model

Qwen3.8-Flash-Next: how much memory, and what runs it

Mixture-of-experts vision-language model. Hugging Face metadata records license: other and license_name: qwen-community-1.0. The LICENSE file is titled Qwen Community License 1.0. It requires the copyright notice. A commercial product or service with more than 100 million monthly active users, or more than US$20 million in monthly revenue, must display the model name. A Model as a Service or AI Work Assistant business needs a separate licence from Qwen before commercial use. Internal use that does not make the model or its outputs available to a third party is exempt from that separate licence. The card says 125B with 6B activated, plus 51B n-gram embedding and 4B MTP. Those round billions are why paramsEstimated stays true. Hugging Face safetensors metadata lists 179,999,981,459 parameters (BF16 179,999,981,424 and I64 35). VRAM math uses that total, stored as 180B. 6B active is the card's activated figure for the 125B portion, not a second measurement. 512 experts in the config, 10 routed per token. The card adds 1 shared expert. 48 layers: 36 linear_attention and 12 full_attention. Those 12 are Qwen Sparse Attention. The card's budget is 512 blocks or 2,048 tokens, and text_config.indexer_budget is 2048. That cache is outside this formula, so kvNotModelled is set and the KV column is not a computed cache. 2 KV heads, head dim 256, max_position_embeddings 262,144. The card says the window is extensible to 1,000,000 tokens with a RoPE change; this catalog uses 262,144. The card's citation is August 2026. Hugging Face lists the repo as created on 24 August 2026. Speed is not estimated: the formula streams every weight and understates sparse MoE, and no speed figure is stored. Unsloth GGUF sizes, shards summed, decimal GB, checked 19 Oct 2026: UD-IQ4_XS 93.68, UD-Q4_K_XL 111.33, Q8_0 188.23. MTP/mtp-Qwen3.8-Flash-Next-Q8_0.gguf is 4.14 GB (4,137,429,120 bytes) and is not included in those three totals. mmproj-F16 is 0.90 GB (904,004,000 bytes), also separate. Strata's README says the graphics card keeps the experts that are used most often and RAM holds all of them (https://github.com/Niko1221/Strata). Releases: https://github.com/Niko1221/Strata/releases. Mainline llama.cpp GPU expert-cache pull requests were still open on 19 Oct 2026: https://github.com/ggml-org/llama.cpp/pull/27861, https://github.com/ggml-org/llama.cpp/pull/30594 and https://github.com/ggml-org/llama.cpp/pull/30637.

Qwen3.8-Flash-Next needs about 112.1 GB at Q4_K_M with 8K context (estimate). See which GPUs and Macs fit it, and EU prices. Speed is not estimated for this mixture-of-experts model.

At 8,192 tokens of context. Catalog quant rates are a formula. sourceUrl on that memory stays null.

KV cache not modelled for this architecture: real use needs more than the figures shown

Parameter count source: huggingface.co/Qwen/Qwen3.8-Flash-Next. 180B total, 6B active. Memory math uses the total.

VRAM per quant and the cheapest rig for Qwen3.8-Flash-Next

Open in the calculator

[![Qwen3.8-Flash-Next Q4 on RTX 3060 12GB: ✗ · needs 112.1 GB (estimate)](https://localllmrig.com/badge/qwen3.8-flash-next/rtx-3060-12gb.svg)](https://localllmrig.com/can-i-run/qwen3.8-flash-next/rtx-3060-12gb?utm_source=badge)

[![Qwen3.8-Flash-Next Q4: needs ~112.1 GB VRAM (estimate)](https://localllmrig.com/badge/qwen3.8-flash-next.svg)](https://localllmrig.com/models/qwen3.8-flash-next?utm_source=badge)