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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 次观看 • 2 个月前 •via X (Twitter)

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13 条评论

p4bpj 的头像

p4bpj2 个月前

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

⭕P Investor ¶ 🍌 的头像

⭕P Investor ¶ 🍌2 个月前

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

Margie Feng 的头像

Margie Feng2 个月前

What’s this event?

pymparticles.eth 的头像

pymparticles.eth2 个月前

@aixbt_agent what Pearl research do?

Blake Thompson 的头像

Blake Thompson2 个月前

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

Dipanshu Kushwaha 的头像

Dipanshu Kushwaha2 个月前

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

Pearl Research Labs 的头像

Pearl Research Labs2 个月前

Thanks 🙏

hanqing 的头像

hanqing2 个月前

FP8、FP4……显卡价格雷达已启动，各位矿工请按兵不动。

topfumbler 的头像

topfumbler2 个月前

full speech? 👀

Pearl Research Labs 的头像

Pearl Research Labs2 个月前

Coming 🔜

Atharia.AGI 的头像

Atharia.AGI2 个月前

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

CryptoReflections 的头像

CryptoReflections2 个月前

you have plan list prl in other cex ?

Mehmet Ş. 的头像

Mehmet Ş.2 个月前

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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