The RadixArk GLM-5.3-Flash-NVFP4 model is a quantized version of zai-org/GLM-5.3-Flash-BF16, pinned to source revision `a6c167b6`. RadixArk converted the 300B-class hybrid Mixture-of-Experts model to mixed-precision NVFP4 W4A4 using NVIDIA Model Optimizer. The conversion uses plain abs-max scaling with 256 tensor-scale normalization. **Run on SGLang**: use the launch command below and see the GLM-5.3-Flash cookbook for deployment guidance. This model is not owned or developed by RadixArk. It is a quantized derivative of Z.ai's model; see the upstream GLM-5.3-Flash-BF16 model card for the source model's capabilities, training information, and license.
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View details ↗The RadixArk GLM-5.3-NVFP4 model is the quantized version of zai-org/GLM-5.3-BF16. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following an expert-only NVFP4 W4A4 recipe. **Run on SGLang**: launch command and per-platform recipes in the GLM-5.3 cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Z.ai's model; see the upstream GLM-5.3 model card for the source model's capabilities, training information, limitations, and license.
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View details ↗A DSpark speculator for the Inkling-Small NVFP4 target, enabling faster inference through speculative decoding. DSpark extends the DFlash parallel-draft backbone with a Markov logit-bias head and a per-position confidence head. This checkpoint was trained with SpecForge using hidden states from a live SGLang target engine. **Preview:** This is a research preview. Official training and evaluation are in progress. - **Base model:** `thinkingmachines/Inkling-Small-NVFP4`; the exact matching target checkpoint and tokenizer are required. - **Format:** Safetensors (single-file BF16, 0.90B trainable parameters; draft weights only). - **Draft:** 5 layers (Qwen3-style GQA), hidden 4096, 64 heads / 16 KV heads, head_dim 64, FFN 8192, rope_theta 8000000, `block_size=15`. - **RoPE:** YaRN, factor 128, `original_max_position_embeddings=8192`, `max_position_embeddings=1048576` (matches the target addressable range); config carries both `rope_scaling` and `rope_parameters` schemas. - **Vocabulary:** 200,058 tokenizer entries and 201,024 padded weight rows; `mask_token_id=200064`. - **DSpark heads:** Markov rank 256 (vanilla) and confidence head (with-Markov). - **Aux hidden-state layers:** `[5, 11, 23, 29, 35]`. - **Trained context:** 8,192 native; long-context adaptation with sequences up to 65,536. - **Target weights:** target embedding and unembedding weights are not included in this checkpoint.
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View details ↗A long-context DSpark speculator for Kimi K3. It supports context lengths of up to 1 million tokens. A DSpark speculator for the Kimi K3 target, enabling faster inference through speculative decoding. DSpark extends the DFlash parallel-draft backbone with a Markov logit-bias head and a per-position confidence head. This checkpoint was trained with SpecForge using hidden states from a live SGLang target engine. - **Base model:** `moonshotai/Kimi-K3` - **Format:** Safetensors (single-file BF16, 2,249,289,601 parameters) - **Draft:** 5 full-attention Qwen3-style GQA layers, hidden size 7168, 64 query heads / 16 KV heads, and `block_size=7` - **Verification width:** 1 current token + 7 draft tokens - **Auxiliary target layers:** `[7, 23, 51, 67, 83]` - **Trained context:** 65,536 tokens - **Target weights:** embedding and unembedding weights are not included
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View details ↗The vendor-recipe quantized checkpoint of **Muse Glimmer**, packed for serving with SGLang. This is the model vendor's own `fp_awq` mixed-precision recipe (`MIXED_PRECISION`): **NVFP4** (E2M1, group 16) on most projections, **MXFP8** (E4M3, group 32) on the higher-sensitivity set (`v_proj`, `down_proj`, `lm_head`), norms in bf16 — 18.3 GiB versus 52 GiB for the bf16 export. Grab it and serve it directly by repo id; no conversion step needed. - **Base model:** `meta-models/Muse-Glimmer-30B` (Muse Glimmer bf16 export) - **Format:** Safetensors, 5 shards, 18.34 GiB - **Recipe:** vendor `fp_awq` hand-off packed with the SGLang fork's `convert_fp_awq_to_hf.py` (default flags — the recipe as shipped); quantization map in `hf_quant_config.json` (`quant_algo: MIXED_PRECISION`) - **Language model:** 52 layers, hidden 6656, 32 Q / 2 KV heads, vocab 202,048 - **Text-only serving** is recommended (`--language-model-only`); the vision tower ships in the base export, not in this quantized checkpoint Notes: decode is memory-bound, so the speedup tracks the 52→18 GiB weight reduction. On GB10, quantized *prefill* is slower than bf16 at batch ≥ 4 (~650 vs ~1500 input tok/s) — decode-heavy workloads win, prefill-heavy workloads should measure. Correctness smoke-verified (greedy arithmetic and generation with correct stop tokens); full accuracy suite pending.
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View details ↗A DSpark speculative-decoding draft model for Qwen3.8-27B target models, trained with SpecForge and served with SGLang. The checkpoint has been evaluated with both RadixArk/Qwen3.8-27B-NVFP4 and Qwen/Qwen3.8-27B-FP8 targets. The acceptance-length evaluation below uses the NVFP4 target. The throughput evaluation uses the FP8 target. - Draft parameters: 1,857,358,337 (1.86B) - Draft weight dtype: BF16 - Hidden size: 5,120 - Transformer layers: five full-attention layers - Attention: GQA with 32 query heads and eight key/value heads - Target auxiliary feature layers: 5, 19, 33, 47, 61 - Markov head: VanillaMarkov, rank 256 - Training target width: 16 future positions - Serving gamma: seven draft proposals - Target verification width: eight tokens, including the target bonus token - Maximum position embeddings: 262,144
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View details ↗The RadixArk Qwen3.8-27B-NVFP4 model is the quantized version of Qwen/Qwen3.8-27B. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following a mixed NVFP4 W4A4 recipe. **Run on SGLang**: launch command and per-platform recipes in the Qwen3.8-27B cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license.
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View details ↗The RadixArk Qwen3.8-27B-NVFP4-BF16-LMHead model is a variant of RadixArk/Qwen3.8-27B-NVFP4, the quantized version of Qwen/Qwen3.8-27B produced at RadixArk using NVIDIA Model Optimizer with a mixed NVFP4 W4A4 recipe. The only difference from the source checkpoint is that `lm_head` is **not** quantized: it uses the original BF16 `lm_head` weights from Qwen/Qwen3.8-27B instead of the NVFP4 W4A4 quantized ones. All other tensors are identical to the source checkpoint. **Run on SGLang**: launch command and per-platform recipes in the Qwen3.8-27B cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license.
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View details ↗RadixArk Qwen3.8-Flash-Next-NVFP4 is the quantized version of Qwen/Qwen3.8-Flash-Next, a hybrid-architecture multimodal Mixture-of-Experts model. Quantization was performed with NVIDIA Model Optimizer (snapshot `87c9f8cf`) using the NVFP4 W4A4 recipe, scoped to the routed experts only. See the source model for license terms. Developers evaluating NVFP4-quantized serving of Qwen3.8-Flash-Next for agentic systems, chat, coding, and multimodal reasoning workloads.
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