Qwen3.8 2.4T A95B
Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows
Qwen3.8 2.4T A95B
Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows
- • FP16 Weights = 95.0B × 2B = 190.00 GB
- • INT4 Weights = 95.0B × 0.55B = 52.25 GB
- • KV Cache (262144 ctx, FP16) ≈ 160.00 GB
- • Activation Buffer = ~20% overhead
Hardware & Execution ParametersCode
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of Qwen3.8 2.4T A95B by Alibaba Cloud, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
An advanced dense model specializing in Code & Math synthesis, powered by a vast 152k multi-lingual tokenization vocabulary.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding.
LLM Hardware Sizing & Serving
For open deployments via vLLM/SGLang, quantization (INT4/AWQ) is heavily recommended to fit dense memory constraints, or multi-GPU pipeline parallelism for full FP16.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying Qwen3.8 2.4T A95B.
Key Architectural Strengths
- Specialized Code & Math synthesis backed by a highly efficient 152k multi-lingual tokenization vocabulary.
- Massive 262,144-token context allows full-repository and book-length ingestion.
Operational Considerations
- Extensive vocabulary embedding tables increase static parameter VRAM overhead before KV cache allocation.
- 128k+ token prefill stages become heavily compute-bound and balloon KV cache without PagedAttention chunking.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for Qwen3.8 2.4T A95B.
python3 -m vllm.entrypoints.openai.api_server --model Qwen3.8 2.4T A95B --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run qwen3.8 2.4t a95bpython3 -m sglang.launch_server --model-path Qwen3.8 2.4T A95B --tp 4 --trust-remote-codetext-generation-launcher --model-id Qwen3.8 2.4T A95B --num-shard 4 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~228.0 GB VRAM required
~114.0 GB VRAM (Hopper speedup)
~62.7 GB VRAM
CPU RAM / Apple Silicon optimized
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Deployment Tier | Pricing Structure |
|---|---|
| Open Checkpoint Weights | $0.00 (Free Download) |
| Inference Token Consumption | $0.00 / Token |
Comparable Foundation Architectures
Alternative models in the Code class with similar capabilities, context windows, or deployment profiles.
DeepSeek V4 Flash Vision Exp
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("ALIBABA_CLOUD_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="alibaba-qwen3.8-2.4t-a95b",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about Qwen3.8 2.4T A95B
Essential facts, architectural specs, hardware constraints, and pricing answers for Qwen3.8 2.4T A95B.
To run Qwen3.8 2.4T A95B (95B) locally, you generally need Depends on quantization. We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for Qwen3.8 2.4T A95B are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.