Qwen3.8-Max — VRAM, Speed & Local Setup

作者： Jakub Rusinowski · 最后更新： 2026年9月6日

2.4 trillion parameters with roughly 95B active per token. Shipped as a service on 3 August 2026 with open weights following on 12 August under a model-specific Qwen3.8-Max licence, not Apache. Cluster-class self-hosting — about 1.45 TB at Q4_K_M — so for almost everyone this is an API model; it is listed here so the hardware checker can say so rather than stay silent.

Qwen3.8-Max needs about 1450 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	2.4 Trillion (95B active)
Context window	262,144
Architecture	Mixture-of-Experts
Provider	Alibaba Cloud
Licence	Qwen3.8-Max License (model-specific)
Specified at	Q4_K_M
System RAM	2048 GB
Record updated	2026-09-06

Licence

Custom Open-Weight — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

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	789.0 GB	789.8 GB	—	Won't fit
Q3_K_M	1023.0 GB	1023.8 GB	—	Won't fit
Q4_K_M	1449.0 GB	1449.8 GB	—	Won't fit
Q5_K_M	1701.0 GB	1701.8 GB	—	Won't fit
Q6_K	1968.0 GB	1968.8 GB	—	Won't fit
Q8_0	2550.0 GB	2550.8 GB	—	Won't fit
F16	4800.0 GB	4800.8 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Qwen3.8-Max VRAM calculator.

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How to Run Qwen3.8-Max

Install Ollama, then run:

ollama run qwen3-8

Weights on Hugging Face: Qwen/Qwen3.8-Max.

Best for: reasoning, enterprise, cloud api, agentic tasks.

Can I Run Qwen3.8-Max on My GPU?

Other Qwen3.8 Sizes

Qwen3.8 27B — about 18 GB VRAM at Q4_K_M
Qwen3.8-Flash-Next — about 109 GB VRAM at Q4_K_M

Qwen3.8-Max — Frequently Asked Questions

How much VRAM does Qwen3.8-Max need?

About 1450 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 Qwen3.8-Max run on an RTX 4090 (24 GB)?

No. Qwen3.8-Max needs about 1450 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 Qwen3.8-Max locally?

Install Ollama and run `ollama run qwen3-8`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Qwen3.8 come in?

Qwen3.8 27B (18 GB), Qwen3.8-Max (1450 GB), Qwen3.8-Flash-Next (109 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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