Skip to main content

AaKash : India's own low priced Tablet

Finally India produces it's own version of homegrown tablet. And here it is




The wait for the world's cheapest tablet is finally over! The $35 tablet nicknamed Aakash was launched today and will be available at retail stores at a maximum retail price of Rs 2999 ($60), said its maker Datawind. The Aakash tablet, previously nicknamed as Sakshat, looks different from the prototype flaunted by the telecom minister Kapil Sibal last year. However, the specs of the device are same as previously reported.


However it seems that currently there are two versions available at the present.


Version 1 Specs: 

  • Android 2.2
  • Screen Size is  7″ Resistive
  • Processor: 366 MHz + another processor for Graphics or HD Video
  • RAM : 256 MB
  • Flash memory: 2GB + 2GB Micro-SD (expandable up to 32 GB)
  • USB ports: 2
  • Battery Power is 2100mAH
And the Version 2 adds the following in it

  • The $60 tablet for retail sales has an inbuilt cellular modem and SIM to access internet, which will be absent in the $35 device, supplied to the government.
Both versions of the tablet, will run on Google's Android platform, with WiFi connectivity for internet access and cloud storage. The tablets will have 256 MB of RAM, a 32 GB expandable memory slot and two USB ports. 

The commercial version of the tablet would have no duty waivers or subsidy, as in the government's version. An inbuilt cellular modem and SIM card will add to the price of the commercial tablet. 

The commercial version of the tablet, is expected be out within 60 days, of its launch on October 5. 

Datawind adds that it is supplying to the government at a price of Rs 2200, which includes sales tax and replacement warranty. "The $35 price is achievable at higher volume levels. When we supply the product to the government at $35, then too it will allow us a margin, albeit at higher volumes," Datawind CEO added. 

We will however have to wait to see how it fairs against the present tablets.



Comments

Popular posts from this blog

Racecraft (Project Koru) · Prologue — The Origin Story

Racecraft · Prologue , The Origin Story It Started With a Wine List and a Question About Racing How a happy-hour conversation in the Bay Area turned into a trustable AI race coach , and then into a second version that runs entirely on a phone, on the NPU. This is the prologue to a five-part series. Two years ago(1st November, 2024) I was in the Bay Area for a GDE Summit. If you've never been: it's a couple of days of talks among Google Developer Experts, the kind of people who get unreasonably excited about a new on-device runtime, and then , mercifully , a happy hour where everyone stops performing and just eats. We ended up at a restaurant(Puesto Santa Clara), a long table of GDEs, and I was doing the most important engineering of the evening: trying to decide which wine to order. Across the table was Ajeet Mirwani . I don't even remember how the wine talk turned into racing talk , these things drift , but the moment the word "racing" ...

The Throughput Trap: Benchmarking vLLM on OpenXLA and the Reality of Production LLM Serving

vLLM Systems · DevLab 2026, Deep Dive I was recently invited by the Google TPU team to speak at the OpenXLA Summer DevLab 2026 . This post breaks down our deep-dive evaluation of the matured vLLM + OpenXLA stack, the fundamental engineering mismatches between CUDA and XLA serving paths, and why traditional capacity metrics are lying to you. If you are operating large language models at enterprise scale right now, your platform architecture team is likely staring at a massive infrastructure crossroads: Should we migrate our core serving workloads from GPUs to TPUs? Historically, NVIDIA's CUDA ecosystem was the only serious option for user-facing, low-latency LLM generation. But here in 2026, the economics and infrastructure options have transformed. Google TPUs are highly available, cheaper per chip, and the open-source serving stack built around vLLM and OpenXLA has officially achieved absolute production readiness. Yet, when our infrast...

A Split‑Brain Neuro‑Symbolic Training Method for High‑Velocity Autonomous Coaching from Telemetry

 Author: Rabimba Karanjai Scope: Problem statement + data methodology + model training (no deployment discussion) Abstract Real‑time coaching in motorsport is a safety‑critical learning problem : a system must map noisy, high‑frequency telemetry to short, actionable guidance that remains physically consistent and avoids hazardous recommendations . This paper proposes a “Split‑Brain” training formulation that separates (i) a semantic coaching target (what action/critique should be expressed) from (ii) a reflexive interface (how actions are represented as compact, verifiable tokens). The approach trains a Small Language Model (SLM) in the Gemma family [1] using QLoRA fine‑tuning [2] , and introduces a telemetry tokenizer plus teacher‑student synthesis pipeline to generate instruction‑action pairs at scale. Core contribution: a reproducible method to convert “ golden lap ” differential tel...