document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls. Time the job and identify CPU, storage or external-service waits before replacing hardware.
Imports, processing and simultaneous users.Hardwarevereisten voor lokaal diepgaand onderzoek: RAM, CPU, opslag, documentinname en AI-backends
Plan lokaal diepgaand onderzoek naar RAM, CPU, permanente opslag en serviceafhankelijkheden, met op workloads afgestemde dimensionering van ZimaOS, back-ups en praktische hardwarekeuzes.
Local Deep Research hardware requirements at a glance
Local Deep Research supports multi-step research, retrieval and configured model/API calls. Size it around document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls, including the complete service stack and durable data. Budget ingestion workers, search/vector storage and inference separately. A lightweight web frontend does not establish the RAM or GPU needed by an indexing or generation backend.
- RAM
- The linked source does not establish a fixed numerical whole-host baseline for this complete workload. Size the full stack during the busy operation described below.
- CPU
- The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite.
- Storage
- Persist original documents, parsed content, embeddings/indexes, application database and model configuration.
- Required services
- Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology.
- Main sizing variable
- document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls.
- Practical Zima starting point
- ZimaCube 2 Standard with SSD-backed active state is an editorial starting point for modest use. Include the OS and all companion services; it is not a measured capacity guarantee.
From official requirements to the right setup
Plan the complete Local Deep Research workload before selecting a server.
-
Official requirements
Choose the exact edition, stable release and supported container/runtime. Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology.
-
Confirm your needs
Test a representative busy operation: document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls. Record peak resident memory, CPU, disk latency and completion time, including other containers.
-
Leave room to grow
Place active state on reliable persistent storage. Persist original documents, parsed content, embeddings/indexes, application database and model configuration. Budget working space for an upgrade or import and test recovery before relying on the deployment.
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Run it on ZimaOS
On ZimaOS, use a compatible amd64 container through Custom Install and reproduce all required services, volume mappings and device access. The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite.
Check every playback client
- Exact edition and stable release
- Compatible amd64 image/runtime
- All required companion services
- Realistic peak activity
- Persistent data and file ownership
- Free host ports and reachable URLs
- Working space and backup retention
- Successful database/file restore
Official minimum requirements
Reviewed October 30, 2026 against the linked upstream deployment documentation. Numerical values retain their stated minimum, recommendation or example scope; select release-compatible instructions before installation.
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. Numerical examples above retain their stated deployment scope; co-hosted services and independent backups need additional capacity.
| Requirement | Official minimum | What this supports |
|---|---|---|
| RAM guidance | The linked source does not establish a fixed numerical whole-host baseline for this complete workload. Size the full stack during the busy operation described below. | Official guidance keeps its original test, recommendation or example scope. |
| CPU guidance | The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. | Core counts do not predict a fixed user, device or document capacity. |
| Storage planning | Persist original documents, parsed content, embeddings/indexes, application database and model configuration. | Installation space is separate from retained content, working files and backups. |
| Service topology | Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology. | Include every stateful companion service in the host budget. |
| Persistent state | Persist original documents, parsed content, embeddings/indexes, application database and model configuration. | An image rebuild or container replacement is not a backup. |
| Deployment boundary | The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. | Check the linked project instructions for the selected edition and release. |
When to upgrade your hardware
Use observed Local Deep Research workload pressure to decide when an upgrade is warranted.
Busy operations exceed the available window
The complete stack runs short of memory
Persistent data approaches available capacity
Inspect the application and each required service for OOM events, sustained swap or growing queues. Reduce unnecessary concurrency before adding capacity.
Shared hosts and background work.Persist original documents, parsed content, embeddings/indexes, application database and model configuration. Track primary state and backup retention separately.
Long-lived installations and growing collections.Plan hardware growth with confidence
Address storage, memory, compute and deployment limits separately.
Use suitable active storage
Put latency-sensitive application/database state on durable local SSD storage and keep recoverable copies.
SATA or PCIe SSD according to measured data growth.Add memory for the complete stack
Choose a higher-RAM host when peak application and companion-service usage leaves inadequate OS headroom.
ZimaCube 2 Standard or ZimaCube 2 Pro according to the measured constraint.Separate capacity from compute
A multi-drive system helps when retained content and backup storage are the problem; it is not automatically a faster application.
ZimaCube 2 when a broader multi-drive workload justifies it.Fix the observed bottleneck
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite.
