Qwen 3.5 122B-A10B

Overview

Alibaba's mid-flagship Qwen 3.5 with 122B total / 10B active params and 262k native context. Frontier-class quality that fits on a single H100.

When to pick this model

• Frontier-quality inference on a single H100
• Long-context document and codebase analysis (262k)
• Multilingual reasoning workloads
• Apache 2.0 deployments where Qwen 397B is overkill
• Cost-sensitive agentic systems needing top-tier quality

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	73 GB
Q5_K_M	88 GB
Q8_0	131 GB
FP16 (no quantization)	244 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• Frontier-class quality with only 10B active params
• 262k native context window
• Apache 2.0
• Single-H100 deployment is realistic
• Strong multilingual coverage

Limitations

• Roughly 73 GB VRAM in Q4 — still needs multi-GPU on consumer cards
• Mid-flagship positioning means it's eclipsed by 397B on the hardest tasks

Architecture & training

Architecture: MoE · 122B total / 10B active · Qwen 3.5 flagship · 262k context

Training: Qwen 3.5 accessible flagship — 10B active out of 122B, native 262k ctx.

Quick start

ollama run qwen3.5:122b-a10b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.