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TL;DR
Recent developments in quantization techniques, especially trained-in quantization and dynamic mixed-precision, are enabling large language models to run efficiently on consumer hardware. Kimi K3 exemplifies this shift, with models trained directly at low precision, reducing memory needs significantly.
Recent advances in quantization techniques now allow large language models (LLMs) to be trained and shipped at native low-precision formats, such as MXFP4, significantly reducing their memory footprint. This marks a shift from traditional post-training quantization to trained-in quantization-aware training, exemplified by models like Kimi K3, which are optimized during training for low-precision deployment. This development impacts the feasibility of running frontier-scale models on consumer hardware, including Macs and GPUs, without extensive hardware resources.
Historically, models were released at full precision (FP16 or BF16), then quantized afterward to reduce size for inference. The common approach was post-training quantization (PTQ), which applied lossy compression after training. However, recent models like Kimi K3 are trained with low-precision formats (MXFP4 and MXFP8) from the outset, a process known as quantization-aware training (QAT). This means the models are inherently optimized for low-precision deployment, making further compression less effective or even damaging if applied uniformly.
One of the key breakthroughs is dynamic, mixed-precision quantization. Instead of uniformly reducing all weights to 1 or 2 bits, this approach selectively preserves critical layers at higher precision while compressing the rest, validated against lossless reference models. This technique enables models like Kimi K3 to be stored at about 1.4TB in 4-bit format, a significant reduction from the 5.6TB native FP16 size, allowing efficient inference on consumer devices with limited memory.
Quantization is the lever that turns a model needing a datacenter into one needing a workstation. In 2026 it stopped being a simple after-the-fact shrink — and Kimi K3 is the clearest example of why.
Quantization stores the same weights at coarser precision. Fewer bits per weight means less memory and bandwidth, and slightly less accuracy. The size scales almost linearly with bit-depth.
bytes ≈ parameters × bits ÷ 8. K3 figures are Unsloth-reported for the 2.8T model.“Quantized” isn’t one thing. The format decides which hardware, which loader, and which trade-offs you get.
For years, labs shipped at FP16 and the community shrank the model afterward. Kimi K3 inverts that — and it changes the advice.
- Precision reduced after the model is trained
- Exploits the slack between FP16 and 4-bit
- “Just download a smaller quant” — the old default
- K3 ships natively at MXFP4, MXFP8 activations
- The compression was spent before release
- Can’t be squeezed further uniformly — the slack is gone
If K3 can’t be squeezed uniformly, how does a 594GB 1-bit build exist? Mixed precision — most weights at 1–2 bits, the load-bearing layers upcast to 8-bit, the whole thing measured against a lossless reference.
Both distort the simple bytes-equals-params-times-bits math, and both bite hardest on the frontier models people most want to run.
The abstractions resolve into a hard boundary. Drawn on a 512GB M3 Ultra:
Choosing a quant is choosing a point on a curve — steep at the ends, flat in the middle.
Now the frontier labs are spending the compression before you download it.
Impact of Native Low-Precision Training on Model Deployment
This shift to trained-in quantization fundamentally changes how large models are deployed locally. It enables running frontier-scale models on hardware with limited RAM and VRAM, such as consumer Macs and GPUs, without the need for extensive hardware upgrades. For users and developers, this means broader accessibility to advanced LLMs, lower costs, and more flexible deployment options. It also signals a move toward more integrated training and inference workflows, reducing the reliance on post-hoc compression that can degrade model accuracy.
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Evolution of Quantization Techniques in Large Language Models
Until recently, the standard was to train models at high precision (FP16/BF16) and then apply post-training quantization (PTQ) to compress them for inference. Formats like GGUF and MLX were used for different hardware targets, with PTQ methods like GPTQ and AWQ optimizing for GPU inference. The emergence of trained-in quantization, especially in models like Kimi K3, marks a significant departure, as models are now designed during training to operate efficiently at low precision. This approach was made possible by advances in hardware-native formats like MXFP4, which are accelerated directly on GPUs like Blackwell-class chips.
"The compression that normally shrinks a model after release was already spent during training in models like Kimi K3, making further uniform quantization less effective."
— Thorsten Meyer
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Unresolved Questions About Low-Precision Model Stability
It remains unclear how well trained-in quantization approaches will scale to future models beyond Kimi K3, especially regarding stability and accuracy across different architectures. The long-term impact of native low-precision training on model robustness and generalization is still being studied, and support for these formats across various hardware and inference frameworks is evolving.
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Next Steps for Quantization and Model Deployment
Researchers and developers will likely focus on refining dynamic mixed-precision quantization techniques, improving hardware acceleration for formats like MXFP4, and expanding support across inference frameworks. Additionally, further training of large models using native low-precision formats is expected to become more widespread, potentially leading to new standards in model deployment and optimization. Monitoring hardware support and software tooling updates will be crucial for broader adoption.
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Key Questions
How does trained-in quantization differ from post-training quantization?
Trained-in quantization involves optimizing the model during training to operate effectively at low precision, whereas post-training quantization compresses a fully trained model afterward, often with some loss of accuracy.
Why is native low-precision training important for local inference?
It reduces memory and computational requirements, enabling large models to run efficiently on consumer hardware without sacrificing stability or accuracy.
What hardware formats support these low-precision models?
Formats like MXFP4 and MXFP8 are designed for acceleration on modern GPUs such as Blackwell-class chips and are supported by inference frameworks like MLX and custom loaders.
Will this approach make large models more accessible to the public?
Yes, by reducing hardware requirements, trained-in quantization can democratize access to frontier-scale models for individual users and smaller organizations.
Are there any limitations or risks associated with native low-precision training?
Potential challenges include ensuring model robustness and stability across diverse architectures, as well as widespread software support, which are still being actively researched.
Source: ThorstenMeyerAI.com
