Open-Weight LLM · Private & Custom AI
deepseek-v4-gguf
Quantized MoE text generator optimized for private inference on large-RAM systems; DeepSeek V4 Flash compressed into GGUF for operator-controlled deployment.
DeepSeek-V4-Flash packaged as GGUF quantizations (2-bit and 4-bit variants) by antirez, designed for the ds4 inference engine but compatible with standard GGUF loaders. Built for companies running billion-parameter models entirely on-premises—no API calls, no data egress—with the flexibility to trade speed for VRAM via quantization levels.
Model facts
Private deployment
Run deepseek-v4-gguf in your own environment
Self-hosted on single high-RAM machines (128–512 GB depending on quantization). The q2 variant (80.8 GiB) runs on 128 GB systems; q4 (153.3 GiB) requires ≥256 GB. Deploy via ds4 engine (custom inference framework), or load into llama.cpp / Ollama if GGUF support exists. Data never leaves your infrastructure—ideal for regulated industries, proprietary knowledge, and teams avoiding cloud SaaS lock-in.
Operational AI use cases
Internal Knowledge & Documentation Synthesis
Ingest employee handbooks, process documentation, compliance guides into a private Q&A agent. Route queries through the MoE router; only relevant experts activate, keeping inference cost (tokens/sec) predictable. No risk of proprietary docs leaking to external APIs.
Support Ticket Triage & Routing
Classify and summarize incoming tickets (email, Slack, Zendesk) before human review. Use the model as a pre-filter to extract intent, urgency, and topic. Run entirely on-premises so ticket text and customer metadata never touch third-party infrastructure.
Finance & Operations Reporting
Summarize expense reports, extract key line items, generate executive summaries from sales/operations logs. Large context window (exact size unknown, requires verification) allows ingestion of multi-page documents. Quantized inference keeps per-token cost low; MoE routing means you pay for only the experts needed per query.
Custom AI
As a base for custom AI
Strong foundation for vertical AI products (vertical SaaS, internal tools). The 4-bit variant retains enough signal for instruction-following and reasoning tasks; use it as backbone for fine-tuning on proprietary workflows, workflows specific to finance, HR, or supply-chain ops. Low VRAM footprint (via q2/q4) makes it feasible to add adapter layers or LoRA without exhausting memory.
In the operating system
Where it fits
Knowledge & agent layer. Acts as the reasoning engine in an LLM.co ops-AI stack: ingest documents (RAG layer below), route queries through the router, generate summaries/decisions, and hand off to workflow orchestration (layer above). MoE design means you can scale token throughput without re-deploying a monolithic model.
Data control & security
Deploying on-premises means customer data—tickets, documents, financial records—remains in your network. No telemetry, no model logging by third parties. Does NOT guarantee the model itself is 'secure' or 'compliant'; you inherit responsibility for network isolation, access control, audit logging, and encryption at rest. For PII-heavy workflows (health, finance), add additional data masking/redaction layers before inference.
Hardware footprint
**Estimate (unverified).** q2 variant (~80.8 GiB on disk) likely ~85–95 GiB loaded VRAM for inference + KV cache (batch size 1). q4 variant (~153.3 GiB on disk) ~160–180 GiB VRAM. For larger batches or extended context, add 20–40% overhead. Speculative decoding (MTP model, 3.6 GiB) optional; negligible additional VRAM if enabled.
Integration
Load via ds4 binary + REST API (`ds4-server --ctx 100000 --kv-disk-dir /tmp/ds4-kv`), or integrate via llama.cpp Python/Node bindings if GGUF support is confirmed. Supports extended context (up to 100k tokens via kv-disk offload). Plug into workflow orchestration (n8n, Zapier, custom Python/Go agents) via HTTP POST. For Slack/email workflows, wrap in a job queue (e.g., Celery, Bull) to avoid blocking user interactions.
When it's not the right fit
- —You need sub-100ms latency on every request—MoE routing + quantized experts trade speed for efficiency; expect 50–300 ms per token depending on expert selection and VRAM pressure.
- —Your team lacks infrastructure expertise—private deployment requires managing Linux/Kubernetes, VRAM allocation, kv-disk tuning, and rollout coordination. No managed service or API fallback.
- —You need the original full-precision model behavior—aggressive quantization (q2 especially) will degrade factuality, nuance, and edge-case reasoning vs. native weights.
- —Your context length requirements are unknown—card does not specify max context; verify against base model (DeepSeek-V4-Flash) before committing.
Alternatives to consider
Llama 3.1 70B (GGUF, Meta)
Dense, simpler architecture; fewer dependencies than MoE. Slightly smaller quantized footprint; broader GGUF tooling support. Trade-off: less parameter efficiency, higher per-token cost if running large batches.
Mixtral 8x22B (Mistral, GGUF)
Alternative MoE design; smaller total parameter count; clearer public benchmarks. Lower VRAM bar (fits on 80–128 GB systems unquantized). Trade-off: may be less capable on reasoning tasks than DeepSeek V4.
Qwen2.5 72B (Alibaba, GGUF)
Strong multilingual instruction-following; ample public quantizations; widely tested in ops workflows. Simpler to integrate than MoE. Trade-off: requires larger VRAM for competitive quality vs. q4 DeepSeek V4.
Related open models
FAQ
Can I run this on a cloud instance without sending data to DeepSeek or Hugging Face?
Yes. Download the GGUF weights and the ds4 binary to a private VPC instance (e.g., AWS EC2, on-prem), run inference locally. Weights are distributed via Hugging Face, but once cached, all inference stays in your network. No telemetry home to DeepSeek.
Is commercial/production use of this model permitted?
The GGUF redistributions are MIT-licensed. MIT permits commercial use, modification, and distribution. The underlying base model (DeepSeek-V4-Flash) is copyrighted by DeepSeek; review DeepSeek's original license terms for any restrictions on model output, derivative works, or competitive use. Requires verification—not explicitly stated in this card.
Will the q2 quantization lose too much quality for customer-facing features?
Depends on the use case. Q2 (2-bit routed experts, Q8 projections/router) is aggressive; suitable for classification, summarization, and routing tasks. For open-ended generation or high-stakes reasoning (legal, medical), test against q4 or original weights first. Quality degradation is non-linear across token positions—early tokens often retain high fidelity.
What's the exact context length?
Unknown from this card. Inherited from base model DeepSeek-V4-Flash; check DeepSeek's official documentation. The GGUF server supports up to 100k tokens via kv-disk offload, but the model's native position encoding may limit supported context shorter.
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