SenseNova Loop 9B Preview on NVIDIA RTX A1000 Laptop GPU

Can the NVIDIA RTX A1000 Laptop GPU run SenseNova Loop 9B Preview?

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q2_K it needs about 5.1 GB of 4.0 GB usable, with an estimated speed of 27.6 tok/s.

Best quant: Q2_K
Generation speed: 27.6 tok/s
Prompt processing: 444 tok/s
First token (1K prompt): 2.3 s
Largest safe context: —

Best app to run it on the NVIDIA RTX A1000 Laptop GPU

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

Splits the model between GPU memory and system RAM, which this setup needs.
Handy if you already work in Docker.

docker model run hf.co/sensenova/SenseNova-Loop-9B-Preview

Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

Splits the model between GPU memory and system RAM, which this setup needs.
Handy if you already work in Docker.

docker model run hf.co/sensenova/SenseNova-Loop-9B-Preview

Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

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 SenseNova Loop 9B Preview

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 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, 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 welloffloads to RAM: How well it runs on the NVIDIA RTX A1000 Laptop GPU: 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.	24.6 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	12.5 / 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	62.0 / 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 NVIDIA RTX A1000 Laptop GPU

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	5.1 GB	4.0 GB	27.6 tok/s	Runs welloffloads to RAM	54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q3_K_M`	6.0 GB	4.0 GB	18.4 tok/s	Slowheavy RAM offload	65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q4_K_M`	7.2 GB	4.0 GB	12.6 tok/s	Slowheavy RAM offload	74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q5_K_M`	8.1 GB	4.0 GB	9.7 tok/s	Slowheavy RAM offload	75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	9.2 GB	4.0 GB	7.8 tok/s	Slowheavy RAM offload	77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q8_0`	11.5 GB	4.0 GB	5.3 tok/s	Slowheavy RAM offload	78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	20.6 GB	4.0 GB	—	Won't run	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.	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

78Great quality (A) · 78 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. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 43.5 tok/s · Runs great
78Great quality (A) · 78 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. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 47.6 tok/s · Runs great
78Great quality (A) · 78 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. NVIDIA GeForce RTX 5070 Ti Q8_0 · 58.0 tok/s · Runs great
78Great quality (A) · 78 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. NVIDIA GeForce RTX 5080 Q8_0 · 62.1 tok/s · Runs great
78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 F16 · 53.4 tok/s · Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER Q6_K · 44.4 tok/s · Runs great

Quick answers

How much memory does SenseNova Loop 9B Preview need on the NVIDIA RTX A1000 Laptop GPU 4GB?

At Q2_K it needs about 5.1 GB. The NVIDIA RTX A1000 Laptop GPU 4GB has 4.0 GB usable.

How fast is SenseNova Loop 9B Preview on the NVIDIA RTX A1000 Laptop GPU 4GB?

About 27.6 tok/s at Q2_K, 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 NVIDIA RTX A1000 Laptop GPU 4GB?

Q2_K. 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.