Can OLMo 2 7B run on GTX 1080 8GB?

YES — With Offload

A72Great

○Estimated from fit model

OLMo 2 7B needs ~7.9 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.9 GB, 47.5 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

47.5 tok/s

TTFT

4073 ms

Safe context

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsOLMo 2 7B on GTX 1080 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	47.5 tok/s	2222 ms	4K
Coding	A	Runs with offload	47.5 tok/s	4073 ms	4K
Agentic Coding	F	Too heavy	21.8 tok/s	12901 ms	4K
Reasoning	A	Runs with offload	47.5 tok/s	4814 ms	4K
RAG	F	Too heavy	21.8 tok/s	16126 ms	4K

Quantization options

How OLMo 2 7B (7B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4	3.9 GB	Medium	A74
Q4_K_M	4	4.3 GB	Medium	A74
Q5_K_MBest for your GPU	5	5.0 GB	High	A73
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run OLMo 2 7B on your machine.

Run

ollama run olmo2:7b

Your hardware

More models your GTX 1080 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	A	19 tok/s
👁 Alibaba Qwen 3 8B	8B	A	24.7 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	26.3 tok/s
👁 InternLM InternVL2 8B	8B	A	26.3 tok/s
👁 Mistral Ministral 3 8B	8B	A	24.7 tok/s

Frequently asked questions

See all results for GTX 1080 8GB See all hardware for OLMo 2 7B

URL: https://willitrunai.com/can-run/olmo-2-7b-on-gtx-1080-8gb