Qwen3.8-Flash-Next — VRAM, Speed & Local Setup

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

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Model library → Qwen3.8 → Qwen3.8-Flash-Next

An experimental preview of the architecture intended to underpin Qwen4, not a conventional production release. 180B of parameters must be resident — a 125B MoE backbone, a 51B n-gram lookup table and a 4B multi-token-prediction head, about 109 GB at Q4_K_M — while only 6B activate per token. 48 layers alternating three Gated DeltaNet blocks (compressing history into a fixed recurrent state) with one Qwen Sparse Attention block (precise long-range retrieval). Natively multimodal, 262,144-token context extensible to 1M via YaRN. Qwen Community License 1.0, NOT the Apache 2.0 the 27B carries.

Qwen3.8-Flash-Next needs about 109 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	180 Billion (6B active)
Context window	262,144
Architecture	Gated DeltaNet + Qwen Sparse Attention MoE
Provider	Alibaba Cloud
Licence	Qwen Community License 1.0
Specified at	Q4_K_M
System RAM	192 GB
Record updated	2026-09-11

Licence

Qwen Community License 1.0 — 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	59.2 GB	60.0 GB	—	Won't fit
Q3_K_M	76.7 GB	77.5 GB	—	Won't fit
Q4_K_M	108.7 GB	109.5 GB	—	Won't fit
Q5_K_M	127.6 GB	128.4 GB	—	Won't fit
Q6_K	147.6 GB	148.4 GB	—	Won't fit
Q8_0	191.3 GB	192.1 GB	—	Won't fit
F16	360.0 GB	360.8 GB	—	Won't fit

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

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Recommended GPU

The cheapest catalogued GPU that runs Qwen3.8-Flash-Next is the Apple M4 Max (128 GB).

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Apple Mac Studio M4 Max

128 GB VRAM · 35 W board power

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

How to Run Qwen3.8-Flash-Next

Install Ollama, then run:

ollama run qwen3-8

Weights on Hugging Face: Qwen/Qwen3.8-Flash-Next.

Best for: research, agentic coding, long context, multimodal.

Can I Run Qwen3.8-Flash-Next on My GPU?

Other Qwen3.8 Sizes

Qwen3.8 27B — about 18 GB VRAM at Q4_K_M
Qwen3.8-Max — about 1450 GB VRAM at Q4_K_M

Qwen3.8-Flash-Next — Frequently Asked Questions

How much VRAM does Qwen3.8-Flash-Next need?

About 109 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-Flash-Next run on an RTX 4090 (24 GB)?

No. Qwen3.8-Flash-Next needs about 109 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-Flash-Next 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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