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Can the NVIDIA GeForce RTX 5090 run SenseNova Loop 9B Preview?

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.

Yes, it runs great

Runs great. At F16 it needs about 22.0 GB of 32.0 GB usable, with an estimated speed of 53.4 tok/s.

Best quant
F16
Generation speed
53.4 tok/s
Prompt processing
7,811 tok/s
First token (1K prompt)
0.3 s
Largest safe context
178K

Best app to run it on the NVIDIA GeForce RTX 5090

On Windows

Docker Model Runner

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

  • Handy if you already work in Docker.
docker model run hf.co/sensenova/SenseNova-Loop-9B-Preview

On Linux

Docker Model Runner

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

  • Handy if you already work in Docker.
docker model run hf.co/sensenova/SenseNova-Loop-9B-Preview
  • vLLM For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.
  • SGLang For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.

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 5 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 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.
How capable the model itself is at F16, 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 GeForce RTX 5090: 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.35.1 / 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.15.0 / 15
Match score90.1 / 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 GeForce RTX 5090

Quant Memory needed Available Speed Runs Quality Download
Q2_K 6.5 GB 32.0 GB 374 tok/s Runs great 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. All files (opens in a new tab)
Q3_K_M 7.4 GB 32.0 GB 294 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 74/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 8.6 GB 32.0 GB 228 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 79/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 9.5 GB 32.0 GB 188 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 80/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 10.6 GB 32.0 GB 158 tok/s Runs great 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 12.9 GB 32.0 GB 116 tok/s Runs great 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 22.0 GB 32.0 GB 53.4 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. 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

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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