IO Intelligence breakdown: 25 open-source models, VectorDB and AI agents
Thread657 words
Market since$IO−74%since this pieceHow it played out ↓Walks through io.net's IO Intelligence platform: Vector Database as a Service, 25 open-source models with benchmark context and prebuilt API-accessible agents, all on io.net's decentralized compute. Accompanied by an interactive dashboard he built.
Key claims
IO Intelligence offers 25 open-source models
Platform includes VectorDB as a service and specialized agents
io.net is the only network offering geo-distributed GPU clustering
Open-source AI models are growing more powerful rapidly & GPU networks like @ionet make them accessible at scale.
Meet IO Intelligence, a platform providing Vector Database as a Service, 25 open-source AI models, and specialized agents.
Check out my interactive dashboard. 👇 t.co/YCbwuRsGII
1/ @ionet is the largest decentralized GPU network in the market, and the only solution enabling geo-distributed clustering of compute resources with institutional-grade quality & reliability.
2/ $IO is a market-leading solution offering unique benefits, including elastic scaling.
3/ The most disruptive product undoubtedly is IO intelligence, a full-stack solution for AI builders.
4/ Flexible execution, VectorDB as a service, access to leading AI models and templatized AI agents.
IO Intelligence offers it all.
5/ Providing access to 25 open-source models, builders have a lot of flexibility to choose the right model for their needs.
So how do all these models compare?
Let's have a closer look at model performance.
6/ Llama-4-Maverick-17B
Llama 4 Maverick, a 17 billion active parameter model with 128 experts, is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks.
It also achieves comparable results to the new DeepSeek v3 on reasoning and coding, at less than half the active parameters.
7/ DeepSeek-R1
DeepSeek AI introduced first-generation reasoning models, trained via large-scale reinforcement learning (RL), demonstrated remarkable performance on reasoning.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
8/ QwQ-32B
Compared with conventional instruction-tuned models, QwQ-32B, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems.
QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
9/ Llama-3.3-70B-Instruct
Llama 3.3 is an auto-regressive language model that uses an optimized transformer architecture.
The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
10/ Mistral-Large-Instruct-2411
Mistral-Large-Instruct-2411 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities.
It extends Mistral-Large-Instruct-2407 with better Long Context, Function Calling and System Prompt.
11/ DeepSeek-R1-Distill-Llama-70B
DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
This one is fine-tuned Llama 3.3 70B and their configs and tokenizers are slightly changed.
12/ DeepSeek-R1-Distill-Qwen-32B
DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
This one is fine-tuned Qwen-32 and their configs and tokenizers are slightly changed.
13/ dbrx-instruct
DBRX Instruct is a mixture-of-experts (MoE) large language model trained from scratch by Databricks. DBRX Instruct specializes in few-turn interactions.
14/ Ministral-8B-Instruct-2410
The Ministral-8B-Instruct-2410 Language Model is an instruct fine-tuned model significantly outperforming existing models of similar size, released under the Mistral Research License.
15/ Confucius-o1-14B
Confucius-o1-14B is a o1-like reasoning model developed by the NetEase Youdao Team, it can be easily deployed on a single GPU without quantization.
This model is based on the Qwen2.5-14B-Instruct model and adopts a two-stage learning strategy, enabling the lightweight 14B model to possess thinking abilities similar to those of o1.
16/ More info on performance scoring and benchmarks.
17/ Aside leading open-source models, IO intelligence also offers API access to pre-built AI agents specialized on specific tasks.
18/ They can seamlessly be integrated into workflows, increasing efficiency through AI-powered automation.
19/ Additionally, they can be combined for more complex workflows.
20/ Check out the @ionet docs to experiment with their agents yourself.
I might drop a demo on that shortly as well, stay tuned.
21/ If you want to check out the beta version of the dashboard (hosted by my fren Claude), check out the link below.
claude.site/artifacts/80a9ea94-596a-4932-bde3-223b8d89e107
Thanks for reading chads
If you liked this thread, please drop a like, comment or RT on the post linked below.
Make sure to drop a follow if you are looking for alpha on blockchain infra and AI infrastructure (or more interactive dashboards).
x.com/zerokn0wledge_/status/1913170695468581050
21 images on X
How the covered assets moved · since 18 Apr 2025
Prices as of 23 Oct 2026This breakdown analyzes the project without making a call on the token, so price moves since publication are shown as market context.
Covered in this piece
Since then
Zero has written 12 more pieces on io.net and $IO since this one.
- io.net IDE tokenomics: #1 revenue DePIN at 5.3x MC/ARRBullish call on $IO
- DeAI as crypto's endgame: compute, agents and autonomous economiesOn io.net
- Why decentralized compute solves the startup GPU cost squeezeOn io.net
- Big compute is cracking: summary of the DeAI case against centralized cloudOn io.net
Daily closes in USD from DefiLlama and Yahoo Finance. Only a bullish or bearish thesis is read as a call; explainers, maps and mentions are context. Not financial advice.