FitMyLLM
Alibaba/Dense

AlibabaQwen3.5-122B-A10B

chatThinkingTool Use
125.1B
Parameters
256K
Context length
17
Benchmarks
17
Quantizations
621K
HF downloads
Architecture
Dense
Released
2025-06-01
Layers
80
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

QuantBitsVRAMQuality
IQ2_XXS2.3837.7 GBlow
IQ2_M2.9346.3 GBlow
Q2_K3.1649.9 GBlow
IQ3_XXS3.2551.3 GBlow
IQ3_XS3.555.2 GBlow
Q3_K_S3.6457.4 GBlow
IQ3_M3.7659.3 GBlow
Q3_K_M463.0 GBlow
Q3_K_L4.367.7 GBmoderate
IQ4_XS4.4670.2 GBmoderate
Q4_K_S4.6773.5 GBmoderate
Q4_K_M4.8977.0 GBgood
Q5_K_S5.5787.6 GBgood
Q5_K_M5.789.6 GBgood
Q6_K6.56103.1 GBexcellent
Q8_08.5133.4 GBlossless
FP1616250.7 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

▸ READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN QWEN3.5-122B-A10B NOW

Rent on RunPod →Or Vast.ai →

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (17)

Arena Elo1492
τ²-Bench93.6
GPQA Diamond85.7
IFBench75.7
AA Long Context66.7
IFEval59.4
BBH45.0
MMLU-PRO42.5
SciCode42.0
AA Intelligence41.6
BigCodeBench35.0
AA Coding34.7
Terminal-Bench31.1
MATH23.4
HLE23.4
MUSR16.3
GPQA12.2

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:125.1b-instruct-q4_K_M

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.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines
HuggingFaceOllama LibraryGGUF DownloadsBuild Hardware

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA H100 SXM5 80GB
80 GB VRAM • 3350 GB/s
NVIDIA
$25000
NVIDIA H100 PCIe 80GB
80 GB VRAM • 2000 GB/s
NVIDIA
$25000
NVIDIA A100 SXM 80GB
80 GB VRAM • 2039 GB/s
NVIDIA
$10000
NVIDIA A100 PCIe 80GB
80 GB VRAM • 1935 GB/s
NVIDIA
$10000
NVIDIA A100 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$15000
NVIDIA A100 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
$10000
NVIDIA A100X
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H100 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 80 GB
80 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 CNX
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA A800 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
NVIDIA A800 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 SXM5
80 GB VRAM • 3360 GB/s
NVIDIA
NVIDIA RTX 6000D
84 GB VRAM • 1570 GB/s
NVIDIA
$7500
NVIDIA B200
90 GB VRAM • 4100 GB/s
NVIDIA
$30000
NVIDIA H100 NVL 94 GB
94 GB VRAM • 3940 GB/s
NVIDIA
$30000
NVIDIA H100 SXM5 94 GB
94 GB VRAM • 3360 GB/s
NVIDIA
$25000
RTX Pro 6000
96 GB VRAM • 1792 GB/s
NVIDIA
$8565
NVIDIA H100 PCIe 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
Intel Data Center GPU Max 1350
96 GB VRAM • 2460 GB/s
INTEL
NVIDIA RTX PRO 6000 Blackwell Server
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
NVIDIA RTX PRO 6000 Blackwell
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
AMD Instinct MI300A
120 GB VRAM • 5300 GB/s
AMD
$12000
Apple M4 Max (128GB)
128 GB VRAM • 546 GB/s
APPLE
$3999
AMD Instinct MI250X
128 GB VRAM • 3277 GB/s
AMD
$10000
Apple M1 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$4999
Apple M2 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$3999
AMD Radeon Instinct MI250
128 GB VRAM • 3280 GB/s
AMD
$12000

Find the best GPU for Qwen3.5-122B-A10B

Build Hardware for Qwen3.5-122B-A10B

Model Card

View on HuggingFace

Read the full model card for detailed information about this model.

▸ SPEC SHEET

Qwen3.5-122B-A10B — 125.1B Dense.

▸ SPECIFICATIONS
PARAMETERS
125.1B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat
RELEASE DATE
2025-06-01
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ2_XXS2.3837.7 GB65%
IQ2_M2.9346.3 GB75%
Q2_K3.1649.9 GB78%
IQ3_XXS3.2551.3 GB82%
IQ3_XS3.555.2 GB84%
Q3_K_S3.6457.4 GB85%
IQ3_M3.7659.3 GB86%
Q3_K_M463.0 GB88%
Q3_K_L4.367.7 GB90%
IQ4_XS4.4670.2 GB92%
Q4_K_S4.6773.5 GB93%
Q4_K_M4.8977.0 GB94%
Q5_K_S5.5787.6 GB96%
Q5_K_M5.789.6 GB96%
Q6_K6.56103.1 GB97%
Q8_08.5133.4 GB100%
FP1616250.7 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO42.5
MATH23.4
IFEval59.4
BBH45.0
GPQA12.2
MUSR16.3
BigCodeBench35.0
Arena Elo1492.0
GPQA Diamond85.7
HLE23.4
AA Intelligence41.6
AA Coding34.7
aa_ifbench75.7
aa_terminal_bench31.1
aa_tau293.6
aa_scicode42.0
aa_lcr66.7
§ 02RUN COMMAND

Run Qwen3.5-122B-A10B locally with Ollama — needs 77.0 GB VRAM at Q4_K_M:

$ollama run qwen3:125.1b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M
NVIDIA H100 SXM5 80GB
80 GB · 3350 GB/s
NVIDIA H100 PCIe 80GB
80 GB · 2000 GB/s
NVIDIA A100 SXM 80GB
80 GB · 2039 GB/s
NVIDIA A100 PCIe 80GB
80 GB · 1935 GB/s
NVIDIA A100 SXM4 80 GB
80 GB · 2040 GB/s
NVIDIA A100 PCIe 80 GB
80 GB · 1940 GB/s
NVIDIA A100X
80 GB · 2040 GB/s
NVIDIA H100 PCIe 80 GB
80 GB · 2040 GB/s
NVIDIA H100 SXM5 80 GB
80 GB · 3360 GB/s
NVIDIA H100 CNX
80 GB · 2040 GB/s
NVIDIA A800 PCIe 80 GB
80 GB · 1940 GB/s
NVIDIA A800 SXM4 80 GB
80 GB · 2040 GB/s
NVIDIA H800 PCIe 80 GB
80 GB · 2040 GB/s
NVIDIA H800 SXM5
80 GB · 3360 GB/s
NVIDIA RTX 6000D
84 GB · 1570 GB/s