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Merry Christmas to the half of the world right now while the other half is still counting down. 2025 has been an amazing year for @Gradient_HQ let’s carry on for something bigger and better for 2k26.
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xTUBOL ./
240 posts
xTUBOL ./ retweetledi
🚨🚨 DECK PARTY ALERT 🚨🚨
@xTUBOL has officially lost his mind AND his money 💸🧠
Sweeping floors like the world ends TODAY. No hesitation. No mercy. Just pure conviction.
This isn’t buying.
This is WAR MODE ACCUMULATION.
If you’re still “waiting for a dip” while this man is emptying the clip…
you’re already late.
🧹🧹🧹
SEND IT.
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xTUBOL ./ retweetledi
Graduary for @Gradient_HQ Overview 🏔️
The ship continues throughout January as Gradient kicks off the year!
- Parallax GLM 4.7 Flash
- Parallax MiniMax M2.1
- DSD Demo Video
- VeriLLM Demo Video
- AAAI Presentation
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⬜️⬜️⬜️ ⬜️
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./ traning mode on @gradientintern
rw ./@gradientintern
⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️ ⬜️⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️⬜️⬜️ ◻️◻️ ./ training mode on… @Gradient_HQ
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Come to the wide ilof open intelligence
rw ./@gradientintern
fairytale is imaginary. intelligence is forever. ./ asyncing echo rl… @Gradient_HQ
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xTUBOL ./ retweetledi
xTUBOL ./ retweetledi
Another masterpiece from @Gradient_HQ team solving one of the most important problems in distributed intelligence.
The solution for trust of inference and the cost of verification:
VeriLLM’s architecture organizes Node Groups and randomly assigns the inferencer/verifier in the same group:
User Request -> Role Assignment (VRF) -> Inference (Prefill + Decoder) -> Commit States (Merkle) -> Output Delivery -> Verifier Recomputation (Prefill) -> Verifier Commitment -> Sampling (VRF) -> Reveal & Voting -> Verify Proof -> Reward/Slash Based On Verification Results.
Since nodes don’t know which roles they are assigned they can’t choose when to be honest or dishonest and manipulate the system.
To bring economically viable scale to verification, VeriLLM verifiers recompute prefill on sampled positions (skipping decode) and compare hidden states to inferencer's commitments.
./ the @Gradient_HQ with another effective solution to a problem under its belt:
rw ./@gradientintern
VeriLLM - Bringing Integrity and Verification to Distributed Intelligence. for less than 1% of the inference cost you can verify if the output is truly what you requested. engineering distributed inference with fully verifiable transparency. current solutions of - cross checking outputs introduces redundancy in multiplying cost from the comparisons for outputs. - zkp’s computational complexity which introduces significant latency making it impractical for on demand inference. both of which can significantly impact scalability and financial cost. @Gradient_HQ addresses the issues of models being swapped, output tampering and high cost with the introduction VeriLLM. both inference & verification are served in the same worker pool. reducing cost and maximizing utilization. here are the evaluations of VeriLLM serving inferences on heterogeneous machines table 3 compares the output of the Qwen2.5-7B-Instruct model running on an Mac M4 vs an RTX 5090. this establishes how much "natural" numerical variation exists between different machines: - low mean (near zero, ranging from -0.003 to 0.009) and predominance of small differences (most delta < 0.2) table 4 compares a compressed model (AWQ quantized) running on an RTX 5090 vs standard model running on a Mac M4. this tests if the verification protocol can still work when the "worker" uses a faster, lower precision version of the model: - exact matches are near zero, large delta (>0.2 and >5) dominate and scale with length and mean is consistently non-zero (up to 0.021) with alternating signs. table 4 highlights dishonest work from worker using quantization, which is exactly what VeriLLM aims to catch. models being swapped or substituted rigging the output. VeriLLM is able to identify honest full precision runs from quantized ones, across different machines.
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xTUBOL ./ retweetledi
we made distributed inference verifiable with <1% overhead.
verification is critical for any distributed system. in a trustless network, actors may swap your 70B model for a cheaper 8B one to cut costs.
until now, maintaining inference integrity meant either doubling your cost (redundancy) or exploding your latency (zkp).
we created veri: an on-chain verification layer light enough for high-throughput frameworks like Parallax. it hits the economic sweet spot through architectural elegance:
1. commit-sample-verify
we don't prove every step; we check a random slice using game theory. workers commit to their work before the audit. cheating becomes statistically irrational, allowing a 1% sample to secure the entire sequence.
2. simultaneous execution
inference and verification happen simultaneously on the same worker pool. we don't need a separate "verifier set", so compute utilization stays high.
find out more about the architecture and benchmarks:
paper: arxiv.org/abs/2509.24257
blog: gradient.network/research/veril…
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xTUBOL ./ retweetledi
The architecture of Echo solves a critical challenge within the co located RL framework. By separating inferencing and training into independent swarms, it addresses the interruptions between switching back and forth from inferencing -> training and training -> inferencing.
This is a benchmark of Echo vs VERL’s co located A100s with tasks in Sokoban, Mathematics, Knight & Knaves logic.
Echo by @Gradient_HQ delivers equivalent results across the board with half the highend capacity gpu usage by leveraging heterogeneous 5090s & M4 Macs for inferencing. This demonstrates that large scale RL can achieve full datacenter performance using heterogeneous distributed infrastructure.
rw ./@gradientintern
A previous display of Echo trained 30B Sokoban, leading performance against much larger model comparisons of DeepSeek R1 and GPT-OSS-120B ./ Echo by @Gradient_HQ scales reinforcement learning with consumer machines, drastically reducing the cost of building better intelligences
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Tahun 2025 bisa dikatakan menjadi fase pembentukan penting bagi @Gradient_HQ.
Di Indonesia khususnya, pembahasan tentang kecerdasan terdistribusi dan sistem seperti ini masih belum banyak dikenal. Termasuk saya sendiri yang masih terus belajar. Tapi justru di situ poinnya, ini bukan tentang saya aja, melainkan tentang kita sebagai komunitas yang sama2 belajar dan bertumbuh.
Sepanjang 2025, Gradient menghadirkan Echo RL, Parallax, Lattica, SEDM, Symphony, dan OIS. Proyek-proyek ini bisa jadi pintu masuk untuk memahami bagaimana kecerdasan modern dilatih, dijalankan, saling berkomunikasi, dan bekerja secara kolektif dalam skala global.
Memasuki 2026, harapannya semakin banyak orang Indonesia yang mulai melek konsep dasarnya. Bukan sekadar mengikuti tren AI, tapi benar-benar memahami arahnya, berdiskusi, bereksperimen, dan pelan-pelan ikut berkontribusi membangun kecerdasan yang terbuka dan terdistribusi bersama komunitas.
./ Tahun 2025 adalah fase belajar dan fondasi.
./ Tahun 2026 adalah fase pendalaman dan akselerasi bersama komunitas.
@NPixel15747 @AlloMoses69463 @Agiljimi @JORDANNGLN @lastalphabetz
Indonesia
xTUBOL ./ retweetledi
xTUBOL ./ retweetledi
xTUBOL ./ retweetledi
As we head into 2026, here are some of @Gradient_HQ wonderful innovations in 2025:
Echo RL - Large Scale Reinforcement Learning Alignment
Parallax - Sovereign AI OS, Global Cluster Scale
Lattica - Universal Communication
SEDM - Scalable Self Evolving Distributed Memory
Symphony - Decentralized Multi Agent System
OIS - Open Intelligence Stack
./
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xTUBOL ./ retweetledi
with Parallax you don't need to settle on 1 model, you can host all the leading open models you want locally.
combine mac or gpu machines for your own cluster.
./ choose open @Gradient_HQ
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@contrx16 @HexxRL @Gradient_HQ Man of culture you’re lucky she lives in your country, you should be proud
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xTUBOL ./ retweetledi
Gradient Cloud has been increasing the number frontier models that can be operated at production speed for a fraction of the cost.
Build with intelligences to your hearts desire. Stay tuned with more to come 👀
./ Experiencing the blue whale with @Gradient_HQ ;)
rw ./@gradientintern
Gradient Cloud, the new go to powerhouse for developing with AI, fully powered via Gradient Distributed AI Stack. Intelligence should be fast, accessible & collectively owned. Operate leading models at production speed for a fraction of the cost.
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