Qwen3.8-27B-NVFP4-W4A16-PreTensor

Qwen3.8-27B NVFP4 W4A16 Multimodal for PreTensor

This is the complete local PreTensor distribution for Qwen3.8-27B. It contains the language model in PreTensor's pre-quantized NVFP4 W4A16 format and the original vision encoder and multimodal merger in BF16.

The model runs through the PreTensor Chrome extension and its local NVIDIA CUDA host. It is not a WebGPU model and does not use a browser WebGPU inference runtime.

• Language weights: packed FP4 E2M1, group size 16
• Language weight scales: FP8 E4M3FN plus FP32 global scales
• Language activations: BF16 (W4A16)
• Vision encoder and multimodal merger: original BF16, not quantized
• Text tensors copied byte-for-byte from the verified PreTensor language checkpoint
• Vision tensors copied byte-for-byte from the source model

The files are valid sharded safetensors, but the packed tensor names and fused Gate/Up layout require the PreTensor loader. Stock Transformers does not load this pre-quantized checkpoint directly. This limitation is explicit so that Transformers does not silently interpret packed FP4 bytes as ordinary weights.

See pretensor_config.json for the exact storage contract and shard hashes.

Model identity and size

• Model architecture: Qwen3.8-27B
• Source parameter count: 27,781,427,952 parameters (27.8B)
• Quantized tensor payload: 17,881,899,428 bytes (16.654 GiB)

The 16.654 GiB figure is compressed on-disk storage, not a 16B parameter model. NVFP4 weights are packed into byte arrays, so counting stored array elements does not recover the source model's parameter count. The exact source parameter count is recorded as metadata.total_parameters in model.safetensors.index.json.

Long-context prefill validation

Warm native measurements on NVIDIA RTX PRO 6000 Blackwell Max-Q (CUDA device 0, driver 620.12), after model/graph and exact KV-capacity warm-up. Each result is the median of 3 runs.

These figures validate this PreTensor implementation on the stated machine; they are not general hardware-independent model benchmarks.

Package contents

• Safetensors shards: 5
• Total tensors: 1,986
• Text tensors: 1,653
• Vision tensors: 333
• Quantized tensor payload: 17,881,899,428 bytes (16.654 GiB; not parameter count)

All five model-0000x-of-00005.safetensors files and model.safetensors.index.json are required. The language tensors span shards 1-4; the vision tensors span shards 4-5.

The tokenizer, processor configuration, chat template, and license are copied from the source Qwen/Qwen3.8-27B model revision recorded in pretensor_config.json.