Alternatives
Products that do what Finite Field Assembly:Emulate GPU on CPU does
A new programming language rooted in Pure Mathematics
- 1AC
2020 · github.com
- 2AL
2019 · dev.to
- 3
- 4AM
2018 · lambdaway.free.fr
- 5FA
2020 · github.com
- 6AA
2017 · github.com
- 7AM
2017 · github.com
- 8PB
2021 · github.com
- 9OD
2017 · octaspire.com
- 105L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 110X
2019 · github.com
- 12GA
2015 · github.com
- 13FF
I am playing around with using arrays of arbitrary dimension as framework for designing FFT implementations, as opposed to the more classical approach of tensor products and butterflies (too complicated in my opinion). It turns out, that with a modern compiler, you do not need much complexity to make a really fast implementation. This implementation is for powers of 2, and optimized for arrays that do not fit in cache. I do think it would be better to use a higher-level language to implement other cases (e.g. n = 2^a * 3^b * 5^c, multiple small FFTs, higher-dimensional), so I am currently…
Oct 2025 · gitlab.sac-home.org
- 14FA
2015 · github.com
- 15CK
2025 · tensara.org
- 16FF
2016 · github.com
- 17IT
2017 · youtube.com
- 18CA
2020 · rxi.itch.io
- 19AG
2019 · made2591.github.io
- 20LF
I submitted an earlier version of this a few months ago (as llama2.f90). At that time it had a lot of steps to run and was just a toy, now it's easy to run and is a competitive option for llm inference. See the motivation section for discussion and the `Performance` issue for an ongoing discussion about performance.
2023 · github.com
- 21AT
2017 · logicpundit.com
- 22HF
We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models. There's how it works: 1. You upload the dataset with preconfigured format into HuggingFaсe [1]. 2. Choose your LLM (e.g. LLaMa 70B, Mistral 7B) 3. Place your submission into the queue 4. Wait for it to get trained. 5. Then you get your trained model there on HuggingFace. Essentially, why would we want to do it? 1. We already have an experience with training big LLMs. 2. We could achieve near-perfect infrastructure performance for training. 3. Sometimes GPUs have just nothing to…
2023 · higgsfield.xyz
- 23IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 24CR
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
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