Life & fun · alternatives · 2026
24 alternatives to Power-Performance Curves for CPUs
Hi HN! Following the release of the latest generation of CPUs by AMD and Intel with rather high power requirements and the corresponding uptake in interest of undervolting / power level adjustment, I was interested to find out in more detail how these CPU models would perform and compare at different power settings. However, it couldn't find an online source that would provide a satisfactory amount of data to be really useful to compare different models. So I took what data I could find to set up a Bayesian regression model. Find the results and more details on the linked page. Hope…
Power-Performance Curves for CPUs is a tool that models how different CPU models from AMD and Intel perform at various power settings. It uses Bayesian regression to analyze performance data and enable comparisons… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Power-Performance Curves for CPUs launched in 2023; newer entries below may have overtaken it.
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Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. - Andyyyy64/whichllm
May 2026 · github.com · its alternatives →
- 2
The overall idea is to chart out the thousands of Mini PCs by benchmark and reveal the Pareto Front so you can get the most Compute per Dollar. Definitely a labor of love as I have a number of Mini PCs for my "homelab" (TrueNAS, piHole, Plex, basic stuff). It uses Gemini to extract specs from listings (since they're not often strongly categorized). Quick blog post here: https://luke.zip/posts/pareto-pcs/
Jun 2026 · minipcs.zip · its alternatives →
- 3RH
Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research. My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The…
Oct 2025 · sturdystatistics.com · its alternatives →
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I replicated David Ng's RYS method (https://dnhkng.github.io/posts/rys/) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76…
Mar 2026 · github.com · its alternatives →
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Pick hardware and a model, and feel the real speed of a local LLM before you buy. Simulates tokens/sec and time-to-first-token from verified benchmarks for GPUs, Macs, and more.
6d ago · llmspeed.dev · its alternatives →
- 6IM
2023 · vram.asmirnov.xyz · its alternatives →
- 7HP
2018 · scss.tcd.ie · its alternatives →
- 8
Weights & Biases▲110Developer tools for deep learning & machine learning
2019 · wandb.ai · its alternatives →
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This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com · its alternatives →
- 10
Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!
2024 · github.com · its alternatives →
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TensorFlow Lite▲163Low-latency inference of on-device ML models
2017 · developers.google.com · its alternatives →
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Simple CPU model written in 60 lines of C with 8 registers and basic instructions - wkoszek/cpu60
2015 · github.com · its alternatives →
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🐢 bayesAB: Fast Bayesian Methods for A/B Testing. Contribute to FrankPortman/bayesAB development by creating an account on GitHub.
2017 · github.com · its alternatives →
- 14
CPU, GPU and SoC comparisons, ratings and benchmarks
2025 · cputronic.com · its alternatives →
- 15
Run processes on the "Icestorm" efficiency cores of the M1 Mac chip - runbg.swift
2021 · gist.github.com · its alternatives →
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The fastest CPU benchmark tool on the web
2023 · 5sbm.com · its alternatives →
- 18
Vibe profile your ML models to get max performance.
2025 · github.com · its alternatives →
- 19
The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.
Mar 2026 · github.com · its alternatives →
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I built a tiny 8-bit CPU simulator in Python to better understand how computers work at a low level. It visualizes registers, memory, and instructions in real-time, so you can actually see each operation as it happens. You can write simple assembly code and watch how the CPU executes it step by step. The project is mainly for learning and experimentation, but I’d love feedback or ideas for improvement.
Oct 2025 · github.com · its alternatives →
- 21
Hi HN! We’re Arthur and Adrien from CodSpeed. We’re building a tool measuring software performance before any production deployment, catching performance regressions before they hit production environments and reporting performance changes directly in Pull Request comments. It’s kind of like Codecov but for performance measurement. Today, the go to solution to measure performance is probably to use an APM(DataDog, Sentry, …), continuously analyzing your production environment. However, since those solutions are operating on real environments they need real users to experience poor…
2023 · codspeed.io · its alternatives →
- 22WM
Pick a Mac and a model, get the memory it needs and the tokens/sec to expect. Covers M1 to M6, every unified memory size, Q4 to FP16. Formula fitted and validated on our own measurements on two machines.
6d ago · macyou.co · its alternatives →
- 23
Github: https://github.com/errorcodezero/turbine
2025 · errorcodezero.dev · its alternatives →
- 24
Also compare
- Find the best local LLM for your hardware, ranked by benchmarks alternatives
- MiniPCs.zip – Charting the Pareto frontier of Mini PCs alternatives
- Research Hacker News, ArXiv & Google with Hierarchical Bayesian Models alternatives
- Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training alternatives
- Llmspeed – feel how fast a local LLM runs before you buy the hardware alternatives
- I made a GPU VRAM calculator for transformer-based models alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →