Can OLMo 2 13B run on RTX 3090 24GB?

YES — Runs Great

A81Great

Estimated from fit model

OLMo 2 13B needs ~13.7 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~87 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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, 87.4 tok/s, Runs well

13.7 GB required24.0 GB available

57% VRAM used

Fit status

Runs well

Decode

87.4 tok/s

TTFT

2214 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 3090 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: 87.4 tok/s decode · 2.2s TTFT (warm) · 219 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	87.4 tok/s	1208 ms	33K
Coding	A	Runs well	87.4 tok/s	2214 ms	33K
Agentic Coding	A	Runs well	87.4 tok/s	3220 ms	33K
Reasoning	A	Runs well	87.4 tok/s	2616 ms	33K
RAG	A	Runs well	87.4 tok/s	4025 ms	33K

Inference speed

OLMo 2 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for OLMo 2 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~159 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	158.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.6	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	94.1	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	87.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	75.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	63.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	59.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	41.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	41.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	37.4	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	32.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	30.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	25.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	21.9	Offloads
RTX 4060 8GB	8 GB	Q4_K_M	9.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on RTX 3090 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 3090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	65.4 tok/s
Qwen 3.5 27B	27B	S	29.3 tok/s
Qwen 3.6 27B	27B	S	19.6 tok/s
Qwen 3.6 35B A3B	35B	A	42.7 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	102.5 tok/s

Frequently asked questions

Can RTX 3090 24GB run OLMo 2 13B?

Yes, RTX 3090 24GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 87.4 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 13.7 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 3090 24GB?

On RTX 3090 24GB, OLMo 2 13B achieves approximately 87.4 tokens per second decode speed with a time-to-first-token of 2214ms using Q4_K_M quantization.

Can RTX 3090 24GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on RTX 3090 24GB receives a A grade with 87.4 tok/s and 33K context.

What context window can OLMo 2 13B use on RTX 3090 24GB?

On RTX 3090 24GB, 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 3090 24GB See all hardware for OLMo 2 13B

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