need access; avoid exposing a serving endpoint broadly by default.
- Protect authentication credentials and logs, and patch the runtime and its dependencies.
- Separate tenants where required by the workload and threat model.
- Review security guidance for the exact serving software version and topology, especially for multi-node deployments.
Ollama documents a localhost bind by default as well as a configuration that can expose the service to a network. Changing that setting changes the exposure and should prompt an access-control review. The vLLM security guidance warns that inter-node communications in multi-node deployments are insecure by default and that --api-key covers only some endpoint prefixes. Do not treat a single API-key option as complete protection for every route or deployment.
When does a hybrid setup make sense?
Hybrid routing is useful when some tasks can be handled adequately within local capacity but other approved tasks need a hosted endpoint. It lets the deployment decision vary by task instead of forcing every request through the same route.
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Define which task types are eligible for local processing and which may go to a hosted service. Set a quality threshold for local answers and a clear fallback rule when that threshold or local capacity is not met. If fallback means sending data off-device, disclose that behavior and require whatever policy approval or user consent applies. Include the fallback in cost, latency, and failure planning; a hybrid system has both local operations and provider dependencies to govern.
How should I make the final choice?
Keep only the options that meet the hard requirements, then choose among them using measured results from your own permitted workload and a complete cost and operations comparison. The deployment decision is specific to the model, task mix, service level, and governance obligations—not a verdict that local or hosted inference is always better.
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- Choose on-device/local when offline use or keeping inference inside an approved environment is a requirement and you can operate the hardware securely.
- Choose self-hosting when running in your cloud account provides a needed level of deployment control and your organization can manage the infrastructure.
- Choose a hosted API when provider-managed serving and scalable compute fit the workload, and the chosen provider’s data controls and terms satisfy your requirements.
- Choose hybrid routing when tasks differ meaningfully in capability needs and you can make data-leaving fallback explicit and governed.
Frequently Asked Questions
Does open-weight mean an LLM is free to run?
Not necessarily. A model license governs use of its weights, while running it can still require hardware or rented compute, electricity, storage, and operational work; check the exact model’s license and terms.
Can I use a CPU instead of a GPU for a local LLM?
A runtime may report a model loaded on CPU, GPU, or both, but that status alone does not tell you whether the setup meets your response-time or concurrency needs. Assess the exact model, runtime, available memory, and workload against your requirements.
Is 16 GB enough to run gpt-oss-20b?
OpenAI’s 16 GB figure is for the released model deployment it describes, with the model card specifying an MXFP4-related memory description. It does not guarantee enough headroom for every context length, concurrent workload, or serving setup.
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