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Today, the Pearl Protocol relies on INT-based matrix multiplication, which requires model quantization. To eliminate this overhead, we are developing our next-generation Proof of Useful Work to natively support floating-point computations. Launching soon, the initial release will support FP8, with FP4 to follow.

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Pearl Research Labs

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29,684 Aufrufe • vor 3 Monaten •via X (Twitter)

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13 Kommentare

Profilbild von p4bpj
p4bpjvor 3 Monaten

Isn‘t quantization the future? But fp8 and fp4 will also be awesome. Does fp4 support nvfp4?

Profilbild von ⭕P Investor ¶ 🍌
⭕P Investor ¶ 🍌vor 3 Monaten

How will you solve the non deterministic results of the half percussion multiplication on the node validation side?

Profilbild von Margie Feng
Margie Fengvor 3 Monaten

What’s this event?

Profilbild von pymparticles.eth
pymparticles.ethvor 3 Monaten

@aixbt_agent what Pearl research do?

Profilbild von Blake Thompson
Blake Thompsonvor 3 Monaten

Exciting progress toward more efficient AI compute. Native floating-point support could be a major step forward.

Profilbild von Dipanshu Kushwaha
Dipanshu Kushwahavor 3 Monaten

That's awesome! Looking forward to seeing how this makes things even better. Sounds like a big step forward.

Profilbild von Pearl Research Labs
Pearl Research Labsvor 3 Monaten

Thanks 🙏

Profilbild von hanqing
hanqingvor 3 Monaten

FP8、FP4……显卡价格雷达已启动，各位矿工请按兵不动。

Profilbild von topfumbler
topfumblervor 3 Monaten

full speech? 👀

Profilbild von Pearl Research Labs
Pearl Research Labsvor 3 Monaten

Coming 🔜

Profilbild von Atharia.AGI
Atharia.AGIvor 3 Monaten

So can I train my own llm using this while still minting $PRL?

Profilbild von CryptoReflections
CryptoReflectionsvor 3 Monaten

you have plan list prl in other cex ?

Profilbild von Mehmet Ş.
Mehmet Ş.vor 3 Monaten

Shit coin

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Proof-of-Useful-Work forced us to invent a quantization format that did not exist. Pearl Protocol adds noise to both the weights and activations before matrix multiplication. That creates a constraint ordinary inference stacks do not have - the model values and the noise must fit together inside INT8. So we quantize the model to an effective W7A7 range, reserving one bit of headroom for the noise. The perturbed operands still fit in INT8, multiplication uses INT8 inputs with INT32 accumulation, and the low-rank noise can then be peeled off exactly, recovering the exact result of the quantized computation. Existing quantization for INT7 were unsatisfactory, so we built our own - a non-trivial fusion of GPTQ-style weight reconstruction, SmoothQuant-style activation smoothing and custom inference kernels. The result is an end-to-end W7A7 pipeline that, in our internal benchmarks, even outperforms many FP8 baselines (in quality and in efficiency). We are writing a paper detailing this technique so stay tuned. Looking forward, W7A7 is only an intermediate step. Our upcoming upgrade is designed to support FP8 workloads directly, eliminating the need for the extra integer-quantization step.
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