Can OLMo 2 13B run on Tesla P40 24GB?

YES — Runs Great

A77Great

Estimated from fit model

OLMo 2 13B needs ~13.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~28 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) — 13.7 GB, 27.8 tok/s, Runs well

13.7 GB required24.0 GB available

57% VRAM used

Fit status

Runs well

Decode

27.8 tok/s

TTFT

6963 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 Tesla P40 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: 27.8 tok/s decode · 7.0s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	27.8 tok/s	3798 ms	33K
Coding	A	Runs well	27.8 tok/s	6963 ms	33K
Agentic Coding	A	Runs well	27.8 tok/s	10129 ms	33K
Reasoning	A	Runs well	27.8 tok/s	8230 ms	33K
RAG	A	Runs well	27.8 tok/s	12661 ms	33K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on Tesla P40 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

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 Tesla P40 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.5 27B	27B	S	13.4 tok/s

Frequently asked questions

Can Tesla P40 24GB run OLMo 2 13B?

Yes, Tesla P40 24GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 27.8 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 Tesla P40 24GB?

On Tesla P40 24GB, OLMo 2 13B achieves approximately 27.8 tokens per second decode speed with a time-to-first-token of 6963ms using Q4_K_M quantization.

Can Tesla P40 24GB run OLMo 2 13B for coding?

For coding workloads, OLMo 2 13B on Tesla P40 24GB receives a A grade with 27.8 tok/s and 33K context.

What context window can OLMo 2 13B use on Tesla P40 24GB?

On Tesla P40 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 Tesla P40 24GB See all hardware for OLMo 2 13B

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