Hardware requirements

WProofreader SDK Server can run on:

• A dedicated server

• A virtual machine

• A Docker container

• A cloud provider such as AWS, Azure, or GCP

VMware virtualization is supported.

AI-based language models are not supported under VirtualBox.

Resource usage depends on the components you install and the languages you enable.

GEC models, Autocomplete, list of installed languages are selected during installation.

Disk space

Base installation

The base installation uses about 1.5 GB in the application directory.

It includes:

• AppServerX binary and libraries

• Algorithmic grammar engine and core language resources

• Style guide resources

• Front-end static assets

Optional components

Component	Disk space
AI GEC model — English	~683 MB
AI GEC model — German	~1.14 GB
AI GEC model — Spanish	~1.56 GB
Autocomplete model — English	~490 MB
NER model — per language	~336 MB. See Configure NER models
N-gram model — per language	Varies by language. See Configure n-gram models
Hunspell spelling dictionaries — per language	Varies. All languages use ~256 MB total

By default, the installer downloads the English GEC model and the English Autocomplete model.

Full installation

A full installation with all GEC models, English Autocomplete, all NER models, and all n-gram models uses about 6.6 GB in the application directory.

Hunspell dictionaries are stored separately in the data directory.

Additional variable space

Item	Space
User dictionaries (UserDictionaries/)	Up to 50 KB per user
Organization dictionaries (CustomDictionaries/)	Up to 5 MB per dictionary
Style guide collections	Up to 5 MB per collection
AppServer log files (AppServer/Logs/)	Rotated at 10 MB per file

Web server access logs can also grow quickly. Monitor them separately.

RAM

Recommended configuration

Configuration	RAM
Default installation — English, English GEC + English Autocomplete	~4 GB
Full installation — All languages, 3 GEC models + English Autocomplete	~10 GB
Spell check and grammar only — no AI	~2 GB

By default, only the English GEC model and the English Autocomplete model are installed.

Breakdown by component

Component	RAM usage
Spell check engine — per active language	~50 MB
Algorithmic grammar check engine — English only	~1 GB
Algorithmic grammar check engine — all supported languages	~4 GB
AI GEC model — English, steady state after startup	~1 GB
AI GEC model — German or Spanish, steady state	~2 GB each
Autocomplete model — English	~700 MB
N-gram model — per active language	~100 MB
NER model — per active language	~336 MB
Cache — 500,000 misspelling entries	~200 MB

Notes:

• The JVM heap limit is about 2 GB by default.

• If you enable 25 or more languages, increase the heap to about 4 GB. See Configure JVM maximum heap size.

• NER and n-gram models are loaded only for languages selected during installation.

• The English NER model is loaded in the default setup.

• German and Spanish NER models are disabled by default.

• RAM usage grows with concurrent users, active languages, and enabled features.

CPU

Item	Value
Minimum	2 vCPUs
Recommended	4 vCPUs

4 vCPUs are recommended when AI language models are enabled.

AVX2 or AVX512 can improve AI model performance.

Cloud instances

Use case	Suggested instance
Light load or evaluation	AWS t3.medium or t3a.medium — 2 vCPUs, 4 GB RAM
Production workload — English GEC	AWS c8a.xlarge — 4 vCPUs, 8 GB RAM
Production workload — all languages	AWS m8a.xlarge — 4 vCPUs, 16 GB RAM
High AI load with batching	AWS g4dn.xlarge or equivalent — NVIDIA T4 GPU

AWS c8a.xlarge is sufficient for English AI workloads.

For high-throughput AI workloads, NVIDIA T4 Tensor Core GPUs improve batch processing.

Scaling considerations

Actual resource usage depends on:

• Number of concurrent users

• Volume and frequency of checked text

• Enabled languages and their resources

• Percentage of errors in submitted text

• Enabled AI models, NER, and Autocomplete