text model · OLMo · Windows

Can I run OLMo 2 32B Instruct on Nvidia GeForce RTX 4090 (24GB)?

Compatibility verdict VRAM threshold engine

Yes, but tight GPU accelerated ~34 tok/s est.

Yes. OLMo 2 32B Instruct runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~21.7 GB of ~23 GB usable).

Needs ~21.7 GB Device usable ~23 GB

Fits at Q4_K_M (~21.7 GB of ~23 GB usable) but with little headroom. Close other apps; a smaller context frees a few hundred MB.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4090 (24GB) leaves ~1.3 GB of headroom.

Q4_K_M needed: ~21.7 GB
Usable on device: ~23 GB
Device memory: 24 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~23 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~21.7 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~36.5 GB

FP16

~66.7 GB

The line marks Nvidia GeForce RTX 4090 (24GB)'s ~23 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~450 W
Electricity / 1M tokens: ~$0.55

At ~$0.15/kWh and the estimated ~34 tok/s, a million generated tokens costs about $0.55 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf unsloth/OLMo-2-0325-32B-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/OLMo-2-0325-32B-Instruct-GGUF

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model OLMo

Parameters: 32B
Q4_K_M size: 19.5 GB
Q8_0 size: 34.3 GB
Context: 4k

Full OLMo 2 32B Instruct requirements →

Device Windows

Memory: 24 GB vram
Usable for weights: ~23 GB
Power draw: ~450 W
Best runtime: vLLM (Linux) / Ollama (CUDA)

Best models for Nvidia GeForce RTX 4090 (24GB) →

You could also run

Run OLMo 2 32B Instruct on other hardware

Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)Apple M5 Pro (48GB)

FAQ

Can Nvidia GeForce RTX 4090 (24GB) run OLMo 2 32B Instruct?

Yes. OLMo 2 32B Instruct runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~21.7 GB of ~23 GB usable).

How much memory does OLMo 2 32B Instruct need?

It is a tight fit on Nvidia GeForce RTX 4090 (24GB). At Q4_K_M the weights are ~19.5 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context.

What is the best tool to run OLMo 2 32B Instruct on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Markdown

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.