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LLMs ·

Olmo 3.1 32B Think

Parameters
32B
Architecture
Dense
Max context
64K tokens
License
apache-2.0
Released
2025-12

How to run it

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run olmo-3.1:32b-think

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/unsloth/Olmo-3.1-32B-Think-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Olmo-3.1-32B-Think-GGUF

Original weights · Hugging Face

Full-precision files from Ai2, for fine-tuning, vLLM, Transformers.

huggingface.co/allenai/Olmo-3.1-32B-Think (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ai2. The weights themselves download from Hugging Face.

github.com/allenai/OLMo (opens in a new tab)

git clone https://github.com/allenai/OLMo

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps.

KV cache: sliding-window hybrid: the KV estimate counts the global-attention layers plus a small allowance for the sliding-window layers.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 12.9 GB 14.2 GB 16.4 GB GGUF (opens in a new tab)
Q3_K_M Low 15.8 GB 17.1 GB 19.3 GB GGUF (opens in a new tab)
Q4_K_M Good 19.7 GB 21.0 GB 23.2 GB GGUF (opens in a new tab)
Q5_K_M High 22.9 GB 24.2 GB 26.4 GB GGUF (opens in a new tab)
Q6_K High 26.4 GB 27.8 GB 30.0 GB GGUF (opens in a new tab)
Q8_0 Very high 34.2 GB 35.5 GB 37.7 GB GGUF (opens in a new tab)
F16 Full 64.5 GB 65.8 GB 68.0 GB GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Olmo 3.1 32B Think

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7600 XT (launch price $329) at Q2_K.

Grade · points Hardware Quant Memory Speed Fit
88 ptsExcellent (S) · 88 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q5_K_M 25.8 GB / 32.0 GB 56.4 tok/s Runs well
86 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.7/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q4_K_M 22.2 GB / 24.0 GB 38.4 tok/s Tight fit
85 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q4_K_M 22.2 GB / 24.0 GB 35.6 tok/s Tight fit
84 ptsGreat (A) · 84 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 13.3/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q4_K_M 22.2 GB / 24.0 GB 31.5 tok/s Tight fit
81 ptsGreat (A) · 81 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 NVIDIA RTX PRO 6000 Blackwell Q6_K 32.6 GB / 96.0 GB 47.4 tok/s Runs well
79 ptsGreat (A) · 79 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 6.5/15, Memory use 15.0/15 Apple M4 Pro (48 GB) Q5_K_M 27.1 GB / 36.0 GB 7.4 tok/s Runs well
79 ptsGreat (A) · 79 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 10.0/15, Memory use 11.9/15 Apple M4 Max (64 GB) Q4_K_M 24.8 GB / 48.0 GB 17.9 tok/s Runs well
78 ptsGreat (A) · 78 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 8.6/15, Memory use 11.9/15 Apple M1 Max (64 GB) Q4_K_M 24.8 GB / 48.0 GB 13.1 tok/s Runs well
77 ptsGreat (A) · 77 of 100 points: Model quality 23.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER Q2_K 15.0 GB / 16.0 GB 42.0 tok/s Tight fit
77 ptsGreat (A) · 77 of 100 points: Model quality 23.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q2_K 15.0 GB / 16.0 GB 46.0 tok/s Tight fit

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.