FITMYLLM · AUGUST 17, 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_M 2.93 30.9 GB
27.1 + 3.8 KV
low Q2_K 3.16 33.0 GB
29.2 + 3.8 KV
low IQ3_XXS 3.25 33.8 GB
30.0 + 3.8 KV
low IQ3_XS 3.5 36.0 GB
32.3 + 3.8 KV
low Q3_K_S 3.64 37.3 GB
33.6 + 3.8 KV
low IQ3_M 3.76 38.4 GB
34.7 + 3.8 KV
low Q3_K_M 4 40.6 GB
36.8 + 3.8 KV
low Q3_K_L 4.3 43.3 GB
39.6 + 3.8 KV
moderate IQ4_XS 4.46 44.8 GB
41.0 + 3.8 KV
moderate Q4_K_S 4.67 46.7 GB
42.9 + 3.8 KV
moderate Q4_K_M 4.89 48.7 GB
44.9 + 3.8 KV
good Q5_K_S 5.57 54.9 GB
51.1 + 3.8 KV
good Q5_K_M 5.7 56.0 GB
52.3 + 3.8 KV
good Q6_K 6.56 63.9 GB
60.1 + 3.8 KV
excellent Q8_0 8.5 81.5 GB
77.7 + 3.8 KV
lossless FP16 16 149.6 GB
145.9 + 3.8 KV
lossless
Select your GPU above to see speed estimates and compatibility for each quantization.
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Run this model IQ2_M — 27.1 GB VRAM Q2_K — 29.2 GB VRAM IQ3_XXS — 30.0 GB VRAM IQ3_XS — 32.3 GB VRAM Q3_K_S — 33.6 GB VRAM IQ3_M — 34.7 GB VRAM Q3_K_M — 36.8 GB VRAM Q3_K_L — 39.6 GB VRAM IQ4_XS — 41.0 GB VRAM Q4_K_S — 42.9 GB VRAM Q4_K_M — 44.9 GB VRAM Q5_K_S — 51.1 GB VRAM Q5_K_M — 52.3 GB VRAM Q6_K — 60.1 GB VRAM Q8_0 — 77.7 GB VRAM FP16 — 145.9 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:73b-q4_K_MCOPY
Tag may need adjustment — check ollama.com/library/qwen 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 Qwen2-VL 72B
Build Hardware for Qwen2-VL 72B Qwen2-VL 72B — most capable Qwen vision model. Detailed image understanding.
Read full model card ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Qwen2-VL 72B — 72.7B Dense. ▸ SPECIFICATIONS
PARAMETERS 72.7B
ARCHITECTURE Dense Transformer
CONTEXT LENGTH 32K tokens
CAPABILITIES chat, vision
RELEASE DATE 2024-10-03
PROVIDER Alibaba
FAMILY qwen ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_M 2.93 27.1 GB 75% Q2_K 3.16 29.2 GB 78% IQ3_XXS 3.25 30.0 GB 82% IQ3_XS 3.5 32.3 GB 84% Q3_K_S 3.64 33.6 GB 85% IQ3_M 3.76 34.7 GB 86% Q3_K_M 4 36.8 GB 88% Q3_K_L 4.3 39.6 GB 90% IQ4_XS 4.46 41.0 GB 92% Q4_K_S 4.67 42.9 GB 93% Q4_K_M 4.89 44.9 GB 94% Q5_K_S 5.57 51.1 GB 96% Q5_K_M 5.7 52.3 GB 96% Q6_K 6.56 60.1 GB 97% Q8_0 8.5 77.7 GB 100% FP16 16 145.9 GB 100%
§ 01 BENCHMARK SCORES
HumanEval 76.0
MMLU-PRO 50.4
MATH 60.1
IFEval 85.9
BBH 60.5
MMMU 64.5
GPQA 19.4
MUSR 12.3
MBPP 61.6
BigCodeBench 33.2
MMBench 86.9
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback 