Tech News
Back to home
Technology

How to run an LLM on your laptop

Source

MIT Technology Review

Published

TL;DR

AI Generated

The article discusses running local language model (LLM) models on personal devices as an alternative to using online models from big companies like ChatGPT. Local models offer privacy benefits and control over user experience. The barrier to entry for running local models has decreased, making it accessible to users with laptops or smartphones. Opting for local models can help users understand the limitations and behaviors of larger online models. Tools like Ollama and LM Studio make it easier to download and run LLMs, catering to both proficient coders and non-coders. Running local models can be a fun and educational experience for those interested in exploring AI technology.

Read Full Article

Similar Articles

Disaggregating LLM Inference: Inside the SambaNova Intel Heterogeneous Compute Blueprint

Disaggregating LLM Inference: Inside the SambaNova Intel Heterogeneous Compute Blueprint

SambaNova Systems and Intel have introduced a blueprint for heterogeneous inference that optimizes modern large language model (LLM) workloads by utilizing specialized hardware for different phases of inference: GPUs for prefill, SambaNova RDUs for decode, and Intel Xeon 6 CPUs for agentic tools and orchestration. This approach addresses the complexity of agentic AI systems with varying compute demands. By isolating tasks onto specific hardware, the architecture improves efficiency, scalability, and cost-effectiveness. The design reflects a shift towards specialized compute fabrics and better supports the evolving landscape of AI reasoning systems.

SemiWiki•
SemiEngineering

Silent Data Corruption: A Major Reliability Challenge in Large-Scale LLM Training (TU Berlin)

Researchers at Technische Universitat Berlin published a technical paper on the challenges of Silent Data Corruption (SDC) in Large Language Model (LLM) training. As LLMs grow in size, hardware-induced faults like SDC can bypass detection mechanisms, leading to severe consequences during training. The study explores how intermittent SDC impacts LLM pretraining, highlighting the sensitivity of different factors like bit positions and kernel functions. The research proposes a lightweight detection method to identify harmful parameter updates and demonstrates the effectiveness of recomputing training steps upon detection in mitigating corruption.

SemiEngineering•
Automated Security Assertion Generation Using LLMs (U. of Florida)

Automated Security Assertion Generation Using LLMs (U. of Florida)

A technical paper titled "Assertain: Automated Security Assertion Generation Using Large Language Models" by the University of Florida introduces Assertain, an automated framework that generates security properties and SystemVerilog Assertions for hardware designs. By leveraging large language models and self-reflection refinement, Assertain improves assertion quality and reduces manual effort in hardware security verification. In evaluations on 11 hardware designs, Assertain outperformed GPT-5 in correct assertion generation, unique CWE coverage, and architectural flaw detection. The framework significantly enhances vulnerability coverage in hardware security verification.

SemiEngineering•
How SW and HW Vulnerabilities Can Complement LLM-Specific Algorithmic Attacks (UT Austin, Intel et al.)

How SW and HW Vulnerabilities Can Complement LLM-Specific Algorithmic Attacks (UT Austin, Intel et al.)

A technical paper titled “Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems” by UT Austin, Intel Labs, Symmetry Systems, Microsoft, and Georgia Tech explores how software and hardware vulnerabilities can combine with LLM-specific algorithmic attacks to compromise the integrity of compound AI pipelines. The paper demonstrates two novel attacks that leverage system-level vulnerabilities along with algorithmic weaknesses to breach AI safety and confidentiality. By systematically analyzing attack primitives and mapping vulnerabilities to different stages of an attack lifecycle, the paper emphasizes the importance of addressing traditional vulnerabilities for robust defense strategies in the future.

SemiEngineering•

We use cookies

We use cookies to ensure you get the best experience on our website. For more information on how we use cookies, please see our cookie policy.