Can OLMo 2 13B run on GTX 1080 Ti 11GB?

BARELY — Tight on Memory

B66Good

Estimated from fit model

OLMo 2 13B needs ~12.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 12.4 GB, 21.9 tok/s, Very compromised (needs ~0.9 GB host RAM)

12.4 GB required11.0 GB available

113% VRAM needed

1.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.9 GB host RAM)

Decode

21.9 tok/s

TTFT

8834 ms

Safe context

Memory

12.4 GB / 11.0 GB

Offload

10%

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on GTX 1080 Ti 11GB
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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.1 GB host RAM)	27.5 tok/s	3835 ms	7K
Coding	B	Very compromised (needs ~0.9 GB host RAM)	21.9 tok/s	8834 ms	7K
Agentic Coding	F	Too heavy	14.8 tok/s	19082 ms	7K
Reasoning	B	Very compromised (needs ~0.9 GB host RAM)	21.9 tok/s	10440 ms	7K
RAG	F	Too heavy	14.8 tok/s	23853 ms	7K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A80
Q3_K_S	3	6.4 GB	Low	A79
NVFP4	4	7.3 GB	Medium	A79
Q4_K_MBest for your GPU	4	7.9 GB	Medium	A79
Q5_K_M	5	9.4 GB	High	F0
Q6_K	6	10.7 GB	High	F0
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run OLMo 2 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "allenai/OLMo-2-13B-Instruct" \
  --hf-file "OLMo-2-13B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien OLMo 2 13B

RTX 3060 12GBOpción económica

12 GB VRAM (+1)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.21.9 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.36.7 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 68%.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.30.1 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 37%.

~$499 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run OLMo 2 13B?

Yes, GTX 1080 Ti 11GB can run OLMo 2 13B with a B grade (Very compromised (needs ~0.9 GB host RAM)). Expected decode speed: 21.9 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 12.4 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 13B?

The recommended quantization for OLMo 2 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 13B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, OLMo 2 13B achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8834ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on GTX 1080 Ti 11GB receives a B grade with 21.9 tok/s and 7K context.

What context window can OLMo 2 13B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, OLMo 2 13B can safely use up to 7K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if OLMo 2 13B feels slow on GTX 1080 Ti 11GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for GTX 1080 Ti 11GB See all hardware for OLMo 2 13B

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