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Can the AMD Instinct MI350X run Snowflake Arctic Instruct?

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.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q3_K_M it needs about 250 GB of 288 GB usable, with an estimated speed of 209 tok/s.

Best quant
Q3_K_M
Generation speed
209 tok/s
Prompt processing
4,996 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Best app to run it on the AMD Instinct MI350X

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/Snowflake/snowflake-arctic-instruct

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/Snowflake/snowflake-arctic-instruct

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Snowflake Arctic Instruct

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 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 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.
How capable the model itself is at Q3_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welltight fit
How well it runs on the AMD Instinct MI350X: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.18.9 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score72.9 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Instinct MI350X

Quant Memory needed Available Speed Runs Quality Download
Q2_K 207 GB 288 GB 230 tok/s Runs great 35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
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. All files (opens in a new tab)
Q4_K_M 308 GB 288 GB 52.3 tok/s Runs welloffloads to RAM 47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 355 GB 288 GB — Won't run 48Fair quality (C) · 48 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 408 GB 288 GB — Won't run 49Fair quality (C) · 49 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 523 GB 288 GB — Won't run 50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 973 GB 288 GB — Won't run 50Fair quality (C) · 50 of 100: how capable the model is at F16, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

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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