FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
109B
Parameters (17B active)
0
Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K 64K 128K 256K
Quant Bits VRAM @ 16K Quality IQ2_XXS 2.38 34.6 GB
32.9 + 1.7 KV
low IQ2_M 2.93 42.1 GB
40.4 + 1.7 KV
low Q2_K 3.16 45.2 GB
43.5 + 1.7 KV
low IQ3_XXS 3.25 46.5 GB
44.8 + 1.7 KV
low IQ3_XS 3.5 49.9 GB
48.2 + 1.7 KV
low Q3_K_S 3.64 51.8 GB
50.1 + 1.7 KV
low IQ3_M 3.76 53.4 GB
51.7 + 1.7 KV
low Q3_K_M 4 56.7 GB
55.0 + 1.7 KV
low Q3_K_L 4.3 60.8 GB
59.1 + 1.7 KV
moderate IQ4_XS 4.46 62.9 GB
61.3 + 1.7 KV
moderate Q4_K_S 4.67 65.8 GB
64.1 + 1.7 KV
moderate Q4_K_M 4.89 68.8 GB
67.1 + 1.7 KV
good Q5_K_S 5.57 78.1 GB
76.4 + 1.7 KV
good Q5_K_M 5.7 79.8 GB
78.2 + 1.7 KV
good Q6_K 6.56 91.6 GB
89.9 + 1.7 KV
excellent Q8_0 8.5 118.0 GB
116.3 + 1.7 KV
lossless FP16 16 220.2 GB
218.5 + 1.7 KV
lossless
Select your GPU above to see speed estimates and compatibility for each quantization.
Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise → ▸ READY TO RUN THIS? RENT BY THE HOUR
RENT A GPU AND RUN COGITO V2 109B 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
Loading ratings...
Run this model IQ2_XXS — 32.9 GB VRAM IQ2_M — 40.4 GB VRAM Q2_K — 43.5 GB VRAM IQ3_XXS — 44.8 GB VRAM IQ3_XS — 48.2 GB VRAM Q3_K_S — 50.1 GB VRAM IQ3_M — 51.7 GB VRAM Q3_K_M — 55.0 GB VRAM Q3_K_L — 59.1 GB VRAM IQ4_XS — 61.3 GB VRAM Q4_K_S — 64.1 GB VRAM Q4_K_M — 67.1 GB VRAM Q5_K_S — 76.4 GB VRAM Q5_K_M — 78.2 GB VRAM Q6_K — 89.9 GB VRAM Q8_0 — 116.3 GB VRAM FP16 — 218.5 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:109b-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
GPUs that can run this model At Q4_K_M quantization. Sorted by minimum VRAM.
Find the best GPU for Cogito v2 109B MoE
Build Hardware for Cogito v2 109B MoE ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Cogito v2 109B MoE — 109B MoE. ▸ SPECIFICATIONS
PARAMETERS 109B (17B active)
ARCHITECTURE Mixture of Experts
CONTEXT LENGTH 256K tokens
CAPABILITIES chat, reasoning, tool_use
RELEASE DATE 2025-09-10
PROVIDER DeepCogito
FAMILY cogito ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 32.9 GB 65% IQ2_M 2.93 40.4 GB 75% Q2_K 3.16 43.5 GB 78% IQ3_XXS 3.25 44.8 GB 82% IQ3_XS 3.5 48.2 GB 84% Q3_K_S 3.64 50.1 GB 85% IQ3_M 3.76 51.7 GB 86% Q3_K_M 4 55.0 GB 88% Q3_K_L 4.3 59.1 GB 90% IQ4_XS 4.46 61.3 GB 92% Q4_K_S 4.67 64.1 GB 93% Q4_K_M 4.89 67.1 GB 94% Q5_K_S 5.57 76.4 GB 96% Q5_K_M 5.7 78.2 GB 96% Q6_K 6.56 89.9 GB 97% Q8_0 8.5 116.3 GB 100% FP16 16 218.5 GB 100%
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback