𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ

4.7K posts

𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ banner
𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ

𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ

@Tensor_Flow_

Decentralized Intelligence | Bittensor ττ | Privacy • Freedom • Community . What they centralize, Bittensor distributes⚡⚡

U.K Katılım Şubat 2021
278 Takip Edilen444 Takipçiler
𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ
I've been using @engyai SN53 for a week or two now and have to say it's been a great experience. @totheagi has done a cracking job and what with Kimi K3 coming to the Subnet in the very near future it looks like things will only get better. The video below was created via engy. Enjoy!
English
0
3
23
367
WolfWammer
WolfWammer@wolfwammer·
@Tensor_Flow_ @engyai I remember @AlgodTrading discussing that the amount of models provided by chutes is to high to be profitable. Hence, only high demand models will be served on @engyai. Glm 5.5 might be one of them. Although I’d like @totheagi to focus on speed first, before adding extra moddels
English
2
0
3
75
WolfWammer
WolfWammer@wolfwammer·
The amount of +100 TAO buys of SN53 @engyai last 24h is crazy That’s no simple lunch money Kimi is the tailwind Bittensor needed
English
1
3
38
1.6K
𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ
@totheagi This is very clever from the moonshot team. Surley this means they are keeping the BF16 model for their internal api? But letting the world enjoy a QAT 4 bit version is genius marketing.
English
0
0
0
462
Ning
Ning@totheagi·
we got the full Kimi K3, 2.8T params, running on 80x RTX 5090s. 20 tok/s single stream, day one, untuned. Last week we took GLM-5.2 from 30 to 110 tok/s on this same fleet. This number will climb. A first for open weights: frontier intelligence served with zero HBM, the scarcest silicon in AI. Just GDDR7 gaming cards, plain ethernet, and the official MXFP4 weights, nothing requantized. The most powerful open model on Earth, on the most abundant GPUs on Earth. Any lab, startup, or university can now own it, probe it, fine-tune it, run agents on it. @Kimi_Moonshot
Ning tweet media
Kimi.ai@Kimi_Moonshot

Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: huggingface.co/moonshotai/Kim… Tech report: github.com/MoonshotAI/Kim… Tech blog: kimi.com/blog/kimi-k3

English
290
584
6.8K
866K
Punisher ττ
Punisher ττ@CryptoZPunisher·
$TAO >> $dTAO >> >> releases/v440-upgrade This May Be One of the Most Important Bittensor Updates of the Year This update is likely one of the most significant changes Bittensor has seen this year because it fundamentally changes how TAO emissions are distributed, which will ultimately change how the market values subnets. Here are the key takeaways. 1. From Linear Distribution to Selective Distribution Until now: if a subnet represented 2% of network demand, it received roughly 2% of emissions. The system was essentially proportional. That is no longer the case. Version 440 introduces a new mechanism called the Gate. It acts as a filter: subnets well above the threshold keep almost all of their emissions; subnets near the threshold begin to lose emissions; subnets below the threshold see their emissions fall rapidly toward zero. In other words: quality is rewarded more than simple participation. 2. The Threshold Is Dynamic The protocol now computes a value called θ (theta). Theta represents the level of demand required for a subnet to be considered competitive. It is recalculated every 360 blocks. That makes the system dynamic: you can't remain mediocre for long; maintaining strong demand becomes a continuous requirement. 3. Winners Receive Part of the Losers' Emissions This is probably the biggest change. When the Gate reduces a weak subnet's emissions, those emissions do not disappear. Instead, they are redistributed to the subnets above the threshold. The live demo illustrates this perfectly: roughly 35 subnets sit above the Gate; together they account for about 43% of total demand; after redistribution, they receive 60%, and eventually 91%, of the active emissions throughout the pipeline. In other words: the strongest subnets become even stronger. 4. Low-Demand Subnets Will Be Heavily Penalized The document is very clear. Subnets in the lower tail of the demand distribution will experience: significantly lower emissions; reduced validator and miner yields; declining attractiveness for capital. This could accelerate the disappearance of projects that fail to generate meaningful adoption. 5. Historical APY Metrics Become Misleading Taostats and other analytics dashboards will need to update their calculations. Why? Because: emissions ≠ demand anymore. Previously: Demand = Emissions. Now: the Gate fundamentally changes that relationship. Any dashboard relying on the old proportional model will underestimate leading subnets while overestimating weaker ones. 6. Premium Subnets Are Likely to Benefit The live example uses SN64. Its demand share is approximately 9.78%. After redistribution, it receives 16.63% of total emissions. That's an increase of more than +6.8% compared to its original demand share. The same mechanism should benefit other subnets that consistently remain near the top of the rankings. What This Means for Bittensor In my opinion, this update changes the philosophy of the network. The protocol is no longer rewarding popularity alone. It is rewarding the ability to consistently rank among the best. This moves Bittensor even closer to a system where capital naturally flows toward the projects creating the greatest value. If this mechanism performs as intended, several consequences seem likely: the strongest subnets should receive higher emissions and higher yields; weaker subnets will struggle more and more to attract validators and capital; investors will need to become increasingly selective, as the gap between leaders and the rest of the market continues to widen. This evolution also aligns perfectly with Bittensor's recent trajectory, dTAO, Root Reborn, and continuous improvements to capital allocation. The protocol is gradually moving away from a relatively uniform distribution model toward a true economic meritocracy, where value creation is translated much more directly into emissions, and ultimately into capital allocation. Source: ➡️ bittensor.com/releases/v440-… ➡️ s3.hippius.com/rufus/public/r… he Top 20 ranked by emissions is starting to look incredibly strong. I'm seriously thinking about launching a new portfolio built entirely around emission leaders. It should be interesting to see how it performs under the new allocation model.
Punisher ττ tweet media
English
20
16
132
8.5K
philip tao
philip tao@tao_philip53131·
The revolution will not be televized
philip tao tweet media
English
1
0
2
67
Quasar
Quasar@QuasarModels·
Quasar is making a major long-term commitment to SN24 by fully locking approximately 130,000 Alpha in the owner wallet. We were among the earliest teams to test, support, and believe in this conviction. We are not here for short-term extraction. We are here to build, contribute, and remain fully aligned with the long-term success of SN24.
English
9
28
104
12.6K
WolfWammer
WolfWammer@wolfwammer·
This week is @QuasarModels week Novelty search on Friday will make it go ballistic Make sure to tune in!
English
2
0
8
188
Beam - Subnet 105
Beam Data Mesh is almost ready. Cloudflare R2 | Amazon S3 | @hippius_subnet | And beyond. We're building a decentralized data mesh where data isn't trapped inside a single provider, it flows across clouds, storage networks, and AI infrastructure through programmable bandwidth. The era of moving terabytes between ecosystems has arrived. Beam is turning bandwidth into infrastructure. #Bittensor #AI #DataInfrastructure SN105 b1m.ai
Beam - Subnet 105 tweet media
English
3
19
80
4.3K
Ning
Ning@totheagi·
Engy opened permissionless inference mining on Bittensor SN53. Thank you to every early-bird miner who plugged in. 30 clusters 94 machines 420 GPUs 17.6 TB aggregate VRAM 216x L40S, 76x 5090, 60x 4090, 25x RTX 6000 Ada, 16x L40, 13x PRO 6000 Blackwell, 11x H100, 3x H200 engy.ai/providers
English
14
26
132
32.2K