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Can the Samsung Galaxy Z Fold8 (16 GB RAM) run SenseNova Loop 9B Preview?

65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q3_K_M it needs about 5.8 GB of 9.9 GB usable, with an estimated speed of 10.3 tok/s.

Best quant
Q3_K_M
Generation speed
10.3 tok/s
Prompt processing
28.5 tok/s
First token (1K prompt)
35.9 s
Largest safe context
78K

Download SenseNova Loop 9B Preview

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.

On Android

No mobile app in the Play Store runs SenseNova Loop 9B Preview right now: mobile apps need a GGUF version, and none is published. Mobile apps · models that run on mobile devices.

Files and commands for a computer

Original weights · Hugging Face

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

huggingface.co/sensenova/SenseNova-Loop-9B-Preview (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/OpenSenseNova/SenseNova-Loop (opens in a new tab)

SenseNova's official repo for the Loop model. vLLM and SGLang support is still in open pull requests.

git clone https://github.com/OpenSenseNova/SenseNova-Loop

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 75/100 (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 Samsung Galaxy Z Fold8 (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.29.5 / 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.7.6 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.5 / 15
Match score75.6 / 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 Samsung Galaxy Z Fold8 (16 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 4.9 GB 9.9 GB 13.1 tok/s Runs well 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. —
Q3_K_M 5.8 GB 9.9 GB 10.3 tok/s Runs well 65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. —
Q4_K_M 7.0 GB 9.9 GB 8.0 tok/s Slow 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. —
Q5_K_M 7.9 GB 9.9 GB 6.6 tok/s Slow 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. —
Q6_K 9.0 GB 9.9 GB 5.5 tok/s Slowtight fit 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. —
Q8_0 11.3 GB 9.9 GB — Won't runover the app memory limit 78Great quality (A) · 78 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. —
F16 20.4 GB 9.9 GB — Won't runover the app memory limit 78Great quality (A) · 78 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. —

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

Other hardware that runs it

Quick answers

How much memory does SenseNova Loop 9B Preview need on the Galaxy Z Fold8 16GB?

At Q3_K_M it needs about 5.8 GB. The Galaxy Z Fold8 16GB has 9.9 GB usable.

How fast is SenseNova Loop 9B Preview on the Galaxy Z Fold8 16GB?

About 10.3 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of SenseNova Loop 9B Preview should I use on the Galaxy Z Fold8 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.

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