Yi 1.5 34B Chat

Overview

01.AI's dense 34B chat model under Apache 2.0, trained on 3.6T tokens with strong English-Chinese bilingual quality.

When to pick this model

• Chinese-English bilingual chat needing open weights
• Llama-compatible tooling pipelines at the 34B scale
• Research baselines from the 2024 dense-34B era
• Workloads where Apache 2.0 is mandatory at 34B
• Use cases where Qwen 2.5 32B isn't an option

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	20 GB
Q5_K_M	24 GB
Q8_0	36 GB
FP16 (no quantization)	68 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.

Published benchmark scores

Benchmark	Score
MMLU	77.2
HumanEval	75.2

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Excellent Chinese-language performance
• Compatible with Llama tooling and quantization
• Apache 2.0 license enables free commercial use
• Stable chat behavior and well-understood quirks

Limitations

• 4096-token context is severely limiting today
• Outclassed by Qwen 2.5 32B in 2025
• No multimodal or tool-use specialization

Architecture & training

Architecture: Dense Transformer · 34B · Yi 1.5 · Llama-compatible

Training: 01.AI — 3.1T multilingual EN/ZH tokens. Successor to Yi-34B.

Quick start

ollama run yi:34b

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