Can OLMo 2 13B run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

A80Great

○Estimated from fit model

OLMo 2 13B needs ~13.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~77 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 13.7 GB, 76.9 tok/s, Runs well

13.7 GB required24.0 GB available

57% VRAM used

Fit status

Runs well

Decode

76.9 tok/s

TTFT

2518 ms

Safe context

33K

Memory

13.7 GB / 24.0 GB

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on RTX PRO 4000 Blackwell 24GB
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: 76.9 tok/s decode · 2.5s TTFT (warm) · 192 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	76.9 tok/s	1374 ms	33K
Coding	A	Runs well	76.9 tok/s	2518 ms	33K
Agentic Coding	A	Runs well	76.9 tok/s	3663 ms	33K
Reasoning	A	Runs well	76.9 tok/s	2976 ms	33K
RAG	A	Runs well	76.9 tok/s	4579 ms	33K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A72
Q3_K_S	3	6.4 GB	Low	A73
NVFP4	4	7.3 GB	Medium	A74
Q4_K_M	4	7.9 GB	Medium	A74
Q5_K_M	5	9.4 GB	High	A75
Q6_K	6	10.7 GB	High	A76
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 PRO 4000 Blackwell 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	85.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	37 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	28.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	A	37.9 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	88.3 tok/s

Frequently asked questions

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for OLMo 2 13B

URL: https://willitrunai.com/can-run/olmo-2-13b-on-rtx-pro-4000-blackwell-24gb

⇱ OLMo 2 13B on RTX PRO 4000 Blackwell 24GB? YES