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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 19
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查看详情 ↗Flint-UD-Q4_K_XL-NInfer-v3
WaveCut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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价格待核验多模态与渠道费用须分别核对
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价格待核验多模态与渠道费用须分别核对
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查看详情 ↗A NInfer **v3** artifact of huihui-ai/Huihui-Qwen3.8-27B-abliterated, the abliterated Qwen3.8-27B with the lowest reported divergence from the base checkpoint among the widely used releases (the first fifteen layers are untouched; independent KL divergence measurements sit around 0.008) and a working MTP head. Built for NInfer-all, the `master` branch of iamwavecut/ninfer-all: ashalliants' NInfer-3090 consolidated with the fixes and features of the other NInfer forks. It serves the RTX 3090, RTX 4090, RTX 5090 and RTX PRO 6000 Blackwell, each with its measured kernel route profile built in (other GPUs calibrate once at first start). A single RTX 3090 serves a 198,400-token window with Vision and MTP. From the Docker image (an NVIDIA driver of the CUDA 13 branch, 580 or newer, and the NVIDIA Container Toolkit):
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查看详情 ↗Nanbeige4.2-3B-heretic-GGUF
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价格待核验多模态与渠道费用须分别核对
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查看详情 ↗A NInfer **v3** artifact of ISTA-DASLab's Qwen3.8-27B GSQ-RCO IQ3_S (3.5 bits per weight) for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all, which serves the RTX 3090, RTX 4090, RTX 5090 and RTX PRO 6000 Blackwell. The GGUF assigns a ggml quantization type to every tensor; this artifact keeps each tensor's ggml blocks byte for byte, so nothing is requantized, and the engine multiplies the blocks in place. Stock NInfer builds refuse this file: the `gguf_*` formats exist only in that line, from commit `8706bfc0` on. One `.ninfer` file of 15,017,456,896 bytes (13.99 GiB; 10.95 GiB of text weights are resident without Vision and drafters), next to its conversion report, `SHA256SUMS` and `NOTICE`. A single stream with MTP from the Docker image (an NVIDIA driver of the CUDA 13 branch, 580 or newer, and the NVIDIA Container Toolkit):
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查看详情 ↗A single-file NInfer **v3** artifact carrying the community uncensored tune HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF as the target model and the official z-lab/Qwen3.8-27B-DFlash2 draft model as the DFlash2 speculative companion (recipe `qwen3_8_27b-v2`, the first NInfer recipe with the DFlash2 payload). The native NextN/MTP head of the tune is preserved as well, so both `--spec mtp` and `--spec dflash2` work. It runs on **NInfer-all**, the `master` branch of iamwavecut/ninfer-all, which serves the RTX 3090, RTX 4090, RTX 5090 and RTX PRO 6000 Blackwell. Its first (v2) release ran on stock NInfer, which builds only for the RTX 5090; this v3 file was verified on NInfer-all. `--spec mtp --draft-tokens 3 --lm-head-draft` selects the tune's own NextN head instead. Vision (`--vision`) is the official tower: the release's BF16 projector reproduces it bit-for-bit through the FP16 export path, so the artifact carries the official BF16 vision words. **Hardware.** Upstream NInfer targets one RTX 5090 (`sm_120a`, 32 GB). NInfer-all serves the same `qwen3.8-27b / groupwise-int` identity with DFlash2 on the RTX 3090, 4090, 5090 and RTX PRO 6000: its Docker image needs an NVIDIA driver of the CUDA 13 branch (580 or newer) and the NVIDIA Container Toolkit, and a build (its README) sets `CMAKE_CUDA_ARCHITECTURES` to `86`, `89` or `120a`. The speed of the official artifact of this identity on each card is in its reference measurements, this tune itself was not measured there. On 24 GB cards the official artifact of the same size fits DFlash2 with five drafts beside 258,048 tokens of `rk4v4` KV on the 3090 and 245,760 on the 4090 (`--kv-dtype rk4v4 --gdn-state-fp16 --spec dflash2 --draft-tokens 5`).
