Can I run Qwen3.8-Flash-Next on an RX 9060 XT 16GB?

Memory and speed on this page are our estimate, not a measured test. Checked 24 Oct 2026. unsloth UD-Q4_K_XL GGUF (not Q4_K_M).

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 our parameter count is an estimate. Hugging Face safetensors metadata lists 179,999,981,459 parameters (BF16 179,999,981,424 and I64 35). Our memory estimate uses that total, rounded to 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 our estimate, so the Conversation column does not show 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; we use 262,144. The card's citation is August 2026. Hugging Face lists the repo as created on 24 August 2026. Speed is not estimated: our speed estimate reads every weight for each token and would understate a sparse MoE, so we show no speed figure. Unsloth GGUF sizes, shards summed, decimal GB, checked 24 Oct 2026 (https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF): 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 can keep the most-used MoE experts in a GPU cache while all experts stay in system RAM (--cpu-moe or --n-cpu-moe plus --moe-cache-mib, merged 7 Oct 2026, https://github.com/ggml-org/llama.cpp/pull/29887). Multi-GPU support merged 8 Oct 2026 (https://github.com/ggml-org/llama.cpp/pull/30112). https://github.com/ggml-org/llama.cpp/pull/30637 replaced the MoE LRU cache with a 50/50 SLRU cache and merged on 19 Oct 2026 (release b11738 and later). The author says that is about 1–5% better than LRU. That comparison is self-reported. The cache is still not the same as the GGUF fitting in 24 GB of VRAM. https://github.com/ggml-org/llama.cpp/pull/27861 was still an open draft and https://github.com/ggml-org/llama.cpp/pull/30594 was still open, checked 24 Oct 2026, and both stay proposed.