FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
235B
Parameters (22B 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 72.6 GB
70.4 + 2.2 KV
low IQ2_M 2.93 88.8 GB
86.6 + 2.2 KV
low Q2_K 3.16 95.5 GB
93.3 + 2.2 KV
low IQ3_XXS 3.25 98.2 GB
96.0 + 2.2 KV
low IQ3_XS 3.5 105.5 GB
103.3 + 2.2 KV
low Q3_K_S 3.64 109.6 GB
107.4 + 2.2 KV
low IQ3_M 3.76 113.1 GB
110.9 + 2.2 KV
low Q3_K_M 4 120.2 GB
118.0 + 2.2 KV
low Q3_K_L 4.3 129.0 GB
126.8 + 2.2 KV
moderate IQ4_XS 4.46 133.7 GB
131.5 + 2.2 KV
moderate Q4_K_S 4.67 139.9 GB
137.7 + 2.2 KV
moderate Q4_K_M 4.89 146.3 GB
144.1 + 2.2 KV
good Q5_K_S 5.57 166.3 GB
164.1 + 2.2 KV
good Q5_K_M 5.7 170.1 GB
167.9 + 2.2 KV
good Q6_K 6.56 195.4 GB
193.2 + 2.2 KV
excellent Q8_0 8.5 252.4 GB
250.2 + 2.2 KV
lossless FP16 16 472.7 GB
470.5 + 2.2 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 ~73 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 QWEN3-VL 235B A22B INSTRUCT 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 — 70.4 GB VRAM IQ2_M — 86.6 GB VRAM Q2_K — 93.3 GB VRAM IQ3_XXS — 96.0 GB VRAM IQ3_XS — 103.3 GB VRAM Q3_K_S — 107.4 GB VRAM IQ3_M — 110.9 GB VRAM Q3_K_M — 118.0 GB VRAM Q3_K_L — 126.8 GB VRAM IQ4_XS — 131.5 GB VRAM Q4_K_S — 137.7 GB VRAM Q4_K_M — 144.1 GB VRAM Q5_K_S — 164.1 GB VRAM Q5_K_M — 167.9 GB VRAM Q6_K — 193.2 GB VRAM Q8_0 — 250.2 GB VRAM FP16 — 470.5 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 qwen3-vl:235b-a22b-instruct-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 Qwen3-VL 235B A22B Instruct
Build Hardware for Qwen3-VL 235B A22B Instruct ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Qwen3-VL 235B A22B Instruct — 235B MoE. ▸ SPECIFICATIONS
PARAMETERS 235B (22B active)
ARCHITECTURE Mixture of Experts
CONTEXT LENGTH 256K tokens
CAPABILITIES chat, vision, multilingual, reasoning, agentic
RELEASE DATE 2025-10-15
PROVIDER Alibaba
FAMILY qwen ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 70.4 GB 65% IQ2_M 2.93 86.6 GB 75% Q2_K 3.16 93.3 GB 78% IQ3_XXS 3.25 96.0 GB 82% IQ3_XS 3.5 103.3 GB 84% Q3_K_S 3.64 107.4 GB 85% IQ3_M 3.76 110.9 GB 86% Q3_K_M 4 118.0 GB 88% Q3_K_L 4.3 126.8 GB 90% IQ4_XS 4.46 131.5 GB 92% Q4_K_S 4.67 137.7 GB 93% Q4_K_M 4.89 144.1 GB 94% Q5_K_S 5.57 164.1 GB 96% Q5_K_M 5.7 167.9 GB 96% Q6_K 6.56 193.2 GB 97% Q8_0 8.5 250.2 GB 100% FP16 16 470.5 GB 100%
§ 01 BENCHMARK SCORES
MMLU-PRO 82.3
GPQA Diamond 71.2
LiveCodeBench 59.4
AIME 70.7
HLE 6.3
AA Intelligence 20.8
AA Coding 16.5
AA Math 70.7
aa_ifbench 42.7
aa_terminal_bench 6.8
aa_tau2 35.1
aa_scicode 35.9
aa_lcr 31.7
MATH-500 70.7
§ 02 RUN COMMAND
Run Qwen3-VL 235B A22B Instruct locally with Ollama — needs 144.1 GB VRAM at Q4_K_M:
$ ollama run qwen3-vl:235b
§ 03 COMPATIBLE GPUs
13 @ Q4_K_M Feedback 