Post
30
My guess on Ox Alpha -> GLM
Yi Cui
onekq
AI & ML interests
Benchmark, Code Generation Model
Recent Activity
posted an update about 9 hours ago
My guess on Ox Alpha -> GLM posted an update 7 days ago
Lots of attentions are now on GPU residual value. I find car analogy to be useful.
* Both new and used cars can do the same job (you can run the latest model on 6-year-old A100)
* New cars are more efficient (higher performance per power draw)
* It's not the year, but mileage and maintenance
The last point is undeveloped. We need Carfax and KBB for GPUs.
And there will be lemon GPUs.Organizations
posted an update about 9 hours ago
posted an update 4 days ago
Post
92
I for one believe DeepMind will ship one more frontier model.
In any case GCP is irrelevant here. GDM should still have more compute than DeepSeek, Kimi, or GLM, maybe all of them combined.
In any case GCP is irrelevant here. GDM should still have more compute than DeepSeek, Kimi, or GLM, maybe all of them combined.
posted an update 7 days ago
Post
3216
Lots of attentions are now on GPU residual value. I find car analogy to be useful.
* Both new and used cars can do the same job (you can run the latest model on 6-year-old A100)
* New cars are more efficient (higher performance per power draw)
* It's not the year, but mileage and maintenance
The last point is undeveloped. We need Carfax and KBB for GPUs.
And there will be lemon GPUs.
* Both new and used cars can do the same job (you can run the latest model on 6-year-old A100)
* New cars are more efficient (higher performance per power draw)
* It's not the year, but mileage and maintenance
The last point is undeveloped. We need Carfax and KBB for GPUs.
And there will be lemon GPUs.
- 1 reply
posted an update 11 days ago
Post
2758
This is an easy-to-remember pattern.
GLM and Kimi are in Beijing, DeepSeek and Qwen are in Hangzhou.
OpenAI and Anthropic are in SF, xAI and Meta are in the peninsula.
In both China and the Bay Area, token price of one area is ~1/3 of the other area.
GLM and Kimi are in Beijing, DeepSeek and Qwen are in Hangzhou.
OpenAI and Anthropic are in SF, xAI and Meta are in the peninsula.
In both China and the Bay Area, token price of one area is ~1/3 of the other area.
- 1 reply
posted an update 13 days ago
Post
1752
I think contributor pricing is a good idea. But things will get sophisticated very quickly.
* data sharing tiers from exclusive to syndication
* provenance and lineage
* scoping such as duration and geofencing
and their levels of enforcement (best practices, standards, laws)
* data sharing tiers from exclusive to syndication
* provenance and lineage
* scoping such as duration and geofencing
and their levels of enforcement (best practices, standards, laws)
posted an update 14 days ago
Post
2661
Looking forward to the Qwen 3.8 model drop, and congratulations on joining the trillion parameters club.
But my eyes are on the promised 27B model. Small (<50B) models decline on OpenRouter, because they are being run on local devices. If people see the family trees here on HF they will understand.
But my eyes are on the promised 27B model. Small (<50B) models decline on OpenRouter, because they are being run on local devices. If people see the family trees here on HF they will understand.
- 2 replies
replied to their post 16 days ago
That's a brilliant strategy. Mystery is why Kimi and GLM didn't follow it.
replied to their post 16 days ago
You are right. It's margin, simple as that.
Dax (OpenCode) has a tweet with lots of discussions.
posted an update 17 days ago
Post
2431
DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away.
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
- 5 replies
posted an update 18 days ago
Post
142
Jeff Dean departure is 100% unexpected. It's impossible to process this.
And yes, it matters more than other exec shakeups today.
And yes, it matters more than other exec shakeups today.
posted an update 19 days ago
Post
2353
Many developers discovered that the native DeepSeek API has higher cache-hit rate than neocloud APIs hosting the same DS models.
My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).
This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.
My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).
