Raven is developing a distributed network of compute nodes for Artificial Intelligence and Machine Learning. A decentralized computing protocol built on #BNB
Hong Kong and Bangalore
Joined November 2017
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🏆Raven wins the @mozillabuilders Award at the Fix-the-Internet showcase.
Recognition by the top open source organization in the world that Raven Protocol is a core infrastructure project.
We demonstrated best traction and commitment to fix the internet!
medium.com/ravenprotocol/rav…
1/ Open-source AI has made powerful models easier to access. Models like GLM-5.3, DeepSeek V4.1, Beam, and Kimi K3 are pushing the frontier.
But downloading the weights is only the first step.
The harder problem is what comes next. 🧵
2/ Downloading a 500B model & training it are completely different problems
Three walls most open source devs hit
- Single-node memory limits
- Bandwidth bottlenecks during gradient sync
-Node failures mid-run
Most frameworks werent built for consumer hardware across real n/ws.
1/ Last Week in Open Source AI & Distributed Compute
- NVIDIA releases Nemotron 3.5 Super VL, a 120B multimodal model, alongside two open-weight Bolt edge models
- llm-d 1.0 launches as a stable Kubernetes-native stack for production-scale LLM inference
3/
- DeepSeek open-sources DeepInfer, its production inference framework, under Apache 2.0, with support for NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs
- OpenCode discloses a breach exposing API keys belonging to 1.4% of Console users with external providers
GM, new week.
Open-source AI keeps shipping.
The real work starts when you move from accessing a model to making it your own.
1/ Open source models now handle 78% of token volume on major AI gateways.
That shift didn't happen gradually. It happened because open models crossed a threshold, good enough capability at a fraction of the cost. For most workloads, the choice became obvious.
2/ Model access and model ownership are different things.
Running an open model via an inference provider is access. Fine-tuning on your own data, training domain-specific variants, that's ownership.
Most open source developers are still stuck at access.
1/ This chart tells you everything about where AI infrastructure costs are heading.
US semiconductor producer prices surged +26% YoY in September:
- 6th consecutive 25%+ YoY increase
- Up 31% since July 2025
- Declined every year from 1994–2021
Historic territory.
Semiconductor prices are skyrocketing in the US:
Producer prices for semiconductor and other electronic component manufacturing surged +26% YoY in September, their 4th-largest increase on record.
This also marks their 6th consecutive YoY increase of at least +25%.
Since July 2025, the Producer Price Index for semiconductor and other electronic component manufacturing has risen +31%, to its 2nd-highest level since January 2007.
To put this into perspective, the index declined every year from 1994 to 2021.
Meanwhile, producer prices for electronic components and accessories soared +27% YoY last month, also marking their 6th consecutive YoY increase of at least +25%.
The AI Revolution is driving historic technology inflation.
2/ AI demand is putting pressure on semiconductor supply. Expanding production capacity takes time & the cost of new hardware remains a challenge for teams building & training AI models.
As compute demand grows, relying on ever more expensive hardware becomes harder to sustain.
1/ Building a custom training pipeline on distributed infra usually means one thing: your research adapts to the framework, not the other way around.
Custom loss functions, non-standard architectures, experimental optimizers ,most distributed frameworks treat them as edge cases.
2/ The result: researchers spend more time working around the distributed layer than running experiments.
Every unconventional training approach means new workarounds. The infrastructure that should be invisible becomes the biggest obstacle.