--- title: Show HN: Llmspeed – feel how fast a local LLM runs... description: A web tool lets users pick a hardware setup and an LLM model to simulate expected tokens-per-second and time-to-first-token before purchasing hardware for... canonical: https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware | daily.dev og:description: A web tool lets users pick a hardware setup and an LLM model to simulate expected tokens-per-second and time-to-first-token before purchasing hardware for... og:url: https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm og:image: https://api.daily.dev/og/posts/ORTos33sm.png og:image:alt: Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware **[Hacker News](https://daily.dev/sources/hn)** · 1 min read · 0 upvotes · 0 comments ## Summary A web tool lets users pick a hardware setup and an LLM model to simulate expected tokens-per-second and time-to-first-token before purchasing hardware for local LLM inference. Estimates are based on community benchmarks from sources like r/LocalLLaMA and localscore.ai, with some figures derived or projected from similar specs. The project is open source and available on GitHub. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I estimate LLM inference speed on a GPU before I buy it A tool called llmspeed lets you pick a hardware configuration and an LLM model to simulate expected tokens-per-second and time-to-first-token for a short prompt. Estimates draw on community benchmarks from sources like r/LocalLLaMA and localscore.ai, with unmeasured configurations derived or projected from similar specs. It covers GPUs, Macs, and other devices, and the project is open source on GitHub. _daily.dev surfaces tools like this for developers sizing hardware before a local LLM purchase._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 66 comments across hackernews (as of 2026-10-17). **TL;DR:** People found the idea of simulating local LLM speed before buying hardware genuinely useful, but many reported the tool's numbers didn't match their own real-world benchmarks and asked for more configuration options (multi-GPU, custom quants, context size, harness details). **Sentiment:** 25% positive · 55% mixed · 20% skeptical **The case for** - Several found it solves a real problem of not wanting to buy expensive hardware or download huge models just to test speed. - Some said it gave a useful directional sense of hardware tradeoffs (e.g., quantization vs. tokens/second). **The pushback** - Multiple commenters reported real-world tokens/second on their own hardware diverged notably from the tool's estimates. - Several requested missing configurations like multi-GPU/hybrid setups, PCIe/NVLink options, custom quantizations, and specific hardware (Strix Halo, DGX Spark, Snapdragon X Elite). - Some argued the tool ignores prefill/context-window realism and real agentic workloads where long context tanks throughput far more than shown. - One user noted the tool incorrectly claimed a model/GPU combination 'won't run' when it actually works via CPU offloading. **By community** - hackernews (mixed): Appreciation for the concept is paired with frequent pushback that benchmark numbers don't match personal experience and that key configuration options are missing. **Hottest debate:** Whether benchmark-derived tokens/second numbers reflect real-world usage, especially under long context and agentic workloads, versus the simplified scenarios the tool simulates. **Open questions** - How accurate are the simulated numbers against real hardware across different quantizations and context lengths? - Will multi-GPU, hybrid CPU/GPU, and newer hardware (Strix Halo, DGX Spark, Snapdragon X Elite) configurations be added? - Can the tool account for harness/inference-engine differences and parallel/concurrent usage rather than a single cached chat? **Highlights** > This is great. It solves two of my issues: 1. It is difficult to figure out if dropping $10k+ on hardware will make a meaningful difference for running local models. 2. Downloading each model and each quantization of that model to see the speed seems rude to free services like hugging face, so now I can see things like "with my hardware, going to a Q8 only results in 3 more tokens per second" My one complaint is picking my hardware (9070XT) and Qwen3.6 27B results in the message: > This combination won't run. Qwen 3.6/3.8 27B at FP16 needs 54.6 GB — the AMD RX 9070 XT 16GB has 14.9 GB usable. No quantization of this model fits; pick a smaller model or more memory. While it is true that it won't fit in your GPU memory, it demonstrably does run on my hardware. Llama.cpp supports putting some layers on the GPU and running others on the CPU. > — [tom\_alexander on hackernews](https://news.ycombinator.com/item?id=50130918) > A random number I checked feels kinda optimistic, or there's