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There is no single hardware specification for hosting an AI tool. A hosted API usually needs an account, API key, network access and client software, but no model-running hardware on your computer. Local inference is different: your machine or server stores the model and supplies the GPU, RAM, storage and runtime needed for each request.
The right requirement depends on the named model, inference stack, context length, number of simultaneous users and speed you expect. The figures below are documented requirements for specific products—not universal minimums—so check the current documentation for your chosen tool before buying equipment or renting a server.
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What does hosting an AI tool actually mean?
“Hosting” can describe three different arrangements. In a hosted API deployment, a provider runs the model and your application sends requests over the internet. In local inference, you download model weights and run them on hardware you control. A third option places those same self-hosted components on rented cloud infrastructure or a hosting partner.
Hosted API
Your computer does not execute the model. Open WebUI’s provider guidance describes this setup as requiring an account and API key, with “no hardware needed” locally for model inference. You still need a reliable network connection, the provider’s client library or compatible application, secure credential storage and enough local resources to run your own app.
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Local inference
A local server downloads model weights, loads them into memory and performs inference on your CPU, GPU or both. The operating system, drivers, runtime and model format must be compatible. Common stacks named in OpenAI’s gpt-oss overview include vLLM, Ollama and llama.cpp. You pay for and maintain the computer, electricity, storage and upgrades.
Self-hosting on cloud infrastructure
You can run open-weight models on a virtual machine or dedicated server rented from a cloud or hosting provider. OpenAI describes open-weight models as running “on infrastructure you control, or through hosting providers.” This removes the need to own a physical GPU, but you remain responsible for selecting a suitable instance, configuring the runtime, storing model artifacts and paying infrastructure and third-party hosting charges.
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Six variables decide whether a configuration is merely able to load a model or can serve it at a useful speed.
- GPU VRAM: Model weights, attention buffers and runtime overhead must fit in available video memory when you use GPU acceleration. Quantization can reduce memory use, but the exact amount depends on the model and runtime.
- System RAM: The operating system, runtime, CPU offloading, tokenizer, caches and concurrent requests consume regular memory. A machine can have enough VRAM yet still fail because system RAM is exhausted.
- Storage: You need space for model files plus downloaded containers, tokenizer data, caches, logs, job artifacts and generated outputs. Keep free space beyond the file size shown in a download list.
- CPU and instruction support: CPU-only inference depends heavily on core count, memory bandwidth and supported instruction sets. CPU capacity also affects preprocessing and request scheduling even when a GPU performs the matrix operations.
- Context length and concurrency: Longer prompts and more simultaneous users increase memory consumption. A configuration that handles one short request may not handle a long document or several users.
- Runtime compatibility and throughput: Drivers, CUDA or other accelerators, model format and serving software must agree. Decide whether latency, tokens per second or total concurrent sessions matters before selecting hardware.
What hardware does NVIDIA NeMo Helix document?
NVIDIA’s NeMo Helix documentation shows why requirements must be tied to a named workload. Its hosted-provider profile and local GPU profile are substantially different.
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| NeMo Helix scenario | Documented memory | Storage | GPU requirement |
|---|---|---|---|
| CLI, SDK and hosted-provider setup | 8 GB RAM minimum | 16 GB free disk | No GPU requirement stated |
| Local GPU workloads | 16 GB RAM minimum; 32 GB recommended | Additional tens to hundreds of GB for model artifacts and job outputs | 40 GB VRAM minimum; 80 GB recommended |
These numbers describe NeMo Helix’s documented workloads, not every tool that uses an NVIDIA GPU. The local profile’s storage range is intentionally broad because model artifacts and outputs vary by job. If you are evaluating another runtime, use its own matrix rather than transferring the 40 GB or 80 GB figures unchanged.
What does GitLab’s self-hosted guidance imply for model size?