Check service configuration, I/O and network behavior before selecting a stronger CPU.Can it run on ZimaOS?
Official one-click ZimaOS availability has not been certified for this guide. Use the supported upstream runtime and service topology; consider Custom Install only when a maintained compatible container and all required host capabilities are available.
Custom install Local Deep Research in ZimaOS
Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology. Persist: Persist original documents, parsed content, embeddings/indexes, application database and model configuration.
Custom install Local Deep Research in the ZimaOS app โUse the official Local Deep Research deployment instructions
Keep the selected stable release, persistent paths and companion services aligned with upstream documentation.
Review Local Deep Research official installation โCheck workload-specific deployment details
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite.
Review Local Deep Research deployment details โWhich Zima hardware fits Local Deep Research?
Start with the lowest reasonable host for the actual workload. Product specifications were checked against the current product pages.
Which workload best matches your Local Deep Research deployment?
Modest Local Deep Research deployment
Start with durable active data and a measured whole-stack memory budget.
- Workload optionZimaCube 2 Standard
Heavier jobs or more companion applications
Choose added memory or compute only when observed peaks justify the change.
- Workload optionZimaCube 2 Pro
These are practical workload recommendations, not official app requirements, compatibility certification, measured capacity limits or performance guarantees. Include all co-hosted services and independent backups.
| Zima hardware | Best for | Example workload | Core configuration | Recommended boundary | Next step |
|---|---|---|---|---|---|
| ZimaCube 2 Standard | Modest Local Deep Research workload | document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls. |
|
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. | Get Now |
| ZimaCube 2 Pro | More processing or shared-service headroom | Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology. |
|
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. | Get Now |
What the Press Says
Highlights from trusted reviewers worldwide.
โZimaCube 2: Not just another NAS, tested with 25TB storage, local AI agents, 4K transcoding, and real homelab workflows.โRead full review
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I use ZimaCube Pro as 5th Proxmox cluster node. It runs several VMs and containers, including a VM with GPU passthrough to run a self-hosted LLM. A specific LXC container runs a Samba server for NAS capabilities using four of six RAID 6 SATA HDDs with ZFS.
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Frequently asked questions
Answers to Local Deep Research RAM, CPU, storage, Docker, database and ZimaOS sizing questions.
How much RAM should I plan for Local Deep Research?
The linked source does not establish a fixed numerical whole-host baseline for this complete workload. Size the full stack during the busy operation described below. Include ZimaOS, the application and its companion services rather than reserving the entire machine for one container.
What drives the CPU requirement?
The linked source does not establish a fixed numerical whole-host baseline for this complete workload. Measure CPU use during realistic activity. The workload to test is: document imports, parsing/OCR, embedding batches, retrieval queries and simultaneous model calls.
Which data must survive a container update?
Persist original documents, parsed content, embeddings/indexes, application database and model configuration. Preserve a consistent database/file set and verify that your recovery procedure restores a working application.
What other services belong in the budget?
Follow the service topology in the linked upstream deployment for the chosen edition; include every required database, worker and storage service. The repository references SQLite, Elasticsearch / OpenSearch. These may be integrations or alternative backends; install only the services required by your selected topology.
Does this workload need a dedicated GPU?
Accelerator needs depend on the selected inference, synthesis, browser or media backend. The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite.
Can I deploy it on ZimaOS?
Use an amd64 container and the current upstream service topology through Custom Install when a suitable maintained image is available. Container availability alone is not a compatibility certification.
What should I check before upgrading hardware?
The current upstream x86-64 guidance requires AVX-capable CPUs for packaged scientific-Python dependencies. Intel lists AVX2 for the N150; verify the CPU flags exposed to the actual runtime and the selected dependencies. Model inference remains a separate workload. Core-class ZimaCube hardware is an editorial option for more processing headroom, not an AVX prerequisite. Measure the same representative operation before and after a configuration or hardware change.
Which Zima device is a practical starting point?
ZimaCube 2 Standard is the editorial starting point for this modest deployment. Choose ZimaCube 2 Pro for measured memory/processing pressure or broader shared services; retained data determines storage expansion.
What sources informed this Local Deep Research hardware guide?
This guide was reviewed against the linked project deployment documentation on October 30, 2026. Zima device choices are editorial workload guidance, separate from upstream requirements and measured performance.
