Can OLMo 2 13B run on NVIDIA A100 40GB?

YES — Runs Great

A77Great

Estimated from fit model

OLMo 2 13B needs ~15.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~178 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) — 15.3 GB, 177.9 tok/s, Runs well

15.3 GB required40.0 GB available

38% VRAM used

Fit status

Runs well

Decode

177.9 tok/s

TTFT

1088 ms

Safe context

33K

Memory

15.3 GB / 40.0 GB

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on NVIDIA A100 40GB
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: 177.9 tok/s decode · 1.1s TTFT (warm) · 445 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	177.9 tok/s	594 ms	33K
Coding	A	Runs well	177.9 tok/s	1088 ms	33K
Agentic Coding	A	Runs well	177.9 tok/s	1583 ms	33K
Reasoning	A	Runs well	177.9 tok/s	1286 ms	33K
RAG	A	Runs well	177.9 tok/s	1979 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

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B69
Q3_K_S	3	6.4 GB	Low	B70
NVFP4	4

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 A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run OLMo 2 13B?

Yes, NVIDIA A100 40GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 177.9 tok/s.

How much VRAM does OLMo 2 13B need?

OLMo 2 13B (13B parameters) requires approximately 15.3 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 NVIDIA A100 40GB?

On NVIDIA A100 40GB, OLMo 2 13B achieves approximately 177.9 tokens per second decode speed with a time-to-first-token of 1088ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on NVIDIA A100 40GB receives a A grade with 177.9 tok/s and 33K context.

What context window can OLMo 2 13B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, 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 A100 40GB See all hardware for OLMo 2 13B

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