Olmo 3.1 32B Think — Hardware Requirements & GPU Compatibility
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Olmo 3.1 32B Think is a 32.2B-parameter open language model from Allen AI. It supports a context window of up to 65,536 tokens. At BF16 it needs about 65.30 GB of VRAM — see which GPUs and Macs can run it below.
9.3K downloads
87 likesSpecifications
- Publisher
- Allen AI
- Parameters
- 32.2B
- Architecture
- Olmo3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 100,278
- Release Date
- 2026-01-05
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Olmo 3.1 32B Think Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 65.3 GB | 82.0 GB | 64.47 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Olmo 3.1 32B Think?
BF16 · 65.3 GBOlmo 3.1 32B Think (BF16) requires 65.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 85+ GB is recommended. Using the full 66K context window can add up to 16.7 GB, bringing total usage to 82.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Olmo 3.1 32B Think?
BF16 · 65.3 GB5 devices with unified memory can run Olmo 3.1 32B Think, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does Olmo 3.1 32B Think need?
Olmo 3.1 32B Think requires 65.3 GB of VRAM at BF16. Full 66K context adds up to 16.7 GB (82.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.2B × 16 bits ÷ 8 = 64.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 66K context)
VRAM usage by quantization
BF1665.3 GBBF16 + full context82.0 GB- Can NVIDIA GeForce RTX 5090 run Olmo 3.1 32B Think?
No — Olmo 3.1 32B Think requires at least 65.3 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Olmo 3.1 32B Think on a Mac?
Olmo 3.1 32B Think requires at least 65.3 GB at BF16, 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 3.1 32B Think locally?
Yes — Olmo 3.1 32B Think can run locally on consumer hardware. At BF16 quantization it needs 65.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Olmo 3.1 32B Think?
At BF16, Olmo 3.1 32B Think can reach ~45 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 65.3 × 0.55 = ~45 tok/s
Estimated speed at BF16 (65.3 GB)
~45 tok/s~33 tok/s~28 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Olmo 3.1 32B Think?
At BF16, the download is about 64.47 GB.
- Which GPUs can run Olmo 3.1 32B Think?
No single consumer GPU has enough VRAM to run Olmo 3.1 32B Think at BF16 (65.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Olmo 3.1 32B Think?
5 devices with unified memory can run Olmo 3.1 32B Think at BF16 (65.3 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.