FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
2446.18B
Parameters (95B active)
Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K 64K 128K 256K
Quant Bits VRAM @ 16K Quality IQ2_XXS 2.38 729.3 GB
728.2 + 1.1 KV
low IQ2_M 2.93 897.5 GB
896.4 + 1.1 KV
low Q2_K 3.16 967.8 GB
966.7 + 1.1 KV
low IQ3_XXS 3.25 995.3 GB
994.2 + 1.1 KV
low IQ3_XS 3.5 1071.8 GB
1070.7 + 1.1 KV
low Q3_K_S 3.64 1114.6 GB
1113.5 + 1.1 KV
low IQ3_M 3.76 1151.3 GB
1150.2 + 1.1 KV
low Q3_K_M 4 1224.7 GB
1223.6 + 1.1 KV
low Q3_K_L 4.3 1316.4 GB
1315.3 + 1.1 KV
moderate IQ4_XS 4.46 1365.3 GB
1364.2 + 1.1 KV
moderate Q4_K_S 4.67 1429.5 GB
1428.4 + 1.1 KV
moderate Q4_K_M 4.89 1496.8 GB
1495.7 + 1.1 KV
good Q5_K_S 5.57 1704.7 GB
1703.6 + 1.1 KV
good Q5_K_M 5.7 1744.5 GB
1743.4 + 1.1 KV
good Q6_K 6.56 2007.4 GB
2006.4 + 1.1 KV
excellent Q8_0 8.5 2600.6 GB
2599.6 + 1.1 KV
lossless FP16 16 4893.9 GB
4892.8 + 1.1 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 ~729 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 QWEN3.8 2.4T A95B 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 — 728.2 GB VRAM IQ2_M — 896.4 GB VRAM Q2_K — 966.7 GB VRAM IQ3_XXS — 994.2 GB VRAM IQ3_XS — 1070.7 GB VRAM Q3_K_S — 1113.5 GB VRAM IQ3_M — 1150.2 GB VRAM Q3_K_M — 1223.6 GB VRAM Q3_K_L — 1315.3 GB VRAM IQ4_XS — 1364.2 GB VRAM Q4_K_S — 1428.4 GB VRAM Q4_K_M — 1495.7 GB VRAM Q5_K_S — 1703.6 GB VRAM Q5_K_M — 1743.4 GB VRAM Q6_K — 2006.4 GB VRAM Q8_0 — 2599.6 GB VRAM FP16 — 4892.8 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:2446b-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
Find the best GPU for Qwen3.8 2.4T A95B
Build Hardware for Qwen3.8 2.4T A95B ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Qwen3.8 2.4T A95B — 2446.18B MoE. ▸ SPECIFICATIONS
PARAMETERS 2446.18B (95B active)
ARCHITECTURE Mixture of Experts
CONTEXT LENGTH 256K tokens
CAPABILITIES chat, coding, reasoning, multilingual, math, agentic, tool_use
RELEASE DATE 2026-08-12
PROVIDER Alibaba
FAMILY qwen ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_XXS 2.38 728.2 GB 65% IQ2_M 2.93 896.4 GB 75% Q2_K 3.16 966.7 GB 78% IQ3_XXS 3.25 994.2 GB 82% IQ3_XS 3.5 1070.7 GB 84% Q3_K_S 3.64 1113.5 GB 85% IQ3_M 3.76 1150.2 GB 86% Q3_K_M 4 1223.6 GB 88% Q3_K_L 4.3 1315.3 GB 90% IQ4_XS 4.46 1364.2 GB 92% Q4_K_S 4.67 1428.4 GB 93% Q4_K_M 4.89 1495.7 GB 94% Q5_K_S 5.57 1703.6 GB 96% Q5_K_M 5.7 1743.4 GB 96% Q6_K 6.56 2006.4 GB 97% Q8_0 8.5 2599.6 GB 100% FP16 16 4892.8 GB 100%
§ 01 BENCHMARK SCORES
GPQA Diamond 93.5
HLE 42.4
AA Intelligence 57.7
AA Coding 71.9
aa_scicode 51.6
aa_lcr 75.3
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