Qwen3.8-2.4T-A95B lands with day-0 vLLM support
Questions this post answers
What hardware do I need to run Qwen3.8-2.4T-A95B?
At least two NVIDIA B300 or AMD MI355X nodes are required for full-precision inference, or a single node if using FP4 quantization. The model is a 2.4-trillion-parameter sparse mixture-of-experts model with 512 experts, hybrid linear/full attention, and 92 layers, built on the Qwen 3.5 architecture. Teams sizing GPU clusters for frontier MoE models can track hardware requirement changes like these on daily.dev.
Does vLLM support Qwen3.8-2.4T-A95B on release day?
Yes, vLLM shipped day-0 support for Qwen3.8-2.4T-A95B, including deployment guidance and recommended generation parameters. The release also includes kernels co-developed with NVIDIA and AMD covering attention, GEMMs, and MoE routing, meaning support goes beyond just loading the model. Engineers evaluating new model releases for production inference can follow vLLM support updates on daily.dev.
How much accuracy do quantized versions of Qwen3.8-2.4T-A95B lose compared to full precision?
Little to none, according to benchmarks on GSM8K and AIME25 for the NVFP4 and MXFP4 quantized versions released by Inferact, which land close to full-precision scores. Qwen itself shipped FP8 and BF16 checkpoints at launch, giving multiple precision options depending on available hardware. Anyone choosing a quantization strategy for large MoE models can compare accuracy trade-offs like these on daily.dev.