Snowflake Arctic Instruct
By Snowflake · United States
Updated 2026-07-13
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.
In practice, Snowflake Arctic Instruct is server-class even at Q4_K_M (290 GB). Stepping up to Q8_0 nearly doubles the footprint to 510 GB, and unquantized FP16 weights take 960 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Snowflake Arctic Instruct needs roughly 340 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 5 tokens/sec on a mid-range card, and up to 15 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Snowflake Arctic Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Snowflake Arctic Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 290 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 290 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 290 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 290 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 290 GB at Q4_K_M |
Which GPU should you buy to run Snowflake Arctic Instruct?
To run Snowflake Arctic Instruct locally at Q4, you need ~290 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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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.
To put Snowflake Arctic Instruct in context: its MMLU score of 67.3 ranks #26 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 64.3 ranks #21 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
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
Typical workloads
In our catalog grid, Snowflake Arctic Instruct is filed under Apache MoE Reference, Research — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
Note the 4k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Hybrid Dense + MoE · 480B total / 17B active · 128 experts · Snowflake
Training: Snowflake — focus on enterprise SQL, code, analytical reasoning.
A historically important enterprise MoE, but the 4k context and infrastructure demands push it out of contention for new deployments.
Quick start
# Nécessite multi-GPU — non disponible via Ollama standardOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Snowflake Arctic Instruct need?
At the recommended Q4_K_M quantization, Snowflake Arctic Instruct needs about 290 GB of VRAM. Q8_0 takes 510 GB, and unquantized FP16 weights take 960 GB.
Can Snowflake Arctic Instruct run without a GPU?
Yes — with roughly 340 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Snowflake Arctic Instruct support?
Snowflake Arctic Instruct supports a 4k-token context window (4,096 tokens).
Can I use Snowflake Arctic Instruct commercially?
Yes. Snowflake Arctic Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Snowflake Arctic Instruct on consumer hardware?
Our compatibility engine estimates on the order of 5 tokens/sec on a mid-range GPU and up to 15 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Snowflake Arctic Instruct should I download first?
Start with Q4_K_M (290 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.
Is Snowflake Arctic Instruct the right pick for you?