Phi-4

Phi-4 is useful to evaluate because it combines a specific architecture, license and operating envelope rather than simply adding another row to a model leaderboard.

Microsoft reports 9.8T training tokens and 21 days of training on 1,920 H100-80G GPUs. The data mixture emphasizes synthetic datasets, filtered public-domain web material and acquired academic books/Q&A. Alignment includes supervised fine-tuning and direct preference optimization.

From an infrastructure perspective, the key sizing facts are 14B total parameters, 14B active parameters and 16K tokens of context. These values should be read together: weight memory, active compute and KV-cache growth describe different resource constraints.

From a governance perspective, the checkpoint is distributed under MIT License. OpenWeightModels records that independently from the fact that the weights are downloadable, because “open weight” is an access classification—not a universal statement about commercial, redistribution or derivative-work rights.

Phi-4 is a dense model: its stated 14B parameter count is much closer to the parameter set participating throughout inference than in a sparse MoE system. That makes raw weight-memory planning more straightforward, although precision, context, KV cache, batch size and runtime overhead still materially change the real deployment envelope.

For dense models, quantization usually provides the clearest path to lower hardware requirements. Long context can nevertheless dominate runtime memory even when the checkpoint itself is comparatively compact.

Publisher-documented architecture details for this checkpoint include the elements below. These details are more useful for deployment planning than a parameter count alone because attention layout, expert routing, modality encoders and context design can affect throughput and memory independently.

14B dense parameters make Phi-4 far easier to host than frontier-scale models. BF16 remains a high-memory single-GPU or small multi-GPU target; quantization can bring it to consumer-class hardware. The 16K context limits cache growth relative to 128K models.

Real memory use includes model weights, KV cache, activations/buffers, multimodal encoders when applicable, framework overhead and batching. A published “fits on” statement is meaningful only together with precision, context, batch and host/GPU topology.

Phi-4 is most relevant when the application specifically values reasoning, mathematics, code and latency/compute-constrained text applications. That should be balanced against its hardware class, context behavior, license and modality requirements.

A smaller model can be operationally superior when latency, privacy, device deployment or predictable cost matter more than peak benchmark capability. Conversely, a larger sparse model may justify its complexity when the workload benefits from higher capacity, sophisticated reasoning or agent behavior.

The selection decision should therefore compare at least five dimensions: required task quality, data/control requirements, legal eligibility, serving cost and ecosystem/runtime support. OpenWeightModels exposes these dimensions separately so a model is not selected on benchmark reputation alone.

Last full review: 29 September 2026. Publisher model cards, repositories and license texts are preferred. Runtime claims are attached to the relevant runtime/provider rather than inferred from the base checkpoint.

Where documentation conflicts or a field is ambiguous, the passport uses the more conservative interpretation and explains the discrepancy instead of silently selecting the largest number.