Maple Preview (20B-A1B) on OnePlus 13 (24 GB RAM)

Can the OnePlus 13 (24 GB RAM) run Maple Preview (20B-A1B)?

Yes, it runs great

Runs great. At Q4_K_M it needs about 13.0 GB of 15.6 GB usable, with an estimated speed of 44.1 tok/s.

Best quant: Q4_K_M
Generation speed: 44.1 tok/s
Prompt processing: 157 tok/s
First token (1K prompt): 6.5 s
Largest safe context: 128K

Best app to run it on the OnePlus 13 (24 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 Maple Preview (20B-A1B) in the app

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

Want an agent? Maple Preview (20B-A1B) 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 Maple Preview (20B-A1B)

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 maple-preview-TQ1_0-head-F16.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 deepgrove/maple-preview-GGUF, open it and download maple-preview-TQ1_0-head-F16.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/deepgrove/maple-preview-GGUF (opens in a new tab)

ollama run hf.co/deepgrove/maple-preview-GGUF

Original weights · Hugging Face

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

huggingface.co/deepgrove/maple-preview (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/deepgrove-ai/llama.cpp (opens in a new tab)

DeepGrove's llama.cpp fork with setup instructions for the Maple GGUF files.

git clone https://github.com/deepgrove-ai/llama.cpp

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4_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 great: How well it runs on the OnePlus 13 (24 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.	29.9 / 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.	15.0 / 15
Match score	84.9 / 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 OnePlus 13 (24 GB RAM)

Quant	Memory needed	Available	Speed	Runs	Quality	File to pick in the app
`Q2_K`	8.8 GB	15.6 GB	67.1 tok/s	Runs great	49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q3_K_M`	10.6 GB	15.6 GB	55.0 tok/s	Runs great	58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q4_K_M`	13.0 GB	15.6 GB	44.1 tok/s	Runs great	66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q5_K_M`	15.1 GB	15.6 GB	37.2 tok/s	Runs welltight fit	67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q6_K`	17.3 GB	15.6 GB	—	Won't runover the app memory limit	69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q8_0`	22.1 GB	15.6 GB	—	Won't runover the app memory limit	70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	—
`F16`	41.1 GB	15.6 GB	—	Won't runover the app memory limit	70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	`F16`

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

Other hardware that runs it

70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q8_0 · 71.7 tok/s · Runs great
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q8_0 · 195 tok/s · Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q6_K · 175 tok/s · Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q6_K · 183 tok/s · Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q6_K · 187 tok/s · Runs great
67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q5_K_M · 162 tok/s · Runs well (tight fit)

Quick answers

How much memory does Maple Preview (20B-A1B) need on the OnePlus 13 24GB?

At Q4_K_M it needs about 13.0 GB. The OnePlus 13 24GB has 15.6 GB usable.

How fast is Maple Preview (20B-A1B) on the OnePlus 13 24GB?

About 44.1 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Maple Preview (20B-A1B) should I use on the OnePlus 13 24GB?

Q4_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.