How much VRAM does Yi 1.5 34B Chat need?

Yi 1.5 34B Chat requires 29.5 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 15.8 GB.

What quantization should I use for Yi 1.5 34B Chat?

For the best quality, use Q6_K (29.5 GB VRAM). If your GPU has limited VRAM, Q3_K_M (15.8 GB) is the most efficient option with acceptable quality.

ChatReasoningMulti-Purpose34.4B

Yi 1.5 34B Chat

Yi · Apache 2.0

01.AI's 34B model with strong multilingual chat and reasoning.

Parameters: 34.4B
Context: 32,768 tokens
Released: 2024-05-13
Engines: llama.cpp, ollama, vLLM, TGI

Yi 1.5 34B Chat (34.4B) requires 29.5 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 19.5 GB VRAM. For the best experience, High-End Home AI Server ($3,842) is recommended.

— OwnRig methodology, data updated 2026-03-15

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	38 GB	34 GB
recommended	Q6_K	29.5 GB	26 GB
recommended	Q5_K_M	24.5 GB	21 GB
efficient	Q4_K_M	19.5 GB	17 GB
compressed	Q3_K_M	15.8 GB	13.5 GB

Context Length Impact

KV cache VRAM at Q6_K quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	512 MB	30 GBexceeds 24 GB
4K	1.1 GB	30.6 GBexceeds 24 GB
8K	2.2 GB	31.7 GBexceeds 24 GB
16K	4.4 GB	33.9 GBexceeds 24 GB
32K	8.7 GB	38.2 GBexceeds 24 GB

Compatible GPUs

Performance data for Yi 1.5 34B Chat across different hardware.

Device	Quantization	Speed	Rating	Fits in VRAM
NVIDIA GeForce RTX 4060 8GB	Q2_K	—	Not Viable	✗ (offload)
NVIDIA GeForce RTX 4070 Ti 12GB	Q2_K	—	Not Viable	✗ (offload)
NVIDIA GeForce RTX 3080 10GB	Q2_K	—	Not Viable	✗ (offload)
Apple M3 Pro (18GB Unified)	Q3_K_M	—	Not Viable	✗ (offload)

Frequently Asked Questions

How much VRAM does Yi 1.5 34B Chat need?: Yi 1.5 34B Chat requires 29.5 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 15.8 GB.
What quantization should I use for Yi 1.5 34B Chat?: For the best quality, use Q6_K (29.5 GB VRAM). If your GPU has limited VRAM, Q3_K_M (15.8 GB) is the most efficient option with acceptable quality.

Data confidence: estimated. Last updated: 2026-03-15. Source