How much VRAM does Snowflake Arctic (MoE) need?

Snowflake Arctic (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 Snowflake Arctic (MoE) on a consumer GPU?

Snowflake Arctic (MoE) is a 480B 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 Snowflake Arctic (MoE)'s context window?

Snowflake Arctic (MoE) 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 Snowflake Arctic (MoE)?

For Snowflake Arctic (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 Snowflake Arctic (MoE) compare to similar models?

Snowflake Arctic (MoE) is a 480.0B MoE model from the Snowflake family. Browse other Snowflake 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.

SnowflakeMoEApache 2.0

Snowflake Arctic (MoE)

480.0B

Parameters

17.0B

Active

32K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Snowflake Arctic (MoE)

Snowflake Arctic (MoE) is a mixture-of-experts (MoE) transformer language model from the Snowflake family, containing 480B parameters across 64 layers. It has 480B total parameters loaded into VRAM with 17B active per token. It supports up to 33K tokens of context with a hidden dimension of 7168 and 8 KV heads for efficient grouped-query attention (GQA). Apache 2.0. Enterprise SQL/coding MoE. Server class.

CodeEnterprise

Technical Specifications

Total Parameters480.0B

Active Parameters17.0B per token

ArchitectureMixture of Experts

Total Experts17

Attention TypeGQA (MoE)

Hidden Dimensiond = 7,168

Transformer Layers64

Attention Heads56

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	248.4Cluster / Multi-GPU	256.1Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	496.5Cluster / Multi-GPU	504.2Cluster / Multi-GPU
F162.00 B/W Reference	992.7Cluster / Multi-GPU	1000.4Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Find the right GPU for Snowflake Arctic (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