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Can the AMD EPYC 7002 / 7003 series NAS (16 GB) run SmolLM2 360M Instruct?

8Basic quality (D) · 8 of 100: how capable the model is at F16, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 2.6 GB of 12.0 GB usable, with an estimated speed of 91.0 tok/s.

Best quant
F16
Generation speed
91.0 tok/s
Prompt processing
1,111 tok/s
First token (1K prompt)
0.9 s
Largest safe context
8K

Best app to run it on the AMD EPYC 7002 / 7003 series NAS (16 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

  • One command with Ollama’s own build of this model.
  • Serves an OpenAI-compatible API that other apps and coding tools can use.
ollama run smollm2:360m-instruct-fp16

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download SmolLM2 360M Instruct

Recommended for the AMD EPYC 7002 / 7003 series NAS (16 GB): F16. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run smollm2:360m-instruct-fp16

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct-GGUF (opens in a new tab)

ollama run hf.co/HuggingFaceTB/SmolLM2-360M-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Hugging Face. The weights themselves download from Hugging Face.

github.com/huggingface/smollm (opens in a new tab)

git clone https://github.com/huggingface/smollm

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 8Basic quality (D) · 8 of 100: how capable the model is at F16, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, 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 great
How well it runs on the AMD EPYC 7002 / 7003 series NAS (16 GB): 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.3.6 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.25.0 / 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.5.0 / 15
Match score48.6 / 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 EPYC 7002 / 7003 series NAS (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 2.0 GB 12.0 GB 637 tok/s Runs great 5Basic quality (D) · 5 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 2.1 GB 12.0 GB 502 tok/s Runs great 6Basic quality (D) · 6 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
Q4_K_M 2.1 GB 12.0 GB 388 tok/s Runs great 7Basic quality (D) · 7 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
Q5_K_M 2.2 GB 12.0 GB 321 tok/s Runs great 7Basic quality (D) · 7 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
Q6_K 2.2 GB 12.0 GB 269 tok/s Runs great 7Basic quality (D) · 7 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 2.3 GB 12.0 GB 198 tok/s Runs great 8Basic quality (D) · 8 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 2.6 GB 12.0 GB 91.0 tok/s Runs great 8Basic quality (D) · 8 of 100: how capable the model is at F16, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K 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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