Can OLMo 2 13B run on RTX 3080 10GB?

Q: What should I upgrade first if OLMo 2 13B feels slow on RTX 3080 10GB?

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.

YES — With NVFP4

B69Good

Estimated from fit model

OLMo 2 13B needs ~11.6 GB VRAM. RTX 3080 10GB has 10.0 GB. With NVFP4 quantization, expect ~48 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.

OLMo 2 13B at Q4_K_M needs 12.3 GB — too much for RTX 3080 10GB (10.0 GB). Runs at NVFP4 (11.6 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.3 GB, exceeds 10.0 GB available

12.3 GB required10.0 GB available

123% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

37.6 tok/s

TTFT

5152 ms

Safe context

Memory

12.3 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsOLMo 2 13B on RTX 3080 10GB
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: 37.6 tok/s decode · 5.2s TTFT (warm) · 94 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.

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 1.0 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	B	Very compromised (needs ~0.8 GB host RAM)	46.9 tok/s	2254 ms	4K
Coding	F	Too heavy	35.5 tok/s	5453 ms	4K
Agentic Coding	F	Too heavy	25.6 tok/s	10979 ms	4K
Reasoning	F	Too heavy	37.6 tok/s	6089 ms	4K
RAG	F	Too heavy	25.6 tok/s	13724 ms	4K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A80
Q3_K_SBest for your GPU	3	6.4 GB	Low	A79

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

Upgrade options

Hardware that runs OLMo 2 13B well

RTX 3060 12GBBudget pick

12 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.36.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.30.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run OLMo 2 13B?

Yes, RTX 3080 10GB can run OLMo 2 13B at NVFP4 quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q4_K_M requires 12.3 GB which exceeds available memory, but at NVFP4 it needs only 11.6 GB. Expected decode speed: 48.2 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 12.3 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at NVFP4 using 11.6 GB.

What is the best quantization for OLMo 2 13B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is NVFP4, which uses 11.6 GB.

What speed will OLMo 2 13B run at on RTX 3080 10GB?

On RTX 3080 10GB, OLMo 2 13B achieves approximately 48.2 tokens per second decode speed with a time-to-first-token of 4017ms using NVFP4 quantization.

Can RTX 3080 10GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on RTX 3080 10GB receives a F grade with 35.5 tok/s and 4K context.

What context window can OLMo 2 13B use on RTX 3080 10GB?

On RTX 3080 10GB, OLMo 2 13B can safely use up to 5K tokens of context at NVFP4 quantization. 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 RTX 3080 10GB?

See all results for RTX 3080 10GB See all hardware for OLMo 2 13B

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