Deep Cogito8B~6 GB VRAM at Q4_K_M

Cogito v1 8B — VRAM, speed & local setup

Written by Jakub Rusinowski · Last updated

Mid-tier Cogito variant optimized for 8 GB VRAM GPUs. Dynamic reasoning gives it reasoning quality comparable to Llama 3.1 70B on math benchmarks, while running on a single RTX 4060. Best price-to-performance reasoning model at the 8B tier.

Cogito v1 8B needs about 6 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.

Logic78
Creative70
Coding74

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
4.5 GB~155 tok/sFits
Q3_K_M
3.41 bpw
5.3 GB~134 tok/sFits
Q4_K_M
4.83 bpw
6.7 GB~107 tok/sFits
Q5_K_M
5.67 bpw
7.5 GB~96 tok/sFits
Q6_K
6.56 bpw
8.4 GB~86 tok/sFits
Q8_0
8.50 bpw
10.4 GB~70 tok/sFits
F16
16.00 bpw
17.9 GB~41 tok/sFits

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Cogito v1 8B VRAM calculator →

Get Cogito v1 8B running

The cheapest catalogued GPU that runs Cogito v1 8B is the Intel Arc B570 (10 GB).

Buy this hardware Intel Arc B570 10GB — 10 GB VRAM · 150 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 B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.

How to run Cogito v1 8B

Install Ollama, then run:

ollama run cogito:8b
Weights on Hugging Face: deepcogito/Cogito-v1-8B-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
8 Billion
Context window
32,000
Architecture
Hybrid Reasoning Transformer
Provider
Deep Cogito
Licence
Apache 2.0
Practical deployment
Laptop
Specified at
Q4_K_M
System RAM
16 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 forreasoningmathlocal inferenceconsumer gpu
BenchmarkScoreProvenance
MMLU75.3 / 100 %reported
MATH65.8 / 100 %reported
HumanEval70.2 / 100 %reported

Can I run Cogito v1 8B on my GPU?

Other Cogito v1 sizes

Cogito v1 8B — frequently asked questions

How much VRAM does Cogito v1 8B need?

About 6 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 8B run on an RTX 4090 (24 GB)?

Yes. Cogito v1 8B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.

How do I run Cogito v1 8B locally?

Install Ollama and run `ollama run cogito:8b`. 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.