Can OLMo 2 13B run on NVIDIA V100 32GB?

YES — Runs Great

A78Great

Estimated from fit model

OLMo 2 13B needs ~14.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~82 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) — 14.5 GB, 82.1 tok/s, Runs well

14.5 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

82.1 tok/s

TTFT

2357 ms

Safe context

33K

Memory

14.5 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on NVIDIA V100 32GB
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: 82.1 tok/s decode · 2.4s TTFT (warm) · 205 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	82.1 tok/s	1286 ms	33K
Coding	A	Runs well	82.1 tok/s	2357 ms	33K
Agentic Coding	A	Runs well	82.1 tok/s	3429 ms	33K
Reasoning	A	Runs well	82.1 tok/s	2786 ms	33K
RAG	A	Runs well	82.1 tok/s	4286 ms	33K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A71
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4	7.3 GB	Medium	A71
Q4_K_M	4	7.9 GB	Medium	A72
Q5_K_M	5	9.4 GB	High	A72
Q6_K	6	10.7 GB	High	A73
Q8_0	8	13.9 GB	Very High	A75
F16Best for your GPU	16	26.7 GB	Maximum	A75

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 NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	27.4 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run OLMo 2 13B?

Yes, NVIDIA V100 32GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 82.1 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 14.5 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, OLMo 2 13B achieves approximately 82.1 tokens per second decode speed with a time-to-first-token of 2357ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on NVIDIA V100 32GB receives a A grade with 82.1 tok/s and 33K context.

What context window can OLMo 2 13B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, 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 NVIDIA V100 32GB See all hardware for OLMo 2 13B

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