Can OLMo 2 13B run on RTX 4000 Ada 20GB?

YES — Runs Great

A80Great

Estimated from fit model

OLMo 2 13B needs ~13.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 13.3 GB, 38.2 tok/s, Runs well

13.3 GB required20.0 GB available

67% VRAM used

Fit status

Runs well

Decode

38.2 tok/s

TTFT

5062 ms

Safe context

33K

Memory

13.3 GB / 20.0 GB

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on RTX 4000 Ada 20GB
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: 38.2 tok/s decode · 5.1s TTFT (warm) · 96 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	38.2 tok/s	2761 ms	33K
Coding	A	Runs well	38.2 tok/s	5062 ms	33K
Agentic Coding	A	Runs well	38.2 tok/s	7364 ms	33K
Reasoning	A	Runs well	38.2 tok/s	5983 ms	33K
RAG	A	Runs well	35.4 tok/s	9941 ms	33K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A74
Q3_K_S	3	6.4 GB	Low	A75
NVFP4	4	7.3 GB	Medium	A75
Q4_K_M	4	7.9 GB	Medium	A76
Q5_K_M	5	9.4 GB	High	A77
Q6_K	6	10.7 GB	High	A78
Q8_0Best for your GPU	8	13.9 GB	Very High	A77
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

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s
Qwen 3.6 27B	27B	S	10.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	25.3 tok/s
Magistral Small 2507	24B	S	20.6 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run OLMo 2 13B?

Yes, RTX 4000 Ada 20GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 38.2 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 13.3 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 RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, OLMo 2 13B achieves approximately 38.2 tokens per second decode speed with a time-to-first-token of 5062ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on RTX 4000 Ada 20GB receives a A grade with 38.2 tok/s and 33K context.

What context window can OLMo 2 13B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, OLMo 2 13B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for OLMo 2 13B

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