Cogito v1 70B — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 marca 2026

Model library → Cogito v1 → Cogito v1 70B

Largest Cogito variant for multi-GPU workstations. Scores 65.0% on AIME 2025 — comparable to DeepSeek-R1 671B on math benchmarks. Requires ~40 GB at Q4 (dual RTX 3090 or A6000). Best open-source reasoning model for teams with workstation hardware.

Cogito v1 70B needs about 43 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.

Specifications

Parameters	70 Billion
Context window	128,000
Architecture	Hybrid Reasoning Transformer
Provider	Deep Cogito
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	64 GB
Record updated	2026-03-20

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	23.0 GB	26.5 GB	~4 tok/s (est.)	Offloads to system RAM (slow)
Q3_K_M	29.8 GB	33.3 GB	~3 tok/s (est.)	Offloads to system RAM (slow)
Q4_K_M	42.3 GB	45.7 GB	~3 tok/s (est.)	Offloads to system RAM (slow)
Q5_K_M	49.6 GB	53.1 GB	~2 tok/s (est.)	Offloads to system RAM (slow)
Q6_K	57.4 GB	60.9 GB	—	Won't fit
Q8_0	74.4 GB	77.9 GB	—	Won't fit
F16	140.0 GB	143.5 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Cogito v1 70B VRAM calculator.

Buy This HardwareApple MacBook Pro M5 Pro — 64 GB VRAM · 30 W board power Deploy in the Cloud NowNVIDIA A40 on RunPod — from $0.44/hr · rate checked 2026-08

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Recommended GPU

The cheapest catalogued GPU that runs Cogito v1 70B is the Apple M5 Pro (64 GB).

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Apple MacBook Pro M5 Pro

64 GB VRAM · 30 W board power

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

How to Run Cogito v1 70B

Install Ollama, then run:

ollama run cogito:70b

Weights on Hugging Face: deepcogito/Cogito-v1-70B-Instruct.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

Benchmark	Score	Provenance
MMLU	88.9 / 100 %	reported
MATH	83.7 / 100 %	reported
AIME 2025	65 / 100 %	reported
HumanEval	88.4 / 100 %	reported

Best for: reasoning, math, research, multi gpu workstation.

Can I Run Cogito v1 70B on My GPU?

Other Cogito v1 Sizes

Cogito v1 3B — about 3 GB VRAM at Q4_K_M
Cogito v1 8B — about 6 GB VRAM at Q4_K_M
Cogito v1 14B — about 9 GB VRAM at Q4_K_M
Cogito v1 32B — about 20 GB VRAM at Q4_K_M

Cogito v1 70B — Frequently Asked Questions

How much VRAM does Cogito v1 70B need?

About 43 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 70B run on an RTX 4090 (24 GB)?

No. Cogito v1 70B needs about 43 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Cogito v1 70B locally?

Install Ollama and run `ollama run cogito:70b`. 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.

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