bfloat16 floating-point format

And what will that mean? Just better results, greater efficiencies, which as we know is a huge issue right now, or both, or what will be the result of that?

Right now, I think the large language models are being mostly trained on 16-bit, mostly Google's BFloat 16, if you're familiar with that.

It looks like a 32-bit float with the bottom bits kind of sawed off like a shotgun.

That gives you enough dynamic range, but it's not really ideal for...

There was a...

I did try with the BF16, the full format.

It's a little slower, but FP8's a little faster, less degradation, but...

Eventually, I'm sure someone will come up with a Blackwell tune like the NVF P4 format.

If you are doing standard supervised fine-tuning of an eight billion parameter model, you configure four-bit QLoRA array.

Because the thirty ninety and forty ninety are Ampere and Ada Lovelace architectures, the source says you must set BF16 true to use native bfloat16 precision.

Right.

That's crucial.

Bfloat16 allocates more bits to the exponent, offering a much wider dynamic range, which prevents training spikes from overflowing the data type.

But because the older T4 physically doesn't have the hardware to process the bfloat16 exponent structure natively, the source says you must force standard float sixteen.

You set FP16 true and BF16 false.

If you don't do this, the GPU has to emulate bfloat16 in software, which causes massive crippling performance penalties.

Note, though, that none of these are necessary for justifying that any existing open model satisfies the definition.

Packing Bits Together Gemma activations are BF16, 16-bit, so we can pack 2 per 32-bit word.

Then, for operations like reductions, the first level of gates can perform the reduction on 4 operands at once, instead of 2.

So, we can do such reductions at depth log k, 1 instead of depth log k. Fusing gates together.

To implement a sliding attention block, we first compute the Q, K, and V projections.

Each output coordinate is a dot product over the D equals 1,152 input coordinates.

The inputs are BF16 activations, so two pack into each 32-bit word, and the multiplications by e-constant weights fuse into the first level of gates.

Depth log 1152-1 is equal to 10.

The post examined how different levels of quantization impact the performance of the QEN 3.827B model.

The author tested models with 8-bit, 4-bit, 2-bit, and 1-bit quantizations using various benchmarks and found that 4-bit models performed similarly to the full BF16 model, but 1-bit models degraded to random guessing levels.

Longer tasks and reasoning efforts affected model results, especially at the 2-bit level, but 4-bit quantization remains effective for most tasks and fits within consumer GPU memory.

The study also discussed costs, hardware considerations, and the nonlinear damage caused by extreme quantization levels.

It only requires four steps.

And then for each one, they've also released a BF16 variant and an FP8 variant.

For example, for the turbo FP8 variant, you can see that the transformer model is only like 6.7 gigabytes in size.

So this should be able to fit on most consumer GPUs.

Now let's talk hardware because this is what makes Carnes interesting for people who aren't running data centers.

The full BF16 checkpoint is around fifty-five gigabytes, but Carnes also ships in GGUF format, which means you can run quantized versions on consumer hardware.

Here's how the sizes break down.

Q-four_k_m is fifteen point six six gigabytes, smallest recommended.

If weight values cluster tightly, say close to a Gaussian or Laplacian shape instead of spreading uniformly across every possible pattern, most of those bit patterns barely ever get used.

BF16 allocates 16 bits per weight regardless of that.

The measured entropy might come out to 10 or 11.

That gap between stored width and measured entropy is wasted space you can, in principle, recover.

Okay, the entropy story is airtight, and the kernel engineering is genuinely clever.

But Ada, I went back through Table 2 with a red pen, and something's bugging me.

As you flagged earlier, that headline 10x is a ceiling for low-bit formats, but the only full end-to-end serving numbers they actually show are on BF16 models, where the entropy gap is only about 1.5x. Did they ever actually stack this codec on top of an already quantized model? INT4, AWQ, smooth quant, where the entropy gap is supposedly 6 to 10x?

No, and that's the honest read here.

Is the practical translation of a benchmark score.

On throughput, the reviewer got roughly 30 tokens slash second from BF16 on Vell LM.

80 to 120 from Quen's FP8.

And a peak near 206 averaging about 173 from SGLang's NVFP4.