This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.
posted an update 23 days ago
Post
2222
There has been a leaked memo (now struck down) from the founder of DeepSeek. I'm not here to circulate it, but comment on the minimum-effort evolutionary path he proposed.
LLM->CoT->Agent->Self-improvement->Singularity->Physical
This makes sense to me: even at the agent stage I learn world models much faster than when I learned LLM at the LLM stage.
But this means humans are still needed beyond the digital singularity, until robots can close their own loop: eval, manufacturing, self improvement, i.e. physical singularity.
LLM->CoT->Agent->Self-improvement->Singularity->Physical
This makes sense to me: even at the agent stage I learn world models much faster than when I learned LLM at the LLM stage.
But this means humans are still needed beyond the digital singularity, until robots can close their own loop: eval, manufacturing, self improvement, i.e. physical singularity.
posted an update 24 days ago
Post
444
The Nvidia paper came down to this: remove synchronization barriers. DeepSeek has already done that with DeepEP (which this paper cited) one layer above.
Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives (2607.16100)
Nevertheless this is great. More users will benefit from this:
Nvidia NCCL > SGLang/vLLM DeepEP
Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives (2607.16100)
Nevertheless this is great. More users will benefit from this:
Nvidia NCCL > SGLang/vLLM DeepEP
posted an update 25 days ago
Post
112
This is definitely a minority opinion. World model MIGHT be the answer to enterprise AI.
https://arxiv.org/abs/2602.23643
* Tacit knowledge won't be captured in words, much less accessible dataset
* RL is not efficient at all, and labeling gets more and more expensive
* AGI won't cover your specialized tasks
PS: paper folks you missed one from Yann LeCun!!
https://arxiv.org/abs/2602.23643
* Tacit knowledge won't be captured in words, much less accessible dataset
* RL is not efficient at all, and labeling gets more and more expensive
* AGI won't cover your specialized tasks
PS: paper folks you missed one from Yann LeCun!!
posted an update about 1 month ago
Post
114
Hi, it's been a while 🤗
K3 stunned the world (again). But if you followed my post on K2, the architectural lineage is super clear
https://onekq1.substack.com/p/beyond-the-bonsai-how-kimi-k3s-quantization
I will next discuss the K3 optimizer (Muon again)
K3 stunned the world (again). But if you followed my post on K2, the architectural lineage is super clear
https://onekq1.substack.com/p/beyond-the-bonsai-how-kimi-k3s-quantization
I will next discuss the K3 optimizer (Muon again)
posted an update 9 months ago
Post
424
GPT 5.1 codex didn't make SOTA either. This should conclude 2025. No model has ever reached above 0.8.
onekq-ai/WebApp1K-models-leaderboard
Can this leaderboard be saturated in 2026?
onekq-ai/WebApp1K-models-leaderboard
Can this leaderboard be saturated in 2026?
replied to their post 9 months ago
Hmm, I see.
posted an update 9 months ago
Post
285
I am starting a new series on matrix. The idea came to me when I wrote about the Muon optimizer.
Matrix itself has lots of fascinating properties and is applied in STEM fields for many decades. Its application in ML is just the beginning. There are lots of low hanging fruits. At the very least, I hope this math perspective will give you a new lens.
https://huggingface.co/blog/onekq/matrices-transformers-preface
Matrix itself has lots of fascinating properties and is applied in STEM fields for many decades. Its application in ML is just the beginning. There are lots of low hanging fruits. At the very least, I hope this math perspective will give you a new lens.
https://huggingface.co/blog/onekq/matrices-transformers-preface
- 2 replies
posted an update 9 months ago
Post
239
DeepSeek v3.2 is worse than R1. This is quite puzzling. Why the regression with new GRPO and new attention?
onekq-ai/WebApp1K-models-leaderboard
I used reasoning mode against DeepSeek API
onekq-ai/WebApp1K-models-leaderboard
I used reasoning mode against DeepSeek API