Olmo 3.1 32B Think on AMD Radeon RX 7900 XTX
Can the AMD Radeon RX 7900 XTX run Olmo 3.1 32B Think?
Yes, with little memory to spare
Tight fit. At Q4_K_M it needs about 22.2 GB of 24.0 GB usable, with an estimated speed of 31.5 tok/s.
- Best quant
Q4_K_M- Generation speed
- 31.5 tok/s
- Prompt processing
- 2,362 tok/s
- First token (1K prompt)
- 0.4 s
- Largest safe context
- 27K
Download Olmo 3.1 32B Think
Recommended for the AMD Radeon RX 7900 XTX: Q4_K_M. Every other quantization has its own download link in the table below.
Quickest · Ollama
One command downloads a ready-to-run version and starts a chat.
ollama run olmo-3.1:32b-think-q4_K_MQuantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the Q4_K_M file (opens in a new tab)
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:Q4_K_MOriginal 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)
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/OLMoHow the A grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 32.5 / 45 | |
|---|---|---|
| Memory fit | 24.0 / 25 | |
| Speed | 13.3 / 15 | |
| Memory use | 15.0 / 15 | |
| Total | 84.8 / 100 | 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 |
Every quantization on the AMD Radeon RX 7900 XTX
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 15.4 GB | 24.0 GB | 51.7 tok/s | Runs well | 78 ptsGreat (A) · 78 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.8/15 | GGUF (opens in a new tab) |
Q3_K_M | 18.3 GB | 24.0 GB | 40.7 tok/s | Runs well | 83 ptsGreat (A) · 83 of 100 points: Model quality 28.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q4_K_M | 22.2 GB | 24.0 GB | 31.5 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 | GGUF (opens in a new tab) |
Q5_K_M | 25.4 GB | 24.0 GB | 15.2 tok/s | Runs with offload | 67 ptsGood (B) · 67 of 100 points: Model quality 33.2/45, Memory fit 10.0/25, Speed 9.3/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q6_K | 29.0 GB | 24.0 GB | 7.0 tok/s | Runs with offload | 65 ptsGood (B) · 65 of 100 points: Model quality 33.9/45, Memory fit 10.0/25, Speed 6.3/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q8_0 | 36.7 GB | 24.0 GB | 3.1 tok/s | Heavy offload | 57 ptsUsable (C) · 57 of 100 points: Model quality 34.2/45, Memory fit 4.0/25, Speed 4.1/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
F16 | 67.0 GB | 24.0 GB | — | Won't run | 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 | GGUF · folder (opens in a new tab) Original weights (opens in a new tab) |
Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.
Other hardware that runs it
- 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
- 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
- 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
- 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
- 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)
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.