Can the OnePlus 15 (16 GB RAM) run K2 Horizon 7B?

Yes, it runs well

Runs well. At Q3_K_M it needs about 6.2 GB of 9.9 GB usable, with an estimated speed of 11.1 tok/s.

Best quant: Q3_K_M
Generation speed: 11.1 tok/s
Prompt processing: 30.6 tok/s
First token (1K prompt): 33.4 s
Largest safe context: 32K

Best app to run it on the OnePlus 15 (16 GB RAM)

On Android

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 Google Play Then download K2 Horizon 7B in the app

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

Want an agent? K2 Horizon 7B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 K2 Horizon 7B

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 K2-Horizon-7B-Q4_K_M.gguf, the largest single file that fits (mobile apps can’t open a model split into parts).

On Android

PocketPal AI

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

Get it on Google Play

Install PocketPal AI (free) and open it.
Tap the menu (☰), then Models, then + and Add from Hugging Face.
Search for IFM/K2-Horizon-7B-GGUF, open it and download K2-Horizon-7B-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.

Also works: ChatterUI

Files and commands for a computer

Quantized files · GGUF

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

huggingface.co/IFM/K2-Horizon-7B-GGUF (opens in a new tab)

ollama run hf.co/IFM/K2-Horizon-7B-GGUF

Original weights · Hugging Face

Full-precision files from Institute of Foundation Models, for fine-tuning, vLLM, Transformers.

huggingface.co/IFM/K2-Horizon-7B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Institute of Foundation Models. The weights themselves download from Hugging Face.

github.com/ifm-ai/xllm (opens in a new tab)

IFM’s official training code (xLLM) for the K2 Horizon family.

git clone https://github.com/ifm-ai/xllm

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

How it’s rated

Quality 67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 77/100 (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 well: How well it runs on the OnePlus 15 (16 GB RAM): 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.	30.2 / 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.	7.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	14.5 / 15
Match score	77.6 / 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 OnePlus 15 (16 GB RAM)

Quant	Memory needed	Available	Speed	Runs	Quality	File to pick in the app
`Q2_K`	5.4 GB	9.9 GB	14.1 tok/s	Runs well	56Good quality (B) · 56 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q3_K_M`	6.2 GB	9.9 GB	11.1 tok/s	Runs well	67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q4_K_M`	7.3 GB	9.9 GB	8.6 tok/s	Slow	76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	`Q4_K_M`
`Q5_K_M`	8.2 GB	9.9 GB	7.1 tok/s	Slow	77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	`Q5_K_M`
`Q6_K`	9.2 GB	9.9 GB	5.9 tok/s	Slowtight fit	79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	`Q6_K`
`Q8_0`	11.3 GB	9.9 GB	—	Won't runover the app memory limit	80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	`Q8_0`
`F16`	19.8 GB	9.9 GB	—	Won't runover the app memory limit	80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	`BF16`

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 46.7 tok/s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 51.1 tok/s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q8_0 · 62.2 tok/s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Q8_0 · 66.7 tok/s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 F16 · 57.3 tok/s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 38.3 tok/s · Runs great

Quick answers

How much memory does K2 Horizon 7B need on the OnePlus 15 16GB?

At Q3_K_M it needs about 6.2 GB. The OnePlus 15 16GB has 9.9 GB usable.

How fast is K2 Horizon 7B on the OnePlus 15 16GB?

About 11.1 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of K2 Horizon 7B should I use on the OnePlus 15 16GB?

Q3_K_M. 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.