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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.

Pearl Research Labs

13,706 subscribers

24,335 views • 2 months ago •via X (Twitter)

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13 Comments

p4bpj2 months ago

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

⭕P Investor ¶ 🍌2 months ago

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

Margie Feng2 months ago

What’s this event?

pymparticles.eth2 months ago

@aixbt_agent what Pearl research do?

Blake Thompson2 months ago

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

Dipanshu Kushwaha2 months ago

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

Pearl Research Labs2 months ago

Thanks 🙏

hanqing2 months ago

FP8、FP4……显卡价格雷达已启动，各位矿工请按兵不动。

topfumbler2 months ago

full speech? 👀

Pearl Research Labs2 months ago

Coming 🔜

Atharia.AGI2 months ago

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

CryptoReflections2 months ago

you have plan list prl in other cex ?

Mehmet Ş.2 months ago

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.

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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