Open-Weight LLM · Private & Custom AI
DeepSeek-V3.2-Exp
685B sparse-attention model for long-context private inference—built for ops teams running cost-conscious reasoning workloads and custom AI without cloud dependencies.
DeepSeek-V3.2-Exp is a 685B parameter experimental model introducing DeepSeek Sparse Attention (DSA) to reduce compute and memory overhead in extended-sequence processing while maintaining reasoning parity with V3.1-Terminus. For private-deployment ops teams, it trades inference speed for lower hardware cost and full data control; for custom AI builders, it provides a tuned reasoning backbone for agents, document processing, and multi-turn workflows.
Model facts
Private deployment
Run DeepSeek-V3.2-Exp in your own environment
Self-hosting requires 8–16 H100/H200 GPUs (≈320–640 GB VRAM in FP8) or equivalent multi-GPU cluster. DeepSeek provides inference conversion scripts and SGLang/vLLM support; sparse attention requires recent CUDA/ROCm with FlashMLA kernels for efficiency gains. Deployment keeps all prompts, outputs, and fine-tuning data in your environment—eliminating API egress and vendor lock-in.
Operational AI use cases
Internal Knowledge Agent & Support Automation
Deploy V3.2-Exp as the backbone for a private document-retrieval agent over your knowledge base (policies, runbooks, FAQs). Sparse attention handles long policy documents and conversation history without API costs. Use it to auto-triage support tickets, answer internal HR/IT queries, and reduce frontline agent workload—all with zero data leaving your infrastructure.
Financial & Compliance Document Analysis
Process multi-page contracts, audit reports, and regulatory filings (10k+, 50k tokens+) in-house using sparse attention efficiency. Extract risks, flag compliance gaps, auto-populate audit checklists. Long-context capability makes it practical to summarize entire documents in a single pass without expensive API calls or data residency concerns.
Code Review & DevOps Automation Workflow
Wire V3.2-Exp into your CI/CD pipeline for automated code review, security scanning, and infrastructure-as-code validation. Reason over large codebases and logs in a single context window. Fine-tune on your codebase standards to enforce style, detect anti-patterns, and suggest fixes—all private, all on-premises.
Custom AI
As a base for custom AI
Strong foundation for fine-tuning custom reasoning assistants, specialized agents, and domain-specific multi-turn applications. The sparse attention mechanism reduces fine-tuning cost on long-sequence tasks (legal, scientific, code-heavy domains). Use it as a base for a proprietary customer-support copilot, internal workflow automation agent, or RAG system that must stay within your network.
In the operating system
Where it fits
Occupies the **reasoning & agent layer** in an ops AI stack: grounds long-context retrieval outputs, powers multi-step reasoning in agentic loops, and handles complex instruction-following for custom workflows. Sits between a retrieval/knowledge layer and action/integration layer (APIs, databases, ticketing systems).
Data control & security
Self-hosting means prompts, outputs, and fine-tuning data never transit external APIs—a material risk reduction for regulated industries (finance, healthcare, legal) and competitive workflows. No inference logs sent to DeepSeek servers. This is an **architectural choice**, not a model property: you remain responsible for network segmentation, access control, and data retention policies. MIT license permits internal deployment without legal review, but endpoint security is your responsibility.
Hardware footprint
**Estimate**: FP8 quantization ~320 GB VRAM (8× H100 80GB); FP16 ~640 GB (16× H100); context-dependent spikes possible. Multi-GPU parallelism essential (tensor+pipeline). Inference latency lower than dense 685B models due to sparse attention, but absolute throughput constrained by cluster size and batch size.
Integration
Inference API via vLLM (OpenAI-compatible) or SGLang; plug into existing chat frameworks, RAG pipelines, and agent orchestration tools (LangChain, Llamaindex, CrewAI). Conversion script required to convert HF weights to inference-optimized format. Sparse attention kernels (FlashMLA) need manual build; vLLM provides pre-built support. Requires API wrapper for internal tool-calling and function agents.
When it's not the right fit
- —Latency-critical (<500ms per token required): sparse attention trades speed for efficiency; dense smaller models may be faster.
- —GPU cluster unavailable: 685B requires enterprise hardware; smaller 7B–70B models better for edge/single-GPU deployments.
- —Real-time streaming or sub-second interactive use: inference on 685B, even sparse, not optimized for conversational speed.
- —Niche languages or specialized domains without fine-tuning data: experimental architecture may require tuning investment; Llama or Mistral may have better community playbooks.
Alternatives to consider
Llama 3.1 405B
Denser, more stable reasoning; wider community tooling and quantization; no experimental sparse attention. Requires similar hardware but more predictable performance.
Qwen 2.5 72B
Smaller, fits single high-end GPU; strong reasoning and coding; lower ops overhead for teams without 8–16 GPU clusters.
Mixtral 8x22B
Moderate sparse MoE model; faster inference than dense 685B; lower memory footprint; proven production stability.
Related open models
FAQ
Can we fine-tune DeepSeek-V3.2-Exp on proprietary data and keep it private?
Yes. MIT license permits internal fine-tuning without disclosure. You'd fine-tune on your hardware with your data, and the resulting weights stay in-house. Requires LoRA or full fine-tuning setup on your cluster; no API calls to DeepSeek.
Is this model commercially usable under MIT license?
Yes. MIT is permissive: you can use it for internal commercial purposes, SaaS products, and resale without license restrictions. However, you are responsible for any output liability; always review DeepSeek's terms of service for any hosted endpoints you offer.
How much faster is sparse attention in practice for long documents?
Model card shows sparse attention achieves 'substantial improvements' in long-context training and inference, but specific latency numbers are not published. You should benchmark on your documents (5k–100k tokens) on your hardware to validate cost/latency trade-offs.
What's the context window length?
Not specified in available docs. Check the inference config and HF card; likely 128k+ based on V3.1 lineage, but confirm before deploying long-sequence workflows.
Build a Custom Reasoning AI on Your Terms
DeepSeek-V3.2-Exp is built for companies that need private, long-context reasoning without cloud lock-in. Start building a fine-tuned agent, document workflow, or internal copilot on LLM.co—we handle the infra, you own the model and data.