FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
107.49B
Parameters (7.4B active)
0
Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K 64K 128K
Quant Bits VRAM @ 16K Quality IQ2_XXS 2.38 32.9 GB
32.5 + 0.4 KV
low IQ2_M 2.93 40.3 GB
39.9 + 0.4 KV
low Q2_K 3.16 43.4 GB
42.9 + 0.4 KV
low IQ3_XXS 3.25 44.6 GB
44.2 + 0.4 KV
low IQ3_XS 3.5 47.9 GB
47.5 + 0.4 KV
low Q3_K_S 3.64 49.8 GB
49.4 + 0.4 KV
low IQ3_M 3.76 51.4 GB
51.0 + 0.4 KV
low Q3_K_M 4 54.7 GB
54.2 + 0.4 KV
low Q3_K_L 4.3 58.7 GB
58.3 + 0.4 KV
moderate IQ4_XS 4.46 60.8 GB
60.4 + 0.4 KV
moderate Q4_K_S 4.67 63.7 GB
63.2 + 0.4 KV
moderate Q4_K_M 4.89 66.6 GB
66.2 + 0.4 KV
good Q5_K_S 5.57 75.8 GB
75.3 + 0.4 KV
good Q5_K_M 5.7 77.5 GB
77.1 + 0.4 KV
good Q6_K 6.56 89.1 GB
88.6 + 0.4 KV
excellent Q8_0 8.5 115.1 GB
114.7 + 0.4 KV
lossless FP16 16 215.9 GB
215.5 + 0.4 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 LING 2.6 FLASH 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 — 32.5 GB VRAM IQ2_M — 39.9 GB VRAM Q2_K — 42.9 GB VRAM IQ3_XXS — 44.2 GB VRAM IQ3_XS — 47.5 GB VRAM Q3_K_S — 49.4 GB VRAM IQ3_M — 51.0 GB VRAM Q3_K_M — 54.2 GB VRAM Q3_K_L — 58.3 GB VRAM IQ4_XS — 60.4 GB VRAM Q4_K_S — 63.2 GB VRAM Q4_K_M — 66.2 GB VRAM Q5_K_S — 75.3 GB VRAM Q5_K_M — 77.1 GB VRAM Q6_K — 88.6 GB VRAM Q8_0 — 114.7 GB VRAM FP16 — 215.5 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 other:107b-q4_K_MCOPY
Tag may need adjustment — check ollama.com/library/other 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 Ling 2.6 Flash
Build Hardware for Ling 2.6 Flash ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Ling 2.6 Flash — 107.49B MoE. ▸ SPECIFICATIONS
PARAMETERS 107.49B (7.4B active)
ARCHITECTURE Mixture of Experts
CONTEXT LENGTH 128K tokens
CAPABILITIES chat, coding, reasoning, multilingual
RELEASE DATE 2026-02-10
PROVIDER Ant Group
FAMILY other ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 32.5 GB 65% IQ2_M 2.93 39.9 GB 75% Q2_K 3.16 42.9 GB 78% IQ3_XXS 3.25 44.2 GB 82% IQ3_XS 3.5 47.5 GB 84% Q3_K_S 3.64 49.4 GB 85% IQ3_M 3.76 51.0 GB 86% Q3_K_M 4 54.2 GB 88% Q3_K_L 4.3 58.3 GB 90% IQ4_XS 4.46 60.4 GB 92% Q4_K_S 4.67 63.2 GB 93% Q4_K_M 4.89 66.2 GB 94% Q5_K_S 5.57 75.3 GB 96% Q5_K_M 5.7 77.1 GB 96% Q6_K 6.56 88.6 GB 97% Q8_0 8.5 114.7 GB 100% FP16 16 215.5 GB 100%
§ 01 BENCHMARK SCORES
GPQA Diamond 59.3
HLE 6.2
AA Intelligence 26.2
AA Coding 23.2
aa_ifbench 57.4
aa_terminal_bench 21.2
aa_tau2 86.0
aa_scicode 27.1
aa_lcr 25.0
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback