LLMs ·

Snowflake Arctic Instruct

Parameters
479B (17.0B active)
Architecture
Mixture of experts
Max context
4K tokens
License
apache-2.0
Released
2024-04

How to run it

Original weights · Hugging Face

Full-precision files from Snowflake, for fine-tuning, vLLM, Transformers.

huggingface.co/Snowflake/snowflake-arctic-instruct (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Snowflake Arctic Instruct

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, AMD Radeon

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App Type Opens it as Systems Engine Start
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve Snowflake/snowflake-arctic-instruct
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path Snowflake/snowflake-arctic-instruct
Docker Model Runner CLI + API Original weights macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/Snowflake/snowflake-arctic-instruct
Text Generation WebUI Desktop app Original weights macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization Quality Weights Total at 4K context Download
Q2_K Low 191 GB 193 GB All files (opens in a new tab)
Q3_K_M Low 235 GB 236 GB All files (opens in a new tab)
Q4_K_M Good 292 GB 293 GB All files (opens in a new tab)
Q5_K_M High 340 GB 341 GB All files (opens in a new tab)
Q6_K High 392 GB 394 GB All files (opens in a new tab)
Q8_0 Very high 507 GB 508 GB All files (opens in a new tab)
F16 Full 957 GB 958 GB Original weights (opens in a new tab)

Best hardware for Snowflake Arctic Instruct

Quality Hardware Quant Memory Speed Runs
47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2025) (M4 Max or M3 Ultra)Apple M3 Ultra (512 GB) Q4_K_M 324 GB / 384 GB 43.5 tok/s Runs great
35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2025) (M4 Max or M3 Ultra)Apple M3 Ultra (256 GB) Q2_K 208 GB / 192 GB 56.4 tok/s Runs wellover GPU memory limit
47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (M5 Max or M5 Ultra)Apple M5 Ultra (512 GB) Q4_K_M 324 GB / 384 GB 59.6 tok/s Runs great
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325XAMD Instinct MI325X Q3_K_M 248 GB / 256 GB 188 tok/s Runs welltight fit
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI350XAMD Instinct MI350X Q3_K_M 250 GB / 288 GB 209 tok/s Runs welltight fit
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Dell Pro Max with GB300NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip Q3_K_M 248 GB / 252 GB 211 tok/s Runs welltight fit
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. QNAP QAI-h1290FXAMD EPYC 7002 / 7003 series NAS (512 GB) Q3_K_M 273 GB / 471 GB 10.3 tok/s Runs well
35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. QNAP QAI-h1290FXAMD EPYC 7002 / 7003 series NAS (256 GB) Q2_K 211 GB / 230 GB 12.9 tok/s Runs welltight fit
35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI300XAMD Instinct MI300X Q2_K 202 GB / 192 GB 86.0 tok/s Runs welloffloads to RAM
35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (M5 Max or M5 Ultra)Apple M5 Ultra (256 GB) Q2_K 208 GB / 192 GB 75.3 tok/s Runs wellover GPU memory limit

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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