Llmspeed lets buyers preview local LLM speeds across GPUs, Macs and cloud hardware

What the tool does

Buying hardware for local AI inference has always involved a certain amount of guesswork: reviews cover only a handful of configurations, and a spec sheet says little about how a particular model will behave in daily use. Llmspeed, an open-source project that debuted on Hacker News on October 17 as a "Show HN" post, tries to close that gap. The site lets visitors pick a hardware platform and a language model, then gives them a sense of how quickly that pairing would generate text, so a purchase decision can rest on the lived experience rather than a bare benchmark table.

The hardware and model matrix

According to the project's site, coverage is broad on both axes. On the GPU side it ranges from budget options such as the RTX 3060 12GB and RTX 5060 Ti 16GB through the RTX 3090, RTX 4090 and flagship RTX 5090, plus the workstation-class RTX PRO 6000 96GB and the DGX Spark 128GB. AMD is represented by the RX 7900 XT, RX 7900 XTX, RX 9070 XT and Radeon AI PRO R9700, while Intel appears with the Arc Pro B70 32GB.

Apple Silicon gets extensive coverage, from the MacBook M2 Pro 16GB and M3 Max 48GB up to the MacBook Pro M5 Max 128GB, the Mac mini M4 Pro 64GB and Mac Studio machines with 512GB of unified memory in both M3 Ultra and M5 Ultra variants. There is also an AMD Ryzen AI Max+ 395 (Strix Halo) 128GB entry, a CPU-only configuration built around a Ryzen 9 9950X with 96GB of DDR5, and cloud GPUs in the form of H100, A100 and H200 instances for comparison.

On the model side, the list mixes small workhorses like Llama 3.1 8B, Qwen 3.5 9B and the DeepSeek R1 distill at 14B with a range of mid-size dense and mixture-of-experts models, and extends to larger options including GPT-OSS 120B, Gemma 4 31B, DeepSeek V4 Flash, GLM-5.3 Flash and Qwen variants at 27B and 35B.

Where the numbers come from

The project is upfront about its methodology. Figures backed by real runs come from community benchmark sources such as r/LocalLLaMA and localscore.ai, while the remainder are inferred from comparable configurations or extrapolated from hardware specifications. Every figure assumes a short prompt processed on one device, so longer contexts and multi-GPU setups will land elsewhere. The code is open source and hosted on GitHub.

That blend is worth keeping in mind when using the tool. Entries grounded in community measurements sit alongside projections, and the site frames its results as estimates, so shoppers should treat the numbers as a rough guide rather than a guarantee of what a new machine will deliver.

Why it matters

Local inference has moved from a curiosity to a practical option, but the hard part is matching model to machine. Memory capacity determines what fits, memory bandwidth largely determines how fast it runs, and the interaction between the two is difficult to reason about from spec sheets alone. A buyer choosing between a 24GB RTX 4090, a 128GB Strix Halo system and a 512GB Mac Studio is really choosing which models they can run at all and how patiently they will wait for answers.

By making that trade-off tangible before the money leaves the account, and by leaning on community benchmark data rather than vendor marketing, Llmspeed condenses hours of scattered forum research into a single comparison. Its main limitation is inherited from that same data: figures for less common configurations are projections, and real-world results will shift with quantisation choices, context length and the software stack in use.