GitLab Duo Self-Hosted publishes model-specific examples that illustrate how quickly requirements grow. Its base guidance calls for eight CPU cores (16 threads) and 32 GB of RAM minimum, with 64 GB recommended for most models.
| GitLab Duo Self-Hosted example | GPU configuration in the guidance | Memory detail |
|---|---|---|
| 7B model | One NVIDIA A100 with 40 GB | Uses the base system guidance |
| 22B model | Two NVIDIA A100 80 GB GPUs | 110 GB minimum VRAM |
| Mixtral models | Still larger configurations than the examples above | Exact needs vary by the specific Mixtral model and deployment |
Those are requirements for GitLab Duo Self-Hosted. They should not be presented as a universal 7B, 22B or Mixtral standard: quantization, context size, batching, software version and service-level targets can change the usable configuration.
Do hosted APIs require a GPU or large local server?
No. When a provider performs inference, your machine generally needs no model-serving GPU. You need an account, API key, network connectivity and whatever client software the provider specifies.
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Local resources still matter for the application around the API. A web interface, database, document-processing pipeline, reverse proxy or monitoring stack consumes CPU, RAM and storage. Keep credentials out of source code, restrict outbound access to the endpoints you actually use and plan for provider rate limits and outages. Verify the provider’s current authentication, software and networking requirements because those details vary by service.
How do I size a local AI host step by step?
Start with the workload rather than shopping for a generic “AI computer.” The following sequence prevents most mismatches.
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- Choose the runtime. Confirm whether you will use vLLM, Ollama, llama.cpp or another supported stack. Check its accelerator support, operating-system requirements and maximum context or batching behavior.
- Set a concurrency and latency target. Decide whether one person will send occasional prompts or whether a team needs simultaneous sessions. Measure the required response time; “it loads” is not the same as “it serves users acceptably.”
- Calculate GPU memory first, then add headroom. Include weights, key-value cache, runtime overhead and any other models loaded at the same time. If the model cannot fit, determine whether quantization or CPU offload is supported before assuming a larger card is the only answer.
- Check system RAM and CPU. Reserve memory for the operating system, containers, tokenization, document retrieval and background services. Use the runtime’s guidance for CPU offload and thread counts.
- Allocate storage for the whole lifecycle. Budget for model downloads, duplicate versions, container images, logs, temporary files, generated artifacts and backups. Fast SSD storage improves load and restart times even when it does not change model quality.
- Validate drivers and thermals. Install the runtime’s supported driver version, test the accelerator, and ensure the chassis, power supply and cooling can sustain the intended load.
- Run a representative test. Use the actual prompt lengths, documents and number of users you expect. Record latency, throughput, VRAM, RAM, temperature and failure behavior before opening the service to others.
What is the difference between local hardware and hosted infrastructure?
Both can run the same open-weight model, but they shift responsibility and cost in different directions.
| Decision factor | Local or owned server | Hosted GPU or provider infrastructure |
|---|---|---|
| Compatibility | You select and maintain the operating system, drivers, GPU and runtime. | You select an instance and still configure the runtime, while the provider controls the underlying hardware. |
| Capacity changes | Upgrades require purchasing, installing and validating hardware. | Capacity can usually be changed by selecting another instance, subject to availability and migration work. |
| Data location | Data remains in equipment or facilities you control, subject to your own security. | Data and logs reside in the provider environment; review its region, retention and access controls. |
| Cost profile | Up-front hardware, electricity, cooling, maintenance and replacement costs. | Recurring compute, storage, transfer and third-party hosting fees. |
| Operations | You handle monitoring, patching, backups, capacity and hardware failures. | You still operate the model and application, while the provider handles selected infrastructure layers. |
| Best fit | Predictable workloads, strict control requirements or existing suitable hardware. | Variable demand, rapid trials or teams that do not want to buy GPUs. |
How much storage, RAM and VRAM should I buy?
Do not convert one product’s minimum into a shopping rule. Use the named model’s memory estimate, then add capacity for your workload and operational overhead.
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- For a hosted API client: Start with the provider’s application requirements. NeMo Helix’s hosted-provider profile, for example, lists 8 GB RAM and 16 GB free disk, with no GPU requirement stated.
- For a local GPU workload: NeMo Helix lists 16 GB RAM minimum, 32 GB recommended, 40 GB VRAM minimum and 80 GB recommended, plus tens to hundreds of GB for artifacts and outputs.