something wrong with my setup; Gemma 26B-A4B on a 16GB 5060Ti is reported to get 21-88tok/s with Q4_K_M, meanwhile I can just barely eke out 70tok/s with Q3 (which fully fits in vram), and 40tok/s with Q4 (with recently-merged MoE cache on llama-cpp). I guess the average fits, but the peak feels too high. No clue about details, but Sebastian Raschka's site reports its kv cache usage at 210kB per token, which is like 6x more than this site claims ("0.273 GB per 8k"). Also, the site claims qwen 27b to use 2x more memory per token than gemma, which is the opposite from what I know. > — [adrian17 on hackernews](https://news.ycombinator.com/item?id=50130953) > By running vllm at work I learned that you will usually outperform benchmarks which you will find on the internet. Mostly by running proper parameter tuning. So you get impressive numbers. Still in reality those numbers do not tell the whole truth. We found that real usage is more 60k-140k prompt with a few k of output for realistic agentic coding workloads. Most of the time that will give you half to a third of decode throughput vs the benchmark. It also becomes much more about cache aware routing and kv cache management (how to offload and restore). Because prefilling 60-140k will simply take too long for interactive work. > — [Jabdoa2 on hackernews](https://news.ycombinator.com/item?id=50131687) > It would be great if numbers weren't off, would include optimised versions (ie. speculative decoding), context size, include allowed parallel inference (single agents use parallel inference a lot) etc. I don't think in general people care about long list of models, they just care about the best one. Also dual gpu is common setup, there should be ie. 2x 4090. > — [mirekrusin on hackernews](https://news.ycombinator.com/item?id=50129157) > I don't know what basis these numbers are estimated on, but my M2 Ultra 128GB runs almost twice as fast as what's reported for the M3 Ultra 512GB. I wished there was such a tool before picking my hardware, it's useful. But the estimates needs to be somewhat correct. > — [pil0u on hackernews · 1 comments](https://news.ycombinator.com/item?id=50128532) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=50127979) · 186 points · 66 comments - [hackernews](https://news.ycombinator.com/item?id=49492103) · 2 points · 0 comments ## Similar posts on daily.dev - [tokenspeed — feel LLM tokens-per-second](https://daily.dev/posts/tokenspeed-feel-llm-tokens-per-second-wqp3nschy) · Hacker News · 0 upvotes · 0 comments - [Local LLMs Are Getting Easier: The Complete Guide \(2026\)](https://daily.dev/posts/local-llms-are-getting-easier-the-complete-guide-2026--gaggzz9j2) · SitePoint · 1 upvotes · 0 comments - [Stop guessing which local AI models fit your hardware — this free tool does it for you](https://daily.dev/posts/stop-guessing-which-local-ai-models-fit-your-hardware-this-free-tool-does-it-for-you-g0pvmddqa) · XDA Developers · 0 upvotes · 0 comments - [The tokenomics of self-hosted LLMs](https://daily.dev/posts/the-tokenomics-of-self-hosted-llms-rmgohnaqs) · Red Hat Developer · 0 upvotes · 0 comments --- Tags: [#hardware](https://daily.dev/tags/hardware), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#local-ai](https://daily.dev/tags/local-ai) [View this post on daily.dev](https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware","url":"https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm"},"datePublished":"2026-10-17T10:19:01.279Z","dateModified":"2026-10-17T16:34:48.889Z","description":"A web tool lets users pick a hardware setup and an LLM model to simulate expected tokens-per-second and time-to-first-token before purchasing hardware for...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cef74e9c47cf13c8473900a4c97794da?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cef74e9c47cf13c8473900a4c97794da?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Hacker News","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hacker News","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn","url":"https://daily.dev/sources/hn"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"hardware,gpu,ai-inference,local-ai","timeRequired":"PT1M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hacker News","item":"https://daily.dev/sources/hn"},{"@type":"ListItem","position":3,"name":"Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/show-hn-llmspeed-feel-how-fast-a-local-llm-runs-before-you-buy-the-hardware-ortos33sm#faq","mainEntity":[{"@type":"Question","name":"How can I estimate LLM inference speed on a GPU before I buy it","acceptedAnswer":{"@type":"Answer","text":"A tool called llmspeed lets you pick a hardware configuration and an LLM model to simulate expected tokens-per-second and time-to-first-token for a short prompt. 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