Qwen3.8-2.4T-A95B
Qwen3.8-2.4T-A95B is an open-weights language model from Qwen, released August 8, 2026. It has 2.4T parameters (mixture-of-experts, 95B active). The context window is 256K tokens.
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. For the first time, Qwen3.8 brings a Qwen-Max-class model to open release.
Check it against your own PC →
Can you run Qwen3.8-2.4T-A95B locally?
Even with 32 GB of VRAM and 32 GB of RAM it is too large to run at a usable speed on a single consumer GPU. Estimates assume 32 GB of DDR5 RAM, a 16K context and llama.cpp-style GGUF quantization. Enter your exact hardware in the live app.
| GPU memory | Verdict | Best quant | VRAM | RAM | tok/s |
|---|---|---|---|---|---|
| 6 GB VRAM | Too large | – | – | – | – |
| 8 GB VRAM | Too large | – | – | – | – |
| 12 GB VRAM | Too large | – | – | – | – |
| 16 GB VRAM | Too large | – | – | – | – |
| 24 GB VRAM | Too large | – | – | – | – |
| 32 GB VRAM | Too large | – | – | – | – |
Ready-made quantizations on Hugging Face: unsloth/Qwen3.8-2.4T-A95B-GGUF · Blackfrost-AI/Qwen-3.8-2.4T-A95B-Derisked-IQ1_0-GGUF · a4lg/Qwen3.8-2.4T-A95B-MTP-ONLY-GGUF · nvidia/Qwen3.8-2.4T-A95B-NVFP4 · Inferact/Qwen3.8-2.4T-A95B-NVFP4 · RadixArk/Qwen3.8-2.4T-A95B-NVFP4
Benchmark results
Epoch AI has not published results for Qwen3.8-2.4T-A95B yet. New models are usually evaluated within days to weeks.
Links
Announcement · Hugging Face: Qwen/Qwen3.8-2.4T-A95B
More from Qwen
Qwen3.8 Omni Flash · Qwen3.8 Max (0902) · Qwen3.8 Flash · Qwen3.8 Flash Next · Qwen 3.8 27B · Qwen 3.8 Max · Qwen3.7 Flash · Qwen3.7-Plus