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Qwen3.5-122B-A10B

Qwen3.5-122B-A10B by Alibaba Qwen (122B parameters, MoE with 10B active per token). At Q4_K_M with an 8K context it needs about 73.0 GiB of memory. A 122B MoE with 10B active: a model for 128 GB unified-memory machines or a 96 GB workstation card.

Last updated: 2026-10-13

Model facts

DeveloperAlibaba Qwen
Parameters122B (10B active, MoE)[src]
Native context262,144 tokens[src]
LicenceApache-2.0[src]
Commercial useYes
KV cache12 of 48 layers (the rest are linear attention with a fixed-size state)[src]
Released2026-02

Good for: reasoning · coding · agents · Chinese · 128GB unified-memory machines

Run it

ollama run qwen3.5:122b

Memory needed

Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.

Quantization4K context8K context32K context
Q4_K_M72.9 GiB73.0 GiB73.6 GiB
Q5_K_M85.4 GiB85.5 GiB86.1 GiB
Q6_K98.9 GiB99.0 GiB99.5 GiB
Q8_0127.4 GiB127.4 GiB128.0 GiB
FP16239.2 GiB239.3 GiB239.9 GiB

Which hardware runs it

Best quantization that fits each device with a 8K context. Estimates, not benchmarks. See the methodology.

HardwareMemoryBest quanttok/s (est.)
RTX PRO 6000 Blackwell Workstation Edition96 GBQ5_K_M176
M5 Max (18-core CPU / 40-core GPU)128 GBQ5_K_M56
M4 Max (16-core CPU / 40-core GPU)128 GBQ5_K_M50
M5 Ultra512 GBFP1639
M3 Ultra512 GBFP1627
RTX Spark (N1X) laptops & compact desktops128 GBQ5_K_M25
DGX Spark128 GBQ5_K_M23
Ryzen AI Max+ 395 (Strix Halo) systems128 GBQ5_K_M22
Ryzen AI Max+ PRO 495 (Gorgon Halo) systems192 GBQ8_015
GeForce RTX 3060 (12 GB)12 GBDoesn't fit–
GeForce RTX 309024 GBDoesn't fit–
GeForce RTX 4060 Ti 16GB16 GBDoesn't fit–
GeForce RTX 4080 SUPER16 GBDoesn't fit–
GeForce RTX 409024 GBDoesn't fit–
GeForce RTX 5060 Ti 16GB16 GBDoesn't fit–
GeForce RTX 507012 GBDoesn't fit–
GeForce RTX 5070 Ti16 GBDoesn't fit–
GeForce RTX 508016 GBDoesn't fit–
GeForce RTX 509032 GBDoesn't fit–
Radeon RX 7900 XT20 GBDoesn't fit–
Radeon RX 7900 XTX24 GBDoesn't fit–
Radeon RX 9060 XT 16GB16 GBDoesn't fit–
Radeon RX 9070 XT16 GBDoesn't fit–
Arc B58012 GBDoesn't fit–
RTX A600048 GBDoesn't fit–
RTX 6000 Ada Generation48 GBDoesn't fit–
RTX PRO 5000 72GB Blackwell72 GBDoesn't fit–
Radeon AI PRO R970032 GBDoesn't fit–
Arc Pro B6024 GBDoesn't fit–
Arc Pro B7032 GBDoesn't fit–
M4 Pro64 GBDoesn't fit–
M4 Max (14-core CPU / 32-core GPU)36 GBDoesn't fit–
M532 GBDoesn't fit–
M5 Pro64 GBDoesn't fit–
M5 Max (18-core CPU / 32-core GPU)36 GBDoesn't fit–
M6 (16GB configuration, 153 GB/s)16 GBDoesn't fit–
M6 (24GB/32GB configurations, 170 GB/s)32 GBDoesn't fit–

Frequently asked questions

How much VRAM does Qwen3.5-122B-A10B need?

With an 8K context, about 73.0 GiB at Q4_K_M and 127.4 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.

Can Qwen3.5-122B-A10B run on an GeForce RTX 4090?

Qwen3.5-122B-A10B does not fit on the GeForce RTX 4090 (24 GB), even at Q4_K_M with an 8K context.

Can I run Qwen3.5-122B-A10B on a Mac?

Yes. Qwen3.5-122B-A10B fits on the M3 Ultra (256 GB) at Q8_0 with an 8K context, using about 127.4 GiB, at an estimated 50 tokens/s.

Can Qwen3.5-122B-A10B be used commercially?

Qwen3.5-122B-A10B is released under Apache-2.0. The licence allows commercial use (read its conditions).

Sources