on

Can the AMD Ryzen AI Max+ PRO 495 (192 GB) run SenseNova Loop 9B Preview?

74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 19.7 GB of 159 GB usable, with an estimated speed of 29.9 tok/s.

Best quant
Q4_K_M
Generation speed
29.9 tok/s
Prompt processing
3,941 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the AMD Ryzen AI Max+ PRO 495 (192 GB)

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

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 74/100 (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 well
How well it runs on the AMD Ryzen AI Max+ PRO 495 (192 GB): 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 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.13.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.2.9 / 15
Match score74.2 / 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 AMD Ryzen AI Max+ PRO 495 (192 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 17.7 GB 159 GB 49.1 tok/s Runs great 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 18.6 GB 159 GB 38.6 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 71/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 19.7 GB 159 GB 29.9 tok/s Runs well 74Great quality (A) · 74 of 100: how capable the model is at Q4_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)
Q5_K_M 20.7 GB 159 GB 24.7 tok/s Runs well 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 21.7 GB 159 GB 20.7 tok/s Runs well 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 24.1 GB 159 GB 15.2 tok/s Runs well 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 33.1 GB 159 GB 7.0 tok/s Slow 78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 71/100 (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

Quick answers

How much memory does SenseNova Loop 9B Preview need on the AMD Ryzen AI Max+ PRO 495 192GB?

At Q4_K_M it needs about 19.7 GB. The AMD Ryzen AI Max+ PRO 495 192GB has 159 GB usable.

How fast is SenseNova Loop 9B Preview on the AMD Ryzen AI Max+ PRO 495 192GB?

About 29.9 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 AMD Ryzen AI Max+ PRO 495 192GB?

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.

Was this page helpful?