Qwen 3.5 122B-A10B — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated February 24, 2026

Model library → Qwen 3.5 → Qwen 3.5 122B-A10B

Large MoE model with 122B total parameters (256 routed + 1 shared expert) and 10B active per token. Q4_K_M is ~74 GB — needs 2x24GB+ GPUs, a single 80GB GPU, or a Mac Studio with 96GB+ unified memory. Delivers near-frontier performance for long-context enterprise tasks.

Qwen 3.5 122B-A10B needs about 74 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	122 Billion (10B active)
Context window	262,144
Architecture	Hybrid Gated DeltaNet + MoE
Provider	Alibaba Cloud
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	128 GB
Record updated	2026-02-24

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	40.1 GB	40.9 GB	~18 tok/s (est.)	Offloads to system RAM (slow)
Q3_K_M	52.0 GB	52.8 GB	~15 tok/s (est.)	Offloads to system RAM (slow)
Q4_K_M	73.7 GB	74.5 GB	—	Won't fit
Q5_K_M	86.5 GB	87.3 GB	—	Won't fit
Q6_K	100.0 GB	100.8 GB	—	Won't fit
Q8_0	129.6 GB	130.4 GB	—	Won't fit
F16	244.0 GB	244.8 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen 3.5 122B-A10B VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs Qwen 3.5 122B-A10B is the AMD Ryzen AI Max+ 395 (96 GB).

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Ryzen AI Max+ 395 Laptop (Strix Halo, up to 128GB)

96 GB VRAM · 120 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to Run Qwen 3.5 122B-A10B

Install Ollama, then run:

ollama run qwen3.5:122b

Weights on Hugging Face: Qwen/Qwen3.5-122B-A10B-Instruct.

Best for: enterprise, long context, research, multi gpu.

Can I Run Qwen 3.5 122B-A10B on My GPU?

Other Qwen 3.5 Sizes

Qwen 3.5 0.8B — about 1 GB VRAM at Q4_K_M
Qwen 3.5 2B — about 2 GB VRAM at Q4_K_M
Qwen 3.5 4B — about 3 GB VRAM at Q4_K_M
Qwen 3.5 9B — about 6 GB VRAM at Q4_K_M
Qwen 3.5 27B — about 17 GB VRAM at Q4_K_M
Qwen 3.5 35B-A3B — about 22 GB VRAM at Q4_K_M
Qwen 3.5 397B-A17B — about 240 GB VRAM at Q4_K_M

Qwen 3.5 122B-A10B — Frequently Asked Questions

How much VRAM does Qwen 3.5 122B-A10B need?

About 74 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Qwen 3.5 122B-A10B run on an RTX 4090 (24 GB)?

No. Qwen 3.5 122B-A10B needs about 74 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Qwen 3.5 122B-A10B locally?

Install Ollama and run `ollama run qwen3.5:122b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen 3.5 come in?

Qwen 3.5 0.8B (1 GB), Qwen 3.5 2B (2 GB), Qwen 3.5 4B (3 GB), Qwen 3.5 9B (6 GB), Qwen 3.5 27B (17 GB), Qwen 3.5 35B-A3B (22 GB), Qwen 3.5 122B-A10B (74 GB), Qwen 3.5 397B-A17B (240 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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