OLMo 2 0325 32B Instruct — Hardware Requirements & GPU Compatibility
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OLMo 2 0325 32B Instruct is a 32.2B-parameter open language model from Allen AI in the OLMo family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 20.18 GB of VRAM — see which GPUs and Macs can run it below.
94.8K downloads
147 likesSpecifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 32.2B
- Architecture
- Olmo2ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2025-03-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OLMo 2 0325 32B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.5 GB | 15.1 GB | 13.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 14.9 GB | 15.5 GB | 14.10 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.6 GB | 17.1 GB | 15.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 16.9 GB | 17.5 GB | 16.12 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.2 GB | 20.7 GB | 19.34 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 23.8 GB | 24.3 GB | 22.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.4 GB | 28.0 GB | 26.59 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.1 GB | 33.6 GB | 32.23 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run OLMo 2 0325 32B Instruct?
Q4_K_M · 20.2 GBOLMo 2 0325 32B Instruct (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 4K context window can add up to 0.5 GB, bringing total usage to 20.7 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 2 0325 32B Instruct?
Q4_K_M · 20.2 GB41 devices with unified memory can run OLMo 2 0325 32B Instruct, 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 2 0325 32B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does OLMo 2 0325 32B Instruct need?
OLMo 2 0325 32B Instruct requires 20.2 GB of VRAM at Q4_K_M, or 65.3 GB at BF16. Full 4K context adds up to 0.5 GB (20.7 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 ≈ 1.4 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M20.2 GBQ4_K_M + full context20.7 GB- Can NVIDIA GeForce RTX 4090 run OLMo 2 0325 32B Instruct?
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 2 0325 32B Instruct?
For OLMo 2 0325 32B Instruct, Q4_K_M (20.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.0 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 GBQ4_016.9 GBQ4_K_M ★20.2 GBQ5_021.0 GBQ5_K_M23.8 GBBF1665.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OLMo 2 0325 32B Instruct on a Mac?
OLMo 2 0325 32B Instruct 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 2 0325 32B Instruct locally?
Yes — OLMo 2 0325 32B Instruct 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 2 0325 32B Instruct?
At Q4_K_M, OLMo 2 0325 32B Instruct 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 2 0325 32B Instruct?
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 2 0325 32B Instruct?
7 consumer GPUs can run OLMo 2 0325 32B Instruct 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 2 0325 32B Instruct?
41 devices with unified memory can run OLMo 2 0325 32B Instruct 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.