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Can the NVIDIA GeForce RTX 4070 SUPER run SmolLM2 360M Instruct?

47 ptsUsable (C) · 47 of 100 points: Model quality 3.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15

Yes, it runs well

Runs well. At F16 it needs about 2.3 GB of 12.0 GB usable, with an estimated speed of 403 tok/s.

Best quant
F16
Generation speed
403 tok/s
Prompt processing
71,000 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

Download SmolLM2 360M Instruct

Recommended for the NVIDIA GeForce RTX 4070 SUPER: 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.

ollama run smollm2:360m-instruct-fp16

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

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 7 days ago against Hugging Face, Ollama and GitHub.

How the C grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.3.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.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.4.3 / 15
Total47.9 / 10047 ptsUsable (C) · 47 of 100 points: Model quality 3.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 1.7 GB 12.0 GB 2,822 tok/s Runs well 45 ptsUsable (C) · 45 of 100 points: Model quality 2.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.2/15
Q3_K_M 1.7 GB 12.0 GB 2,222 tok/s Runs well 46 ptsUsable (C) · 46 of 100 points: Model quality 3.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.3/15
Q4_K_M 1.8 GB 12.0 GB 1,719 tok/s Runs well 46 ptsUsable (C) · 46 of 100 points: Model quality 3.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.4/15
Q5_K_M 1.8 GB 12.0 GB 1,420 tok/s Runs well 46 ptsUsable (C) · 46 of 100 points: Model quality 3.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.4/15
Q6_K 1.8 GB 12.0 GB 1,192 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 3.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.5/15
Q8_0 1.9 GB 12.0 GB 875 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 3.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.7/15 GGUF (opens in a new tab)
F16 2.3 GB 12.0 GB 403 tok/s Runs well 47 ptsUsable (C) · 47 of 100 points: Model quality 3.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15 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.