How much VRAM does Yi-Coder 9B need?

Yi-Coder 9B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Yi-Coder 9B on a consumer GPU?

Yes, Yi-Coder 9B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 16 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Yi-Coder 9B's context window?

Yi-Coder 9B supports a maximum context window of 32,768 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Yi-Coder 9B?

For Yi-Coder 9B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Yi-Coder 9B compare to similar models?

Yi-Coder 9B is a 9.0B Dense model from the Yi family. Browse other Yi models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

YiDenseApache 2.0

Yi-Coder 9B

Yi-Coder 9B is a dense transformer language model from the Yi family, containing 9B parameters across 40 layers. It supports up to 33K tokens of context with a hidden dimension of 4096 and 8 KV heads for efficient grouped-query attention (G…

9.0B

Parameters

32K

Max Context

Dense

Architecture

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Released

Text

Modality

About Yi-Coder 9B

Code

Technical Specifications

Total Parameters9.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers40

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	4.81Consumer GPU	9.65Consumer GPU
Q8_01.00 B/W ~100% of FP16	9.46Consumer GPU	14.30Consumer GPU
F162.00 B/W Reference	18.76Consumer GPU	23.61Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Find the right GPU for Yi-Coder 9B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide