FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
671B
Parameters (37B active)
0
Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K 64K 128K 160K
Quant Bits VRAM @ 16K Quality IQ2_XXS 2.38 200.9 GB
200.1 + 0.8 KV
low IQ2_M 2.93 247.0 GB
246.2 + 0.8 KV
low Q2_K 3.16 266.3 GB
265.5 + 0.8 KV
low IQ3_XXS 3.25 273.9 GB
273.1 + 0.8 KV
low IQ3_XS 3.5 294.9 GB
294.1 + 0.8 KV
low Q3_K_S 3.64 306.6 GB
305.8 + 0.8 KV
low IQ3_M 3.76 316.7 GB
315.9 + 0.8 KV
low Q3_K_M 4 336.8 GB
336.0 + 0.8 KV
low Q3_K_L 4.3 362.0 GB
361.2 + 0.8 KV
moderate IQ4_XS 4.46 375.4 GB
374.6 + 0.8 KV
moderate Q4_K_S 4.67 393.0 GB
392.2 + 0.8 KV
moderate Q4_K_M 4.89 411.4 GB
410.6 + 0.8 KV
good Q5_K_S 5.57 468.5 GB
467.7 + 0.8 KV
good Q5_K_M 5.7 479.4 GB
478.6 + 0.8 KV
good Q6_K 6.56 551.5 GB
550.7 + 0.8 KV
excellent Q8_0 8.5 714.2 GB
713.4 + 0.8 KV
lossless FP16 16 1343.3 GB
1342.5 + 0.8 KV
lossless
Select your GPU above to see speed estimates and compatibility for each quantization.
Too big for a single GPU — plan a multi-GPU deployment
Even the lightest quant needs ~201 GB. Size GPUs, replicas, TCO and scaling for a production setup. Open in Enterprise →
▸ READY TO RUN THIS? RENT BY THE HOUR
RENT A GPU AND RUN COGITO V2 671B MOE NOW
Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.
Community Ratings Chat Coding Reasoning Creative Vision Roleplay Agentic
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Run this model IQ2_XXS — 200.1 GB VRAM IQ2_M — 246.2 GB VRAM Q2_K — 265.5 GB VRAM IQ3_XXS — 273.1 GB VRAM IQ3_XS — 294.1 GB VRAM Q3_K_S — 305.8 GB VRAM IQ3_M — 315.9 GB VRAM Q3_K_M — 336.0 GB VRAM Q3_K_L — 361.2 GB VRAM IQ4_XS — 374.6 GB VRAM Q4_K_S — 392.2 GB VRAM Q4_K_M — 410.6 GB VRAM Q5_K_S — 467.7 GB VRAM Q5_K_M — 478.6 GB VRAM Q6_K — 550.7 GB VRAM Q8_0 — 713.4 GB VRAM FP16 — 1342.5 GB VRAM
Ollama llama.cpp vLLM LM Studio KoboldCpp Jan Docker
▸ Easiest way to get started · Beginners
DOCS ↗ curl -fsSL https://ollama.com/install.sh | shCOPY
$ ollama run cogito:671b-q4_K_MCOPY
Tag may need adjustment — check ollama.com/library/cogito for available tags.
▸ SETUP GUIDE >_
Auto-setup with fitmyllm CLI Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.
Auto-detect GPU Live tok/s in chat Speed benchmarks 9 inference engines
Find the best GPU for Cogito v2 671B MoE
Build Hardware for Cogito v2 671B MoE ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Cogito v2 671B MoE — 671B MoE. ▸ SPECIFICATIONS
PARAMETERS 671B (37B active)
ARCHITECTURE Mixture of Experts
CONTEXT LENGTH 160K tokens
CAPABILITIES chat, reasoning, tool_use, agentic
RELEASE DATE 2025-09-10
PROVIDER DeepCogito
FAMILY cogito ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 200.1 GB 65% IQ2_M 2.93 246.2 GB 75% Q2_K 3.16 265.5 GB 78% IQ3_XXS 3.25 273.1 GB 82% IQ3_XS 3.5 294.1 GB 84% Q3_K_S 3.64 305.8 GB 85% IQ3_M 3.76 315.9 GB 86% Q3_K_M 4 336.0 GB 88% Q3_K_L 4.3 361.2 GB 90% IQ4_XS 4.46 374.6 GB 92% Q4_K_S 4.67 392.2 GB 93% Q4_K_M 4.89 410.6 GB 94% Q5_K_S 5.57 467.7 GB 96% Q5_K_M 5.7 478.6 GB 96% Q6_K 6.56 550.7 GB 97% Q8_0 8.5 713.4 GB 100% FP16 16 1342.5 GB 100%
§ 01 BENCHMARK SCORES
MMLU-PRO 84.9
GPQA Diamond 76.8
LiveCodeBench 68.8
AIME 72.7
HLE 11.0
aa_ifbench 46.3
aa_terminal_bench 16.7
aa_scicode 41.0
aa_lcr 21.7
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