RadixArk/Qwen3.8-2.4T-A95B-NVFP4

Model Overview

Description:

The RadixArk Qwen3.8-2.4T-A95B-NVFP4 model is the quantized version of Qwen/Qwen3.8-2.4T-A95B, a causal language model with 2.4T total parameters and 95B activated parameters. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following an experts-only NVFP4 recipe.

Third-Party Community Consideration

This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-2.4T-A95B model card for the source model's capabilities, training information, limitations, and license.

License/Terms of Use:

Qwen3.8-Max license

Deployment Geography:

Global

Use Case:

Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

Release Date:

Hugging Face 08/07/2026 via https://huggingface.co/RadixArk/Qwen3.8-2.4T-A95B-NVFP4

Model Architecture:

Architecture Type: Transformers (Mixture-of-Experts)
Network Architecture: Qwen3.8-2.4T-A95B
Number of Model Parameters: 2.4T total, 95B activated

Input:

Input Type(s): Text
Input Format(s): String
Other Properties Related to Input: Native context length up to 262,144 tokens.

Output:

Output Type(s): Text
Output Format: String

Software Integration:

Supported Runtime Engine(s):

• SGLang
  

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell (this checkpoint was produced and validated on B300 SXM6)
  

Preferred Operating System(s):

• Linux
  

Model Version(s):

Quantized with NVIDIA Model Optimizer, commit 87c9f8cf83021957d1a1a575c90c9a4eaaf7ef0c.

Training, Testing, and Evaluation Datasets:

Calibration Data:

The conversion used deterministic synthetic token calibration. See CONVERSION.md for the complete conversion and validation contract.

Training Dataset:

RadixArk did not train or fine-tune this checkpoint. Training information is inherited from the upstream Qwen3.8-2.4T-A95B model card.

Evaluation Dataset:

The model was evaluated on GSM8K, AIME 2026, SWE-bench Verified, and Terminal-Bench 2.1.

Post Training Quantization

This model was obtained by quantizing Qwen/Qwen3.8-2.4T-A95B with an experts-only NVFP4 recipe. Only linear operators in the routed-expert MLP blocks are quantized; other components retain the source checkpoint precision. See CONVERSION.md for tensor-level details.

Usage

The recommended SGLang configuration for a single 8×B300 node is:

export SGLANG_FLASHINFER_MNNVL_CUTEDSL_AR_FUSION=1
sglang server \
  --trust-remote-code \
  --model-path RadixArk/Qwen3.8-2.4T-A95B-NVFP4 \
  --tp-size 8 \
  --mamba-ssm-dtype bfloat16 \
  --moe-runner-backend flashinfer_trtllm \
  --mamba-radix-cache-strategy extra_buffer \
  --speculative-algorithm NEXTN \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-linear-replayssm-spec \
  --mem-fraction-static 0.90 \
  --chunked-prefill-size 8192 \
  --max-prefill-tokens 8192 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --host 0.0.0.0 \
  --port 30000

For other deployment topologies and hardware-specific configurations, see the SGLang Qwen3.8 cookbook.

This checkpoint is text-only and requires thinking mode. Multimodal inputs and enable_thinking=false are not supported.

Evaluation

The benchmark results below were produced with this NVFP4 checkpoint on an 8×B300 TP8 SGLang deployment.

Benchmark	Evaluation protocol	Score
GSM8K	Full 1,319-example split, sgl-eval	97.73%
AIME 2026	30 problems × 16 repeats, sgl-eval	98.75% pass@1; 100% pass@16
SWE-bench Verified	500 instances, agentic evaluation with unbounded steps	70.6% pass@1 (353/500)
Terminal-Bench 2.1	89 tasks, Claude Code agent	87.64% pass@1 (78/89)

GSM8K used zero-shot evaluation with recommended sampling (temperature=1.0, top_p=0.95). AIME 2026 used 16 independent samples per problem, for 480 generations in total. SWE-bench Verified used the official test split and the SWE-bench harness to score submitted patches. For Terminal-Bench 2.1, Claude Code multimodal requests were disabled.

Model Limitations:

The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

Ethical Considerations

RadixArk believes Trustworthy AI is a shared responsibility and we follow established policies and practices that enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.