How much VRAM does Kimi K2.6 (MoE) need?

Kimi K2.6 (MoE) 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 Kimi K2.6 (MoE) on a consumer GPU?

Kimi K2.6 (MoE) is a 1000B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is Kimi K2.6 (MoE)'s context window?

Kimi K2.6 (MoE) supports a maximum context window of 262,144 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 Kimi K2.6 (MoE)?

For Kimi K2.6 (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Kimi K2.6 (MoE) compare to similar models?

Kimi K2.6 (MoE) is a 1.0T MoE model from the Kimi family. Browse other Kimi 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.

KimiMoEMIT

Kimi K2.6 (MoE)

1.0T

Parameters

32.0B

Active

256K

Max Context

MoE

Architecture

—

Released

Text + Vision

Modality

About Kimi K2.6 (MoE)

Kimi K2.6 (MoE) is a mixture-of-experts (MoE) transformer language model from the Kimi family, containing 1000B parameters across 61 layers. It has 1000B total parameters loaded into VRAM with 32B active per token. It supports up to 262K tokens of context with a hidden dimension of 7168 and 8 KV heads for efficient grouped-query attention (GQA). Modified MIT. MoE: 384 experts, 8+1 active. MLA for KV compression. Multimodal (MoonViT 400M). Server class. 1T total params.

ResearchEnterprise

Technical Specifications

Total Parameters1.0T

Active Parameters32.0B per token

ArchitectureMixture of Experts

Total Experts32

Attention TypeGQA (MoE)

Hidden Dimensiond = 7,168

Transformer Layers61

Attention Heads64

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	195K ctx	256K ctx
Q4_K_M0.50 B/W ~97% of FP16	517.1Cluster / Multi-GPU	563.4Cluster / Multi-GPU	577.9Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	1034.0Cluster / Multi-GPU	1080.3Cluster / Multi-GPU	1094.8Cluster / Multi-GPU
F162.00 B/W Reference	2067.8Cluster / Multi-GPU	2114.1Cluster / Multi-GPU	2128.5Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Find the right GPU for Kimi K2.6 (MoE)

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