SmolLM 135M Instruct on Apple iPhone 17 Pro / 17 Pro Max

Can the Apple iPhone 17 Pro / 17 Pro Max run SmolLM 135M Instruct?

Yes, it runs great

Runs great. At F16 it needs about 0.9 GB of 7.2 GB usable, with an estimated speed of 175 tok/s.

Best quant: F16
Generation speed: 175 tok/s
Prompt processing: 14,800 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 2K

Best app to run it on the Apple iPhone 17 Pro / 17 Pro Max

On iOS

PocketPal AI

Mobile app · runs on llama.cpp

Runs the model on the device itself, offline: download it once in the app.

Get it on the App Store Then download SmolLM 135M Instruct in the app

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 SmolLM 135M Instruct

On a phone, tablet or other mobile device you install a chat app and get the model file through it, so there are no commands to copy. Use SmolLM-135M-Instruct.Q4_K_M.gguf, the largest single file that fits (mobile apps can’t open a model split into parts).

On iPhone or iPad

PocketPal AI

Free mobile app · runs the model on the device with llama.cpp

Get it on the App Store

Install PocketPal AI (free) and open it.
Tap the menu (☰), then Models, then + and Add from Hugging Face.
Search for QuantFactory/SmolLM-135M-Instruct-GGUF, open it and download SmolLM-135M-Instruct.Q4_K_M.gguf.
Tap Load next to the model and start a chat. It runs on the device, with no connection needed from then on.

Files and commands for a computer

Recommended for the Apple iPhone 17 Pro / 17 Pro Max: 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 smollm:135m-instruct-v0.2-fp16

Quantized files · GGUF

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

huggingface.co/QuantFactory/SmolLM-135M-Instruct-GGUF (opens in a new tab)

ollama run hf.co/QuantFactory/SmolLM-135M-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 43/100 (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 Apple iPhone 17 Pro / 17 Pro Max: 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.	0.9 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; 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.	2.9 / 15
Match score	43.8 / 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 Apple iPhone 17 Pro / 17 Pro Max

Quant	Memory needed	Available	Speed	Runs	Quality	File to pick in the app
`Q2_K`	0.7 GB	7.2 GB	1,224 tok/s	Runs great	1Basic quality (D) · 1 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	`Q2_K`
`Q3_K_M`	0.7 GB	7.2 GB	964 tok/s	Runs great	1Basic quality (D) · 1 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	`Q3_K_M`
`Q4_K_M`	0.7 GB	7.2 GB	745 tok/s	Runs great	1Basic quality (D) · 1 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	`Q4_K_M`
`Q5_K_M`	0.7 GB	7.2 GB	616 tok/s	Runs great	1Basic quality (D) · 1 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	`Q5_K_M`
`Q6_K`	0.8 GB	7.2 GB	517 tok/s	Runs great	1Basic quality (D) · 1 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	`Q6_K`
`Q8_0`	0.8 GB	7.2 GB	379 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	`Q8_0`
`F16`	0.9 GB	7.2 GB	175 tok/s	Runs great	2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	—

Memory figures assume 2K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 130 tok/s · Runs great
2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) F16 · 229 tok/s · Runs great
2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 603 tok/s · Runs great
2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (24 GB) F16 · 229 tok/s · Runs great
2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 521 tok/s · Runs great
2Basic quality (D) · 2 of 100: how capable the model is at F16, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 772 tok/s · Runs great

Quick answers

How much memory does SmolLM 135M Instruct need on the iPhone 17 Pro?

At F16 it needs about 0.9 GB. The iPhone 17 Pro has 7.2 GB usable.

How fast is SmolLM 135M Instruct on the iPhone 17 Pro?

About 175 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of SmolLM 135M Instruct should I use on the iPhone 17 Pro?

F16. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.