Smaller alternatives
Models that need less memory than Olmo 3.1 32B Think and score about as well or better.
Olmo 3.1 32B Think
Allen Institute for AI · 32.2B · Dense
Olmo 3.1 32B Think is a 32.2B model from Allen Institute for AI with a 64K-token context. At Q4_K_M with an 8K context it needs about 19.7 GB; 26 GB leaves room for longer chats.
Quantization options
| Quant | Bits | Memory | Quality | On your hardware |
|---|---|---|---|---|
| Q2_K | 3.16 | — | Noticeably worse | … |
| Q3_K_M | 4 | — | Some loss | … |
| Q4_K_M | 4.89 | — | Good | … |
| Q5_K_M | 5.7 | — | Good | … |
| Q6_K | 6.56 | — | Near original | … |
| Q8_0 | 8.5 | — | Near original | … |
| F16 | 16 | — | Original | … |
Memory by context length
At Q4_K_M. The context cache grows with every token the model keeps in mind.
| Context | Context cache | Total |
|---|---|---|
| 4K | 1 GB | 19.4 GB |
| 8K | 1.3 GB | 19.7 GB |
| 32K | 2.8 GB | 21.2 GB |
Next steps
Compare it with
Scored models of a similar size, side by side on your device.
Least hardware that runs it well
Desktop devices with the least memory that give Olmo 3.1 32B Think grade A or S at Q4_K_M.
Can I run Olmo 3.1 32B Think locally?
Can I run Olmo 3.1 32B Think locally?
Yes, if your GPU or Mac has about 19.7 GB free for it at Q4_K_M. Open this page on that computer to see the grade for your exact hardware, then start it with llama.cpp, Ollama or LM Studio.
How much memory does Olmo 3.1 32B Think need?
About 19.7 GB at Q4_K_M with an 8K context and 33.5 GB at Q8_0. Lower quantizations fit smaller cards with some loss in quality; longer contexts add to the total.