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查看详情 ↗A NInfer **v3** artifact of ISTA-DASLab's Qwen3.8-Flash-Next GSQ-RCO IQ3_S (3.50 bits per weight over the transformer weights) for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all. Qwen3.8-Flash-Next is a 125B-parameter mixture of experts: 48 layers of 512 routed experts, 10 of them active per token, about 6B active parameters. The GGUF assigns a ggml quantization type to every tensor; this artifact keeps each tensor's ggml blocks byte for byte (the hyper-connection projections excepted: they are re-quantized from BF16 to Q8_0), and the engine multiplies the blocks in place. IQ3_S is the release the GSQ-RCO card recommends: the strongest of the four. Stock NInfer builds refuse these files: the model family and the `gguf_*` formats exist only in that line; build `master` from commit `5395ec30b` on. NInfer serves it with up to eight concurrent requests, prompt-prefix reuse, structured output and its Vision tower; This file includes the MTP block. Enable drafting with `--spec mtp --draft-tokens 4`. One file, `Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-ninfer-v3.ninfer`, of 57,902,944,256 bytes (53.93 GiB), next to its conversion report, `SHA256SUMS`, `NOTICE` and `LICENSE`. Original text/Vision conversion command, before the MTP attachment:
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查看详情 ↗A NInfer **v3** artifact of ISTA-DASLab's Qwen3.8-Flash-Next GSQ-RCO Q2_0 (2.40 bits per weight over the transformer weights) for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all. Qwen3.8-Flash-Next is a 125B-parameter mixture of experts: 48 layers of 512 routed experts, 10 of them active per token, about 6B active parameters. The GGUF assigns a ggml quantization type to every tensor; this artifact keeps each tensor's ggml blocks byte for byte (the hyper-connection projections excepted: they are re-quantized from BF16 to Q8_0), and the engine multiplies the blocks in place. Q2_0 is the release built for speed: it avoids the lookup-table formats. Stock NInfer builds refuse these files: the model family and the `gguf_*` formats exist only in that line; build `master` from commit `5395ec30b` on. NInfer serves it with up to eight concurrent requests, prompt-prefix reuse, structured output and its Vision tower; This file includes the MTP block. Enable drafting with `--spec mtp --draft-tokens 4`. One file, `Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-ninfer-v3.ninfer`, of 40,709,159,936 bytes (37.91 GiB), next to its conversion report, `SHA256SUMS`, `NOTICE` and `LICENSE`. Original text/Vision conversion command, before the MTP attachment:
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗The n-gram table of Qwen3.8-Flash-Next's per-layer embedding (PLE), as a NInfer **v3** artifact for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all. Every NInfer conversion of Qwen3.8-Flash-Next published without its table reads its rows from this file: Stock NInfer builds cannot read it: the model family and the `gguf_*` formats exist only in that line; build `master` from commit `82f120de` on. The table holds 320,001,536 rows of 160 values (51.2B parameters), 16 hash heads over the 2- and 3-token n-grams of the context, each head's rows at its own offset. Every token reads the 16 rows its n-grams address. The rows are ISTA-DASLab's IQ4_NL blocks (`per_layer_token_embd` of their GGUF releases, 90 bytes a row), copied byte for byte: the second shard of every GSQ-RCO release, the Coder build's included, is the same file.
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查看详情 ↗Qwythos-9B-v2-Heretic-GGUF
WaveCut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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗A NInfer **v3** artifact of PrismML's Ternary Bonsai 2 27B for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all, which serves the RTX 3090, RTX 4090, RTX 5090 and RTX PRO 6000 Blackwell. Stock NInfer builds refuse this file (`t2_g128_fp16` and the `hadamard_signs` Use auxiliary exist only in that line). The numbers below come from `master` at `2172a598` (September 2026), each card running the device route profile built into it. Every profile of this revision runs there; older engines are covered at the end. With `--lm-head-draft` the drafters score their candidates on the proposal head instead of the full one. MTP decodes 4% faster through it and accepts the same share of its drafts; DFlash2 gains nothing measurable, so its profiles leave the flag off. The head is loaded only with the flag.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。