- For larger self-hosted services: GitLab’s guidance starts at 32 GB system RAM minimum and 64 GB recommended for most models, then scales to multiple data-center GPUs for larger model examples.
- For a mixed CPU/GPU design: Confirm that the runtime supports offloading and understand the speed penalty. More system RAM cannot compensate for an incompatible or undersized GPU in every runtime.
What costs belong in an AI hosting budget?
OpenAI states that compute, storage and third-party hosting fees depend on the infrastructure and workload selected. For local deployment, include the purchase price of the GPU, host system, SSDs, power supply and cooling, plus electricity, maintenance and eventual replacement. For rented infrastructure, include hourly or monthly compute, persistent disks, snapshots, data transfer, observability and support charges.
Model experimentation can multiply storage costs because teams keep several quantizations, checkpoints and container images. Estimate peak concurrency rather than average traffic when selecting capacity, then compare the cost of leaving a server running with the cost of starting instances only when needed.
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- Out-of-memory errors during model load: Reduce quantization or context length if supported, unload other models, increase VRAM or use a runtime that supports CPU offload. Check both GPU and system memory instead of assuming the GPU is the only limit.
- Model loads but responses are too slow: Measure prompt length, batch size, CPU offload and storage speed. A smaller model, shorter context or more capable accelerator may be a better solution than simply adding disk space.
- Crashes during concurrent requests: Lower the concurrency or per-request context allocation, then test again. Reserve memory for caches and background services.
- Driver or runtime incompatibility: Match the model format, accelerator libraries, driver version and serving software versions documented by the runtime. Reproduce the test in a clean environment before changing several variables at once.
- Disk fills after deployment: Track model caches, logs, temporary files and generated artifacts. Set retention limits and alerts, and keep enough free space for a model reload or update.
- Hosted API requests fail: Check API-key scope, network egress, endpoint configuration, rate limits and provider status. Your local machine may be healthy while the remote service or credential is unavailable.
What should I verify before deploying an AI tool?
Use this checklist against the exact product documentation and your own workload.
- Model name, variant, quantization and license recorded.
- Inference runtime and supported operating system selected.
- GPU VRAM, system RAM and SSD capacity measured with headroom.
- Context length, expected throughput and concurrent users defined.
- Driver, accelerator library and runtime versions tested together.
- Data region, retention, access controls and logging policy decided.
- Compute, storage, transfer, electricity and maintenance costs estimated.
- Monitoring, backups, update and rollback procedures prepared.
- Failure tests completed for memory exhaustion, provider outage and disk growth.
What is the practical rule for AI hosting requirements?
Identify the deployment model first. A hosted API shifts model compute to the provider and normally needs only credentials, networking and client software locally. A local or self-hosted model shifts responsibility to your infrastructure, where VRAM, RAM, storage, runtime compatibility, context length and concurrency all matter. Treat every published number—such as NeMo Helix’s 40 GB VRAM minimum or GitLab’s A100 examples—as a product-specific reference, then confirm the current documentation and test your real workload before committing to hardware or a hosting contract.
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Frequently Asked Questions
Can I run an AI tool on a CPU without a GPU?
Some runtimes support CPU-only inference, but the practical speed and model size depend on the processor, memory bandwidth, quantization and context length. Confirm that the chosen model and runtime explicitly support CPU execution before removing GPU capacity from the design.
Does quantization eliminate the need for high-end hardware?
Quantization can lower the memory required by a model, but it does not remove runtime overhead, context-cache memory or system-RAM needs. It may also affect output quality and throughput, so test the quantized build with representative prompts.
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Should I keep model files on the same disk as my application?
They can share a disk, but reserve capacity for updates, temporary files, logs and rollback copies. Separating model storage from the operating-system volume can make capacity alerts and recovery easier to manage.
How often should I recheck hosting requirements?
Recheck whenever the model variant, runtime, context target, concurrency, driver or provider plan changes. Documentation and recommended configurations are version-sensitive, so a configuration that worked for one release is not a permanent guarantee.
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