Albin Östman

engineering physics

[Albin Östman @nicorobinreal]

@vibelancer Hey thats my site 🔥

[vibelancer @vibelancer]

same 8B model. 69 tok/s on an RTX 5090, 63 on a Mac Studio M3 Ultra. then you paste 12,500 tokens and one starts answering in 2s, the other in 12s

llmspeed is a static site that streams an answer at the tok/s your rig would really hit, after the real wait for the first token. 30 GPUs, Macs and CPUs × 15 models · 1,446 combos. no backend. no signup. Vite + Preact, MIT, one json of predictions

most people still buy hardware off one tok/s screenshot from reddit. llmspeed shows the number that screenshot hides

the idea: a spec sheet lies by omission. a stopwatch doesn't

someone dropped it on Show HN with the whole pipeline open: the benchmark csv, the coefficients and the json the site reads

what runs under the hood:

1. DECODE model: gen speed = memory bandwidth ÷ active weights read per token. CUDA lands at 31% of spec bandwidth, Metal 47%, ROCm 57%. MoE counts only active params

2. PREFILL model: bound by FP16 TFLOPS. Llama 3.1 8B Q4 at 12,500 tokens → 7,029 tok/s on the 5090 vs 1,080 on M3 Ultra → first token at 2.08s vs 11.88s. CPU-only 9950X: 62.9s

3. FIT gate: weights + KV cache + 0.6 GB overhead vs usable memory · 75% of unified memory on Mac · 93% of VRAM on GPU. GLM-5.3 Flash Q8 needs 341.7 GB → 5090 has 29.8 → won't run. M5 Ultra 512GB has 384 → fits

4. OFFLOAD model: spilled weights stream from system RAM at 48.3 GB/s, calibrated on 48 measured runs. GPT-OSS 120B on a 5090 needs 64.1 GB → ~38 tok/s, range 14–61

5. TRUST labels: 1,446 combos → 76 measured · 385 derived · 985 won't run. unreleased hardware = projected. drivers and thermals still move real numbers 10–30%

6. RACE mode: two setups, same prompt, side by side, shareable link. alone each number is a spec. together they're a decision

r/LocalLLaMA + localscoreai → submissions.csv → coefficients → app-data.json → stream

RTX 5090 · Qwen 35B A3B Q4 → 383 tok/s · Qwen 27B dense Q4 → 42 tok/s · same card, 9× gap from 3 characters in the model name

the novelty is not the benchmark. the novelty is feeling 11.88 seconds of nothing before you pay for it

[Ahmed @iAhmed_ai]

Before buying hardware for a local model, https://t.co/iwt70GpY3E estimates tokens/sec for 35+ setups, from an RTX 3090 and Strix Halo to the M5 Ultra and DGX Spark. Numbers come from community benchmarks; open source by nicorobinreal on GitHub.

[Robert Sasu | dev/acc @SasuRobert]

llmspeed shows how fast a local model types on a given machine, before you buy it.

question for the local crowd:
what tokens/sec is your floor for coding?

[Albin Östman @nicorobinreal]

@annanidev Hey thats my site 🔥

[Annani @annanidev]

The Llmspeed simulator will happily stream you fake tokens at 60 tok/s for a config that real agentic workloads will run at 20. The tool simulates single-GPU, single-cached-context setups and defaults to very small quants, so its numbers are optimistic by design.

Real agent harnesses push 60k-140k prompt tokens with a few thousand output tokens, and that long context plus KV cache pressure typically cuts benchmark decode throughput to half or a third. Several users report practical numbers far below simulator output: a 35B MoE on 8GB VRAM gets 16-19 tok/s, and a 127B MoE on 10GB VRAM plus system RAM gets 40-50 tok/s at 65k context.

Before you buy hardware for local AI coding, test your actual model on OpenRouter or a cheap cloud box with your real prompt lengths. Then divide the vendor's or simulator's tok/s by three and see if you can still live with it.

[Albin Östman @nicorobinreal]

@Stargridcompass 🔥

[高运通 @Stargridcompass]

准备买 GPU 本地部署大模型的，可以先试试这个开源工具，帮你提前判断设备值不值得买。

llmspeed 可以模拟不同硬件运行大模型的速度，它会基于真实基准数据，在屏幕上实时模拟文本逐字蹦出的流速。

支持约 30 种 GPU、Mac 和 CPU，覆盖 15 款主流模型。

可以查看：
每秒生成多少 Token
首个 Token 需要等待多久
显存能否装下模型
不同硬件同时运行的速度对比

甚至能把 RTX 5090 和 Mac Studio 放在一起模拟对跑。
数据基于实测与性能模型估算，实际速度可能有偏差。

在线体验：https://t.co/mjdCaOBChq
GitHub：https://t.co/QSURnjQDpj

[Albin Östman @nicorobinreal]

@jurlycat Hey thats my site 🔥

[Jurly @jurlycat]

See how fast a local LLM would feel on your next GPU or Mac before buying it.

llmspeed lets you pick hardware, a model, and quantization, then simulates the answer streaming at its estimated speed including the wait for the first token.

Race two setups side by side. Check whether the model fits in memory or needs offloading to system RAM.

Each estimate is labeled Measured, Derived, or Projected, so you can distinguish exact benchmark results from estimates based on similar setups or hardware specs.

[PM「AI」0818 @lc0818036]

买显卡、买Mac跑本地大模型之前，先花1分钟试试这个👇

Llmspeed：选好硬件+模型，直接模拟出真实的出字速度和首字延迟。数据来自实测跑分，RTX 3090/5090、Mac Studio、DGX Spark都有。

做硬件PM最怕：花几万买回来，一跑卡成PPT。免费，HN 261分。
https://t.co/CbtTBba6b6

今日AI热榜Top10🧵

[Albin Östman @nicorobinreal]

@lc0818036 🔥

[Albin Östman @nicorobinreal]

@LescotEtienne 🔥

[Lescot Etienne @LescotEtienne]

Trending in 15 s: llmspeed, feel local LLM speed before you buy.

Same model, same prompt: RTX 4090 1.8s vs MacBook M4 Pro 5.0s. Then a Mac Studio M5 Ultra takes the next race in 1.5s.

@nicorobinreal

[iamcurtismith.base.eth @iamcurtismith]

llmspeed by @nicorobinreal shows how fast a local model would stream on a given gpu or mac before you buy it. best point in the hn thread: for agentic coding, prefill speed matters more than output speed.

tag @iamcurtismith + "engagement bot" to ask me

[Albin Östman @nicorobinreal]

https://t.co/z8ZefuRnrt

[Albin Östman @nicorobinreal]

12 hours on the HN front page:

186 points
6,700+ visitors
10,000+ simulated runs

Didn't expect this many people wondering if they should buy a 5090 or a Mac Studio 😅

[Albin Östman @nicorobinreal]

@iamcurtismith 🔥

[Albin Östman @nicorobinreal]

200GB model. 32GB of VRAM. 💀

(M5 Ultra speeds projected, since it isn't out yet)

Try any GPU/Mac: https://t.co/zjRyb6fo1M

[Albin Östman @nicorobinreal]

RTX 5090 vs M5 Ultra Mac Studio.
Running GLM-5.3 Flash.

The 5090 didn't even make it to the start line. 💀