FITMYLLM · JULY 28, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
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Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K
Quant Bits VRAM @ 16K Quality IQ2_XXS 2.38 37.0 GB
33.2 + 3.8 KV
low IQ2_M 2.93 44.5 GB
40.8 + 3.8 KV
low Q2_K 3.16 47.7 GB
43.9 + 3.8 KV
low IQ3_XXS 3.25 48.9 GB
45.2 + 3.8 KV
low IQ3_XS 3.5 52.4 GB
48.6 + 3.8 KV
low Q3_K_S 3.64 54.3 GB
50.5 + 3.8 KV
low IQ3_M 3.76 55.9 GB
52.2 + 3.8 KV
low Q3_K_M 4 59.2 GB
55.5 + 3.8 KV
low Q3_K_L 4.3 63.4 GB
59.6 + 3.8 KV
moderate IQ4_XS 4.46 65.6 GB
61.8 + 3.8 KV
moderate Q4_K_S 4.67 68.5 GB
64.7 + 3.8 KV
moderate Q4_K_M 4.89 71.5 GB
67.7 + 3.8 KV
good Q5_K_S 5.57 80.8 GB
77.1 + 3.8 KV
good Q5_K_M 5.7 82.6 GB
78.9 + 3.8 KV
good Q6_K 6.56 94.4 GB
90.7 + 3.8 KV
excellent Q8_0 8.5 121.1 GB
117.4 + 3.8 KV
lossless FP16 16 224.2 GB
220.5 + 3.8 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 QWEN 1.5 110B NOW
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Run this model IQ2_XXS — 33.2 GB VRAM IQ2_M — 40.8 GB VRAM Q2_K — 43.9 GB VRAM IQ3_XXS — 45.2 GB VRAM IQ3_XS — 48.6 GB VRAM Q3_K_S — 50.5 GB VRAM IQ3_M — 52.2 GB VRAM Q3_K_M — 55.5 GB VRAM Q3_K_L — 59.6 GB VRAM IQ4_XS — 61.8 GB VRAM Q4_K_S — 64.7 GB VRAM Q4_K_M — 67.7 GB VRAM Q5_K_S — 77.1 GB VRAM Q5_K_M — 78.9 GB VRAM Q6_K — 90.7 GB VRAM Q8_0 — 117.4 GB VRAM FP16 — 220.5 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 qwen:110b-chat-v1.5-q4_K_MCOPY
Downloads and runs automatically. Add --verbose for speed stats.
▸ 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 Qwen 1.5 110B
Build Hardware for Qwen 1.5 110B Qwen 1.5 110B — largest dense Qwen model.
Read full model card ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Qwen 1.5 110B — 110B Dense. ▸ SPECIFICATIONS
PARAMETERS 110B
ARCHITECTURE Dense Transformer
CONTEXT LENGTH 32K tokens
CAPABILITIES chat
RELEASE DATE 2024-02-04
PROVIDER Alibaba
FAMILY qwen ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 33.2 GB 65% IQ2_M 2.93 40.8 GB 75% Q2_K 3.16 43.9 GB 78% IQ3_XXS 3.25 45.2 GB 82% IQ3_XS 3.5 48.6 GB 84% Q3_K_S 3.64 50.5 GB 85% IQ3_M 3.76 52.2 GB 86% Q3_K_M 4 55.5 GB 88% Q3_K_L 4.3 59.6 GB 90% IQ4_XS 4.46 61.8 GB 92% Q4_K_S 4.67 64.7 GB 93% Q4_K_M 4.89 67.7 GB 94% Q5_K_S 5.57 77.1 GB 96% Q5_K_M 5.7 78.9 GB 96% Q6_K 6.56 90.7 GB 97% Q8_0 8.5 117.4 GB 100% FP16 16 220.5 GB 100%
§ 01 BENCHMARK SCORES
MMLU-PRO 42.5
MATH 23.4
IFEval 59.4
BBH 45.0
GPQA 12.2
MUSR 16.3
BigCodeBench 35.0
§ 02 RUN COMMAND
Run Qwen 1.5 110B locally with Ollama — needs 67.7 GB VRAM at Q4_K_M:
$ ollama run qwen:110b
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback 