DeepSeek OCR 2
High-accuracy OCR model for extracting text from documents, screenshots, receipts, and natural scenes
DeepSeek OCR 2
High-accuracy OCR model for extracting text from documents, screenshots, receipts, and natural scenes
Hardware & Execution ParametersMultimodal
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of DeepSeek OCR 2 by DeepSeek, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
Employs Multi-Head Latent Attention (MLA) with a 512-dim compressed KV vector and DeepSeekMoE (256 routed experts, 8 active, 1 shared) for exceptional sparsity and low memory overhead.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding. GRPO training enables robust self-verification.
LLM Hardware Sizing & Serving
Serving DeepSeek MoE optimally requires vLLM or SGLang with tensor parallelism. MLA reduces KV cache size heavily, freeing up VRAM for huge batch sizes during serving.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying DeepSeek OCR 2.
Key Architectural Strengths
- Multi-Head Latent Attention (MLA) with 512-dim compressed KV vector reduces cache footprint dramatically.
- DeepSeekMoE (256 routed experts, 8 active) and GRPO optimize reasoning performance per watt.
Operational Considerations
- Complex MoE topology demands sophisticated all-to-all communication primitives across GPU clusters.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for DeepSeek OCR 2.
python3 -m vllm.entrypoints.openai.api_server --model DeepSeek OCR 2 --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run deepseek ocr 2python3 -m sglang.launch_server --model-path DeepSeek OCR 2 --tp 1 --trust-remote-codetext-generation-launcher --model-id DeepSeek OCR 2 --num-shard 1 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~0.0 GB VRAM required
~0.0 GB VRAM (Hopper speedup)
~0.0 GB VRAM
CPU RAM / Apple Silicon optimized
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Deployment Tier | Pricing Structure |
|---|---|
| Open Checkpoint Weights | $0.00 (Free Download) |
| Inference Token Consumption | $0.00 / Token |
Comparable Foundation Architectures
Alternative models in the Multimodal class with similar capabilities, context windows, or deployment profiles.
Llama Nemotron Rerank VL 1B v2
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("DEEPSEEK_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="deepseek-deepseek-ocr-2",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about DeepSeek OCR 2
Essential facts, architectural specs, hardware constraints, and pricing answers for DeepSeek OCR 2.
To run DeepSeek OCR 2 (Open Weights) locally, you generally need Depends on quantization. We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for DeepSeek OCR 2 are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.