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Devstral-Small-2-24B-AWQ

mattbucci

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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In-house **AWQ-Marlin (4-bit)** quantization of `google/gemma-4-12B-it` — the encoder-free omni Gemma 4 (`Gemma4UnifiedForConditionalGeneration`): text + reasoning + tool-use + vision + audio, global-MQA attention, 5:1 sliding:full, ~262K native context. Packed **data-free** from the QAT base `google/gemma-4-12B-it-qat-q4_0-unquantized` (Gemma's quantization-aware-trained release). Because QAT already conditions the weights onto a 4-bit grid, a plain RTN re-quantization to AWQ group-128 is **near-lossless** — no calibration corpus needed: Weights are **5.4 GB/rank** at int4. Identical text quality to the BF16 base at matched context length.

Visual QA

Input Text · Image→ Output Text

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gemma-4-26B-AWQ

mattbucci

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gemma-4-31B-it-AutoRound-AWQ

mattbucci

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Qwen3.8-27B-AWQ

mattbucci

4-bit AWQ quantization of Qwen/Qwen3.8-27B, calibrated in-house on 2×RTX 3090 (Ampere, sm_86) for **single-user 256K-context serving under SGLang**. Thinking, image, and **video** are all preserved and probe-verified. Weights are ~18.7 GB, so the model fits two 24 GB cards at TP=2 with room for a deep KV pool (697K tokens measured at `--max-running 1`). Single-user decode throughput (M=1, 100 output tokens, depth server-verified via `actual_input_tokens`):

Visual QA

Input Text · Image→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 5

Devstral-Small-2-24B-AWQ

gemma-4-12B-AWQ

gemma-4-26B-AWQ

gemma-4-31B-it-AutoRound-AWQ

Qwen3.8-27B-AWQ

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