Best larger-system route
Team 32
The smallest system in the range that reaches this model's preferred working allowance.
Explore Team 32DeepSeek model
DeepSeek-OCR 2 Hardware Requirements & Compatibility
The BF16 weight file is about 6.8GB. Every current GPU Servers product clears the model-weight floor; useful product choice is driven by page throughput, resolution, queue size, preprocessing and whether OCR shares a GPU with another service.
- Model version
deepseek-ai/DeepSeek-OCR-2- Family and variant
- DeepSeek-OCR 2 · DeepSeek-OCR-2
- Source version
aaa02f381194- Source updated
- 3 February 2026
DeepSeek DeepSeek-OCR 2
deepseek-ai/DeepSeek-OCR-2 - Minimum GPU memory
- 12GB GPU planning floor
- Recommended hardware
- 24GB or more for practical document batches
- Licence
- Apache License 2.0
- Useful for
- Vision & OCR
Buyer verdict
Where DeepSeek-OCR 2 is a sensible fit
DeepSeek-OCR 2 is relevant for scanned documents, page images, structured extraction and OCR pipelines that need a dedicated local vision model under Apache 2.0.
These figures apply to the named model version. Quantisation, fine-tuning, context length, image resolution, batch size and serving software can materially change the hardware needed.
Hardware requirements
- Minimum
- 12GB GPU planning floor
- Recommended
- 24GB or more for practical document batches
The official weights are 6.8GB; image tensors, runtime and batch need additional memory.
Throughput still depends on resolution, preprocessing, batch and storage.
Technical specification
DeepSeek-OCR 2 model and hardware facts
Specifications shown for source version
aaa02f3811945a91062062994c5c4a3f4c0af2b0, updated
3 February 2026.
- Parameters
- About 3.4B
- Modality
- Image + prompt → text
- Repository weights
- 6.78GB BF16
- Primary workload
- Document OCR
- Library
- Transformers with model-specific code
- Licence
- Apache 2.0
Product compatibility
DeepSeek-OCR 2 compatibility across all 11 GPU systems
- Systems that fit without model splitting
- 11
- Systems needing multi-GPU validation
- 0
A system is listed as fitting when its GPU memory meets the requirement shown above. Speed, usable context, batch size and concurrent users still need testing with the final model and software configuration.
sme workstation
Team 32
- Per GPU
- 32GB
- Total GPU memory
- 32GB
- GPU count
- 1
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
sme workstation
Company 64
- Per GPU
- 32GB
- Total GPU memory
- 64GB
- GPU count
- 2
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
sme workstation
Studio 96
- Per GPU
- 96GB
- Total GPU memory
- 96GB
- GPU count
- 1
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
sme workstation
Studio 192
- Per GPU
- 96GB
- Total GPU memory
- 192GB
- GPU count
- 2
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Value Rack 128
- Per GPU
- 32GB
- Total GPU memory
- 128GB
- GPU count
- 4
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Value Rack 256
- Per GPU
- 32GB
- Total GPU memory
- 256GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Enterprise 384
- Per GPU
- 96GB
- Total GPU memory
- 384GB
- GPU count
- 4
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
Enterprise 768
- Per GPU
- 96GB
- Total GPU memory
- 768GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
pcie rack
H200 1.1TB
- Per GPU
- 141GB
- Total GPU memory
- 1,128GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
frontier partner
Frontier Native 2.3TB
- Per GPU
- 288GB
- Total GPU memory
- 2,304GB
- GPU count
- 8
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
frontier partner
Frontier Rack 20TB
- Per GPU
- 288GB
- Total GPU memory
- 20,736GB
- GPU count
- 72
Also meets this model page's recommended working allowance.
Available GPU memory exceeds the calculated or estimated requirement. Confirm the final precision, software, workload and performance before purchase.
Deployment reality
Strengths, limits and runtime route
What it is good at
- Compact model relative to general multimodal LLMs.
- Apache 2.0 licence and public official checkpoint.
- Suitable as one stage in a local document-ingestion pipeline.
- Leaves room on a 32GB GPU for preprocessing and bounded parallel work.
Where to be cautious
- OCR accuracy varies with scan quality, language, handwriting, tables and page layout.
- A model loading successfully does not establish pages per minute or extraction quality.
- The official code uses custom model support and version requirements that need pinning.
- Document retention, access and human review remain part of the deployment.
Serving software
- Transformers
- DeepSeek reference code
Before installation
- Benchmark with a labelled sample of the buyer's invoices, contracts, forms or technical documents.
- Record input resolution, page count, batch, latency and peak VRAM.
- Keep OCR output and downstream extraction evaluation separate.
- A 32GB workstation is often enough for a pilot; rack products matter when several workers or large ingestion queues are required.
System requirements
- GPU layout
- The minimum can fit on one GPU; multiple GPUs may still be useful for replicas or throughput.
- Context and cache
- The model context ceiling is not a guaranteed serving target. KV cache, batch size and concurrent sessions need separate capacity tests.
- System RAM
- Size system memory for model loading, runtime overhead, preprocessing and any CPU offload used by the final configuration.
- Storage
- Allow space for the pinned checkpoint, runtime images, caches, logs and at least one rollback version.
- Serving software
- Validate the exact checkpoint with Transformers, DeepSeek reference code before acceptance.
- Representative workload
- Benchmark representative prompts or media at the required context, quality, latency and concurrency.
Performance: speed, usable context and concurrency depend on the selected system, software and workload. No benchmark is quoted on this page.
Commercial and legal boundary
Apache License 2.0
Commercial use: permitted
Apache 2.0 permits commercial use. Document rights and personal-data handling remain separate.
Always retain the applicable notices and recheck the live terms for the intended organisation, territory, use and distribution route. Obtain legal advice where required; the official licence governs use.
Read the official licenceTechnical questions
DeepSeek-OCR 2 deployment FAQ
Does every GPU Servers product fit DeepSeek-OCR 2?
All current products exceed the planning memory floor. The right size depends on page resolution, throughput, queueing and any language model used after OCR.
Is OCR accuracy guaranteed?
No. Accuracy must be measured against representative pages and an agreed field or character-level acceptance method.
Do I need a rack server for OCR?
Not for a small pilot. Rack systems become relevant for multiple workers, sustained ingestion or a combined OCR, embedding and language-model pipeline.
What a complete DeepSeek-OCR 2 deployment needs
GPU memory is only one part of the system. Storage, data access, serving software, monitoring and administrator handover also affect a reliable deployment.
