Olmo 3 32B Think SFT — Hardware Requirements & GPU Compatibility
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Olmo 3 32B Think SFT is a 32.2B-parameter open language model from Allen AI in the OLMo family. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 20.18 GB of VRAM — see which GPUs and Macs can run it below.
43.0K downloads
4 likesSpecifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 32.2B
- Architecture
- Olmo3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 100,278
- Release Date
- 2025-11-14
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Olmo 3 32B Think SFT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.5 GB | 31.2 GB | 13.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 14.9 GB | 31.6 GB | 14.10 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.6 GB | 33.2 GB | 15.71 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 20.2 GB | 36.8 GB | 19.34 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 23.8 GB | 40.5 GB | 22.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.4 GB | 44.1 GB | 26.59 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.1 GB | 49.7 GB | 32.23 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Olmo 3 32B Think SFT?
Q4_K_M · 20.2 GBOlmo 3 32B Think SFT (Q4_K_M) requires 20.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 66K context window can add up to 16.6 GB, bringing total usage to 36.8 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Olmo 3 32B Think SFT?
Q4_K_M · 20.2 GB41 devices with unified memory can run Olmo 3 32B Think SFT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Olmo 3 32B Think SFT
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Olmo 3 32B Think SFT need?
Olmo 3 32B Think SFT requires 20.2 GB of VRAM at Q4_K_M, or 65.3 GB at BF16. Full 66K context adds up to 16.6 GB (36.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.2B × 4.8 bits ÷ 8 = 19.3 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M20.2 GBQ4_K_M + full context36.8 GB- Can NVIDIA GeForce RTX 4090 run Olmo 3 32B Think SFT?
Yes, at Q5_K_M (23.8 GB) or lower. Higher quantizations like Q6_K (27.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Olmo 3 32B Think SFT?
For Olmo 3 32B Think SFT, Q4_K_M (20.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (23 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.5 GB.
VRAM requirement by quantization
Q2_K14.5 GBQ3_K_L17.4 GBQ4_K_M ★20.2 GBQ5_K_S23.0 GBQ5_K_M23.8 GBBF1665.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Olmo 3 32B Think SFT on a Mac?
Olmo 3 32B Think SFT requires at least 14.5 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Olmo 3 32B Think SFT locally?
Yes — Olmo 3 32B Think SFT can run locally on consumer hardware. At Q4_K_M quantization it needs 20.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Olmo 3 32B Think SFT?
At Q4_K_M, Olmo 3 32B Think SFT can reach ~238 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~33 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 20.2 × 0.65 = ~258 tok/s
Estimated speed at Q4_K_M (20.2 GB)
~258 tok/s~33 tok/s~258 tok/s~238 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Olmo 3 32B Think SFT?
At Q4_K_M, the download is about 19.34 GB. The full-precision BF16 version is 64.47 GB. The smallest option (Q2_K) is 13.70 GB.
- Which GPUs can run Olmo 3 32B Think SFT?
7 consumer GPUs can run Olmo 3 32B Think SFT at Q4_K_M (20.2 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Olmo 3 32B Think SFT?
41 devices with unified memory can run Olmo 3 32B Think SFT at Q4_K_M (20.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.