SenseChat
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- SenseTime
- Organisation type
- Industry
- Country
- Hong Kong
- Published
- 10 April 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Chat, Language modeling/generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 180B
- Training data
- tokens
https://www.thepaper.cn/newsDetail_forward_22639611 Translation: "SenseTime launched the "SenseNova" large model system, which includes natural language generation, image generation services, pre-labeling for perception models, and model development. The "SenseChat" application platform, powered by a 180-billion parameter Chinese language model, supports ultra-long text comprehension and offers capabilities such as question answering, understanding, and generation in Chinese." Link says "hundre…
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.9 × 10²⁴ FLOP
- How it was established
- Hardware
- Plausible range
- 1 × 10²³ – 3 × 10²⁵ FLOP
“Over the course of five years, SenseTime has built SenseCore, a leading AI infrastructure with 27,000 GPUs, capable of delivering a total computational power of 5,000 petaflops” Assuming they used this entire cluster with 30 days of training (rough average of frontier model training times since 2016), 30% utilization rate: 5000e15 * 0.3 * 30 * 24 * 60 * 60 = 3.89e24 FLOP. Assuming the model is dense and trained Chinchilla-optimal: 20 tokens/parameter * (180e9 parameters)**2 * 6 = 3.89e24 FLOP…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Compute cost
- $5,328,550
- Data centre
- SenseTime AI Computing Center
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- SenseTime Launches “SenseNova” Foundation Model Sets and AI Computing Systems, Advancing AGI Development
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SenseChat was published by SenseTime, in the country recorded as Hong Kong, during April 2023. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of chat, Language modeling/generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required arithmetic totalling around 3.9 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Its inclusion criterion: training cost.
Answers
SenseChat — common questions
SenseChat— how many parameters does it have?
It has a parameter count of 180B. https://www.thepaper.cn/newsDetail_forward_22639611 Translation: "SenseTime launched the "SenseNova" large model system, which includes natural language generation, image generation services, pre-labeling for perception models, and model development. The "SenseChat" application platform, powered by a 180-billion parameter Chinese language model, supports ultra-long text comprehension and offers capabilities such as question answering, understanding, and generation in Chinese." Link says "hundreds of billions" but the more precise number above seems more credible. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
SenseChat— who created it?
It was published by SenseTime, based in Hong Kong, an organisation categorised as industry.
SenseChat— when was it released?
It was published in April 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SenseChat— what is it used for?
It works in the domain of Language, and is recorded as handling the task of chat, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
SenseChat— how much compute was used to train it?
Training consumed around 3.9 × 10²⁴ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
SenseChat— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
SenseChat— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.