Reasoning

Check exact model cardOpen weights where releasedUpdated August 2026

Phi-3.5 MoE

Phi-3.5 MoE is a Phi-family model useful for small language model, edge, and low-resource workflow evaluation.

Microsoft · Phi

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (microsoft/Phi-3.5-MoE-instruct)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Efficient reasoning model comparisons

Who should use it

  • Efficient reasoning model comparisons
  • Builders who want local or self-hosted testing options.

Common workflows

  • Efficient reasoning model comparisons
  • small workflows
  • edge workflows
  • local workflows
  • efficient workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Phi workflows.
  • Efficient reasoning model comparisons
  • Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: 131,072 tokens.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Sources to verify

Hugging Face model card (microsoft/Phi-3.5-MoE-instruct)

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Phi family hubModel selection guideFrontier vs open-weight contextLocal coding assistant stackAI tools directory
Hardware~25.4 GB at Q4_K_M (41.9B total parameters; 2 of 16 experts active per token)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext131,072 tokensLast updated2026
Hugging Face model card (microsoft/Phi-3.5-MoE-instruct) →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What can 12GB VRAM run? →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Phi family hub →Phi-3.5 Vision →Phi-3 Medium →Phi-3 Mini →Phi-4 →Qwen2.5 Math 72B →Phi-3 Medium 128K Instruct →

Guides, stacks, and comparisons

Model selection guide →Frontier vs open-weight context →Local coding assistant stack →All model comparisons →

Related models

Phi-3.5 VisionSmall vision-language experimentsPhi-3 MediumLocal assistant, reasoning, and long-context RAG workflows on 12–16 GB VRAM GPUs (RTX 3080/4070/4080). Good step up from 7B without requiring multi-GPU.Phi-3 MiniLocal assistant workflows on 4–8 GB VRAM consumer GPUs, on-device inference, pipeline-embedded classification, and RAG retrieval on constrained hardware.Phi-4Small language model reasoning and assistant workflowsQwen2.5 Math 72BMath-heavy prompts, reasoning tests, and educational workflow evaluation.Phi-3 Medium 128K Instruct14B open-weight model in the Phi family.

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