Can the NVIDIA T4 run SenseNova Loop 9B Preview?

Yes, it runs great

Runs great. At Q4_K_M it needs about 7.8 GB of 16.0 GB usable, with an estimated speed of 40.7 tok/s.

Best quant: Q4_K_M
Generation speed: 40.7 tok/s
Prompt processing: 1,207 tok/s
First token (1K prompt): 0.8 s
Largest safe context: 148K

Best app to run it on the NVIDIA T4

On Linux

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve sensenova/SenseNova-Loop-9B-Preview

SGLang The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
Docker Model Runner Handy if you already work in Docker.

On Windows

vLLM

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
On Windows it runs through WSL2.

vllm serve sensenova/SenseNova-Loop-9B-Preview

SGLang The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
Docker Model Runner Handy if you already work in Docker.

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 74Great quality (A) · 74 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 NVIDIA T4: 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.	33.3 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	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.	11.2 / 15
Match score	84.5 / 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 T4

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	5.7 GB	16.0 GB	66.8 tok/s	Runs great	54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q3_K_M`	6.6 GB	16.0 GB	52.6 tok/s	Runs great	65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q4_K_M`	7.8 GB	16.0 GB	40.7 tok/s	Runs great	74Great quality (A) · 74 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.	All files (opens in a new tab)
`Q5_K_M`	8.7 GB	16.0 GB	33.6 tok/s	Runs great	75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	9.8 GB	16.0 GB	28.2 tok/s	Runs well	77Great quality (A) · 77 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.	All files (opens in a new tab)
`Q8_0`	12.1 GB	16.0 GB	20.7 tok/s	Runs well	78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	21.2 GB	16.0 GB	4.7 tok/s	Slowoffloads to RAM	78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (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 T4 16GB?

At Q4_K_M it needs about 7.8 GB. The NVIDIA T4 16GB has 16.0 GB usable.

How fast is SenseNova Loop 9B Preview on the NVIDIA T4 16GB?

About 40.7 tok/s at Q4_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 NVIDIA T4 16GB?

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.