Snowflake Arctic Instruct

Overview

Snowflake's hybrid Dense-MoE with 17B active parameters out of 480B total. Apache-licensed and tuned for enterprise analytics, but the 4k context shows its age.

When to pick this model

• Enterprise SQL generation and analytical reasoning
• Workloads where 17B-active inference economics matter
• Research into Dense-MoE hybrid architectures
• Permissive-license deployments in data-warehouse stacks

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	290 GB
Q5_K_M	345 GB
Q8_0	510 GB
FP16 (no quantization)	960 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	67.3
HumanEval	64.3

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

Strengths

• Highly efficient inference for its 480B total size
• Strong on SQL and analytical tasks
• Apache 2.0 with no commercial restrictions
• Battle-tested in enterprise scenarios

Limitations

• Around 290 GB VRAM at Q4 — GPU cluster territory
• 4k context is severely limiting in 2026
• Outclassed by modern MoEs across most benchmarks

Architecture & training

Architecture: Hybrid Dense + MoE · 480B total / 17B active · 128 experts · Snowflake

Training: Snowflake — focus on enterprise SQL, code, analytical reasoning.

Quick start

# Nécessite multi-GPU — non disponible via Ollama standard

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