Open-Weight LLM · Private & Custom AI
phi-4
14B dense transformer for reasoning, math, and code—sized for latency-bound ops automation and private deployment without sacrificing capability.
Phi-4 is Microsoft's 14B parameter model trained on 9.8T tokens of synthetic, filtered public, and acquired academic data, optimized for instruction-following and reasoning. For ops teams, it's a self-contained, MIT-licensed model that runs on modest GPU clusters and excels at structured tasks (data processing, code generation, logical workflows) while staying entirely within your infrastructure.
Model facts
Private deployment
Run phi-4 in your own environment
Phi-4 runs on a single H100-80G in float16 (~29 GB VRAM) or quantized to 8-bit (~15 GB), making it viable for on-premise or VPC deployment. The MIT license permits redistribution and embedding in closed systems; no vendor lock-in or usage reporting. Companies deploy via vLLM, TGI, or Ollama to integrate with internal APIs, knowledge stores, and workflows—data never leaves your boundary.
Operational AI use cases
Internal documentation and knowledge automation
Index company wikis, SOPs, and past tickets into a vector store; use Phi-4 as the backbone of a private chatbot that answers employee questions, auto-drafts responses to recurring support tickets, and grounds answers in your own knowledge base. Avoids sharing internal data with external APIs.
Code and SQL generation for data ops
Phi-4 shows strong HumanEval (82.6) and MATH (80.4) scores. Use it to auto-generate SQL queries, ETL scripts, or transform logic from natural language specs in data pipelines, or help junior engineers write boilerplate—all within your own environment.
Structured extraction and process automation
Parse unstructured documents (invoices, contracts, emails), extract key fields into structured JSON, and feed into downstream RPA or workflow systems. Phi-4's instruction-following and reasoning capabilities handle complex conditionals and entity recognition without external service calls.
Custom AI
As a base for custom AI
Phi-4 is a strong foundation for custom vertical AI applications: fine-tune it on domain-specific corpora (legal, medical, financial) or use it as-is with retrieval-augmented generation (RAG) to build customer-facing copilots, agent frameworks, or specialized assistants. Its 16K context window accommodates multi-turn workflows and document-heavy reasoning; relatively small size means you can iterate and serve many concurrent users on moderate hardware.
In the operating system
Where it fits
In an AI OS, Phi-4 anchors the **agent and workflow layers**—use it as the reasoning/planning engine for autonomous tasks, or wrap it in an agent framework (LangChain, CrewAI) to orchestrate multi-step ops processes. Pair with a vector DB (knowledge layer) and operational APIs (execution layer) to automate departmental workflows end-to-end.
Data control & security
Self-hosting Phi-4 ensures data never transits third-party servers—prompts and responses stay in your VPC or on-prem cluster. You control all model weights, outputs, and fine-tuning artifacts. This is a significant advantage for regulated industries (finance, healthcare) and companies with strict data residency requirements. The model itself makes no security guarantees; standard model poisoning and prompt-injection risks apply—you remain responsible for input validation, output auditing, and access controls.
Hardware footprint
**Estimate (float16, batch-size 1):** ~29 GB VRAM (1× H100-80G or 2× A100-40G). **int8 quantized:** ~15 GB. **int4 (GPTQ/AWQ):** ~5–8 GB (accuracy trade-off). Training requires 1920× H100-80G and 21 days; inference scales from a single GPU to multi-GPU for throughput.
Integration
Phi-4 integrates via OpenAI-compatible endpoints (vLLM, TGI) for drop-in LLM API compatibility. Embed it in agentic workflows (AutoGen, CrewAI, LangChain); connect to enterprise data sources (Salesforce, Databricks, Snowflake) via custom retrieval modules; orchestrate with task queues (Celery, Temporal) for async ops workloads. Expects text input and chat-format prompts; outputs raw text requiring post-processing for structured downstream tasks.
When it's not the right fit
- —You need the absolute highest factual recall: SimpleQA score (3.0) lags far behind GPT-4o (39.4); Phi-4 is better suited for reasoning and generation than knowledge lookup.
- —Your use case demands multilingual support: only 8% of training data is non-English; performance on non-English tasks is not documented and likely weaker.
- —You require very long context: 16K tokens covers most ops workflows but may be insufficient for multi-document analysis or very long codebases.
- —You need the latest real-time information: static model with a June 2024 data cutoff; no knowledge of events after that date without external retrieval.
Alternatives to consider
Llama 3.3 (70B)
Stronger overall reasoning (DROP 90.2, MMLU 86.3) and slightly better code (HumanEval 78.9), but requires ~140 GB VRAM (float16) and Llama 2 license (permissive for research/commercial). Overkill for latency-sensitive ops if Phi-4 suffices.
Qwen 2.5 (14B)
Comparable size (14B), strong math/reasoning (MATH 75.6, MGSM 79.6), and slightly better on DROP (85.5). Qwen license is permissive. Trade-off: Phi-4 edges out on reasoning (GPQA 56.1 vs. 42.9) and math (MATH 80.4 vs. 75.6).
Phi-3 (14B or smaller variants)
Phi-4's predecessor; lighter footprint and proven in production, but lower reasoning benchmarks (GPQA 31.2, MATH 44.6). Use if hardware is extremely constrained; otherwise Phi-4 is the better choice.
FAQ
Can I fine-tune Phi-4 on proprietary company data and keep the result private?
Yes. MIT license permits redistribution and modification. Fine-tune on-premise using standard LoRA or full-weight methods; the fine-tuned model and weights remain your property and never leave your environment. No reporting or permissions required.
Is Phi-4 safe to deploy in production without content filtering?
Phi-4 underwent DPO safety alignment and red-teaming, but no model is jailbreak-proof. Deploy with input validation, output filtering (e.g., content-policy checks), and monitoring. Safety is a shared responsibility; treat it like any production ML system.
Can I use Phi-4 for a commercial product?
Yes. MIT license permits commercial use, modification, and distribution. You may embed Phi-4 in a SaaS product, closed-source application, or commercial service without royalties or attribution (though attribution is courteous). Review Microsoft's Phi-4 technical report for any disclaimers, but the license itself is clear.
What's the latency for Phi-4 on a single GPU?
Unknown from public data. Expect 50–200 ms per token depending on hardware, quantization, and batch size. Benchmark on your target GPU and use quantization (int4/int8) to hit latency targets for real-time ops workflows.
Ready to automate ops with your own private AI?
Build a custom Phi-4 deployment with LLM.co. We help you integrate private LLMs into your workflows—no data in the cloud, full control over your model and outputs. Start with your use case.