MODEL PROFILE

ThinkingCap-Qwen3.8-27B-abliterated-NVFP4A16

IstroSec · ThinkingCap-Qwen3.8-27B-abliterated-NVFP4A16

A 4-bit weight-only quantization (NVFP4 weights, bf16 activations) of ThinkingCap-Qwen3.8-27B-abliterated, the uncensored variant of bottlecapai/ThinkingCap-Qwen3.8-27B. 20.6 GB, down from 55.6 GB in bf16, at KL 0.0155 against the bf16 model. The weights fit a 24 GB GPU with a little room for KV cache; 32 GB and up is comfortable. vLLM serves it on Ampere and newer (sm80+) with a weight-only kernel: Marlin FP4 on most GPUs (Ampere, Ada, Hopper, RTX 50, DGX Spark) and a dedicated BF16×FP4 kernel on B200/B300. Activations stay bf16, so no GPU computes in FP4 here. **Which build?** The W4A16 build (19.5 GB, KL 0.0260) runs on the same GPUs (vLLM: Marlin, Machete on Hopper) and is 1.1 GB smaller; this build has the lower KL. Speed was not measured for either. With memory to spare, the FP8-BLOCK build (31.3 GB, KL 0.0046) has the lowest KL of the published builds; on Ada GPUs use FP8-DYNAMIC (31.3 GB, KL 0.0068) instead.

Visual QAlicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 17:26:33
OVERVIEW

Model overview

A 4-bit weight-only quantization (NVFP4 weights, bf16 activations) of ThinkingCap-Qwen3.8-27B-abliterated, the uncensored variant of bottlecapai/ThinkingCap-Qwen3.8-27B. 20.6 GB, down from 55.6 GB in bf16, at KL 0.0155 against the bf16 model. The weights fit a 24 GB GPU with a little room for KV cache; 32 GB and up is comfortable. vLLM serves it on Ampere and newer (sm80+) with a weight-only kernel: Marlin FP4 on most GPUs (Ampere, Ada, Hopper, RTX 50, DGX Spark) and a dedicated BF16×FP4 kernel on B200/B300. Activations stay bf16, so no GPU computes in FP4 here. **Which build?** The W4A16 build (19.5 GB, KL 0.0260) runs on the same GPUs (vLLM: Marlin, Machete on Hopper) and is 1.1 GB smaller; this build has the lower KL. Speed was not measured for either. With memory to spare, the FP8-BLOCK build (31.3 GB, KL 0.0046) has the lowest KL of the published builds; on Ada GPUs use FP8-DYNAMIC (31.3 GB, KL 0.0068) instead.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。