Deep Cogito14B~9 GB VRAM at Q4_K_M

Cogito v1 14B — VRAM, speed & local setup

Written by Jakub Rusinowski · Last updated

14B Cogito variant for 12–16 GB VRAM setups. On AIME 2025, Cogito v1-14B scores comparably to Qwen QwQ-32B — a remarkable result for a non-MoE 14B model. Ideal for developers wanting strong mathematical reasoning on a midrange GPU.

Cogito v1 14B needs about 9 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Logic83
Creative75
Coding79

VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

QuantVRAMSpeed (est.)Fit
Q2_K
2.63 bpw
7 GB~106 tok/sFits
Q3_K_M
3.41 bpw
8.4 GB~89 tok/sFits
Q4_K_M
4.83 bpw
10.9 GB~69 tok/sFits
Q5_K_M
5.67 bpw
12.3 GB~61 tok/sFits
Q6_K
6.56 bpw
13.9 GB~54 tok/sFits
Q8_0
8.50 bpw
17.3 GB~44 tok/sFits
F16
16.00 bpw
30.4 GB~4 tok/sOffload

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Cogito v1 14B VRAM calculator →

Get Cogito v1 14B running

The cheapest catalogued GPU that runs Cogito v1 14B is the Intel Arc B580 (12 GB).

Buy this hardware Intel Arc B580 12GB — 12 GB VRAM · 190 W board powerDeploy in the cloud now RTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Intel Arc B580 12GB
12 GB VRAM · 190 W board power
2026 prices are volatile — check the current listing.

How to run Cogito v1 14B

Install Ollama, then run:

ollama run cogito:14b
Weights on Hugging Face: deepcogito/Cogito-v1-14B-Instruct ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters
14 Billion
Context window
64,000
Architecture
Hybrid Reasoning Transformer
Provider
Deep Cogito
Licence
Apache 2.0
Practical deployment
Laptop
Specified at
Q4_K_M
System RAM
24 GB
Record updated
2026-03-20
LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forreasoningmathcodingmidrange gpu
BenchmarkScoreProvenance
MMLU81.2 / 100 %reported
MATH72.6 / 100 %reported
AIME 202538.4 / 100 %reported

Can I run Cogito v1 14B on my GPU?

Other Cogito v1 sizes

Cogito v1 14B — frequently asked questions

How much VRAM does Cogito v1 14B need?

About 9 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Cogito v1 14B run on an RTX 4090 (24 GB)?

Yes. Cogito v1 14B needs about 9 GB at Q4_K_M, inside a 24 GB card, at an estimated 69 tokens/sec.

How do I run Cogito v1 14B locally?

Install Ollama and run `ollama run cogito:14b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Cogito v1 come in?

Cogito v1 3B (3 GB), Cogito v1 8B (6 GB), Cogito v1 14B (9 GB), Cogito v1 32B (20 GB), Cogito v1 70B (43 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.