microsoft

Phi-4-reasoning

14.7B parameters · reasoning · Reasoning · Phi family

Long context — how it uses memory →
mit hash verified source matched revision pinned

Which version should I download?

Default pick: GGUF · Q4_K_M — Balanced size/quality — good default.

Set your rig to confirm it fits and to see if a higher-quality quant runs fully on your hardware.

Set your rig

Quantizations

Why these quants? Which quant should I pick? What do these mean?

GGUF · IQ4_XS

7.40 GB

Set your rig
Est. speed
—
Swarm
2S / 0L webseed OK

Bartowski IQ4_XS quant of Phi-4-reasoning (14.66B dense) — compact low-bit option. MIT.

Magnet .torrent

GGUF · Q4_K_M

8.43 GB

Set your rig
Est. speed
—
Swarm
2S / 0L webseed OK

Balanced size/quality — good default.

Magnet .torrent

GGUF · Q8_0

14.51 GB

Set your rig
Est. speed
—
Swarm
2S / 0L webseed OK

Bartowski Q8_0 quant of Phi-4-reasoning (14.66B dense) — high-quality option. MIT.

Magnet .torrent

Want a different quant? Request it on the board →

Runs fully on

RTX 5090 · Q8_0 RTX 5060 Ti 16GB · Q4_K_M RTX 5070 Ti · Q4_K_M RTX 5080 · Q4_K_M RTX 5070 · Q4_K_M

Green = the model's best quant fits fully in GPU/unified memory at 8K context. Tap a card for its full "what runs on it" page.

Context

Advertised 32k · usable ≈ 32k (reported — model card)

How we know

Evidence grade: reported — a maintainer claim with no independent evaluation.

Card: 32K window. Only FlenQA on a 3K-token subset is published — not a long-context eval.

Reviewed on Jul 18, 2026.

Capabilities (as declared by the maintainer): reasoning

Run it

Runtime completeness (IQ4_XS torrent): llama.cpp ✅ Ollama – vision sidecar –

  • – Ollama — runs the GGUF directly; no Modelfile bundled
  • – vision sidecar — not a vision model

Context / KV 8,192 tokens · FP16 set above the quant table

Generic commands (no rig set). GPU-offload values assume the model fits on your GPU — set your rig for values tuned to your hardware.

Start the server 
llama-server -m microsoft_Phi-4-reasoning-IQ4_XS.gguf -c 8192 -ngl 999

Use llama-cli in place of llama-server for a one-shot prompt.

Save as Modelfile next to the GGUF (Modelfile) 
FROM ./microsoft_Phi-4-reasoning-IQ4_XS.gguf
PARAMETER num_ctx 8192
PARAMETER num_gpu 999
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.8
PARAMETER top_p 0.95
PARAMETER top_k 50
# Chat template: Ollama uses the template embedded in the GGUF (no TEMPLATE directive needed).
Create 
ollama create microsoft-phi-4-reasoning -f Modelfile
Run 
ollama run microsoft-phi-4-reasoning

GGUF is not a first-class vLLM format.

vLLM GGUF support is experimental and single-file only; prefer safetensors/GPTQ/AWQ for production. If you must, pass the .gguf path to `vllm serve` with --load-format gguf on a recent vLLM.

transformers does not load GGUF weights.

GGUF weights run under llama.cpp, Ollama or LM Studio.

Load microsoft_Phi-4-reasoning-IQ4_XS.gguf, set the context length to 8192 tokens. Set GPU offload to Max (all layers).

MLX runs MLX-format weights only (Apple Silicon). This quant is a GGUF build.

GGUF weights run under llama.cpp, Ollama or LM Studio.

Technical details

Chat template

<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>'}}{% generation %}{{message['content'] + '<|im_end|>'}}{% endgeneration %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}

Sampling defaults

top_k
50
top_p
0.95
temperature
0.8

Stop strings

<|im_end|>

Evidence & provenance

Source

Provenance
https://huggingface.co/microsoft/Phi-4-reasoning
Revision pin
1de18ec97600877ce63dbf60c73b998da99f0195
Manifest
Present

License

Name
mit
Commercial use
yes
Access
Open

How verification works →

File hashes (SHA-256)

  • phi-4-reasoning-iq4-xs/LICENSE 7df20dcdf9197e9945c14858d41c60f11b52b93e5b69e2b63416b874d598d322
  • phi-4-reasoning-iq4-xs/README.md 491358fd500902186dd75edcacf03c5eb70242238be399470ad1adb1cd77a9d3
  • phi-4-reasoning-iq4-xs/microsoft_Phi-4-reasoning-IQ4_XS.gguf 57f29567615d82394905088d4b29bc399af0e9919ef7de00268e27b3aec55b77
  • phi-4-reasoning-q4-k-m/LICENSE 7df20dcdf9197e9945c14858d41c60f11b52b93e5b69e2b63416b874d598d322
  • phi-4-reasoning-q4-k-m/README.md 491358fd500902186dd75edcacf03c5eb70242238be399470ad1adb1cd77a9d3
  • phi-4-reasoning-q4-k-m/phi-4-reasoning-Q4_K_M.gguf 960d3870b218f91116c55bf81dc313e6cdbce31b1047bb2bc8bc7ea47899b032
  • phi-4-reasoning-q8-0/LICENSE 7df20dcdf9197e9945c14858d41c60f11b52b93e5b69e2b63416b874d598d322
  • phi-4-reasoning-q8-0/README.md 491358fd500902186dd75edcacf03c5eb70242238be399470ad1adb1cd77a9d3
  • phi-4-reasoning-q8-0/microsoft_Phi-4-reasoning-Q8_0.gguf 2be359aae4671d8233b3ae120539070d94be47f0cd3b2573976026ee640fa1ea

Explore further

Appears in

What runs on 12 GB of VRAM What runs on 16 GB of VRAM What runs on a 16 GB Mac What runs on a 24 GB Mac Coding models that fit 12 GB Writing models that fit 12 GB

Also consider

Get an email when a better quant fits your rig.

Set your rig once; we'll alert you when a new or better-fitting build lands.

Set your rig

Performance reports

Real-world throughput reported by the community (and scraped sources).

View performance leaderboard
Community

Reviews, sampler presets and community runtime reports. Be the first to contribute.

Reviews (0)

Log in to write a structured review of this model.

No reviews yet.

Sampler presets

Log in to share a sampler preset or vote on presets.

No presets yet.

Community runtime reports

Unofficial, community-submitted "it loads for me" reports. These are not the official verified-working badge — an admin reviews reports before anything is marked officially verified.

Log in to report whether a quant loads in your runtime.

No community reports yet.

selected to compare · pick at least 2

Compare ()