Local AI benefits are already extensive
Local AI refers to open-source AI models that you can host on your own systems—enabling your creative and production teams without relying on large hosted models from OpenAI or Anthropic. Performance for open-source models like Llama, Qwen, and DeepSeek is often equivalent to publicly hosted AI services like ChatGPT or Sonnet 4 for many workloads.
The benefits of locally hosted AI include:
- Compliance & privacy: Running models inside your own trust boundary simplifies data residency and audit concerns. These traits are especially important in finance, healthcare, and government sectors.
- No token ceilings: Publicly hosted models (Open AI, Anthropic etc.) often throttle performance during heavy usage. This requires you to either stop work for a period of time, or purchase more credits (or both!). Such limits don’t exist with local hosting.
- Right-sizing: Not every task needs a1-trillion-parameter giant. Smaller models trained to support specific tasks have snappy latency and often better performance than their huge, remotely hosted, general-purpose counterparts.
Rapid innovation is propelling local AI viability
Quantization Innovations Mean Faster Processing
Quantization improvements (reducing the size and memory needs for hosting AI) have made hosting large parameter models much more viable.
Recent cutting-edge techniques such as activation-aware weight quantization (AWQ) now let us squeeze large language models to lower precision thresholds (3 or 4-bit) without sacrificing accuracy. In many benchmarks, the quality drop is well under 1%—a dramatic improvement for local AI. AWQ has rapidly matured and won a Best Paper award at MLSys 2024.
Hardware requirements are Decreasing
You no longer need a data-center budget to run serious models. A single Mac Studio with 512 GB of unified memory can host models with tens (or low hundreds) of billions of parameters. Improved availability of high-VRAM workstations or short-term cloud rentals make private LLM hosting/prototyping more accessible than ever before.
Openly available local models are catching up on agent leaderboards
Don’t take our word for it! LLMs are frequently compared/contrasted on AI leaderboards. These provide valuable insight on performance gaps and model selection as capabilities continue to evolve.
Open models are catching up with—and even surpassing—the performance of the large premium models (GPT, Claude Sonnet etc.) for many workloads, and often without the uncomfortable limitations.
Are you ready to adopt local AI?
Whether you’re augmenting an existing SaaS product with an on-premise AI inference tier or experimenting with private code-assistants that never leave your subnet, open-source AI ecosystems have matured to the point that “build vs. buy” is a real choice.
Axian can help
If you’re interested in integrating open-source AI models into your products or software-development lifecycle without the headaches of designing and building it all yourself, let’s talk. My team and I have helped organizations of every size, for multiple purposes across many industries, make the best use of emerging technology. We would love to help you chart the fastest path from prototype to production.