AI Speed (TPS) Simulator
Don't understand the benchmarks? Feel them. Adjust the slider to see how fast different AI hardware generates text in real-time.
Don't guess benchmarks — feel the speed of Llama 3, Mixtral, and RTX Hardware.
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Why TPS (Tokens Per Second) Matters More Than You Think
When researching hardware for Local AI, benchmarks often throw around raw numbers like "30 T/s" or "100 T/s." But what do those numbers actually mean for your daily workflow? Tokens Per Second (TPS) is the definitive measurement of how fast an AI model generates text (inference speed). A "token" is roughly equivalent to 0.75 of a word, or a single syllable.
The "Reading Speed" Threshold
The average human reads at roughly 5-8 tokens per second (approx. 200-250 words per minute). Therefore, your hardware needs to clear specific thresholds to feel natural:
- Under 10 T/s (Laggy): The AI writes slower than you can read. Waiting for code snippets or long emails feels frustrating and bottlenecks your productivity.
- 15-30 T/s (The Goldilocks Zone): The AI writes slightly faster than you read, creating a smooth, conversational feel identical to using ChatGPT Plus on the web.
- 50+ T/s (Instantaneous): Ideal for developers and heavy researchers. At this speed, massive codebases or long document summaries appear almost instantly, allowing you to skim results without waiting.
Apple Silicon vs. Dedicated GPUs
While Nvidia RTX cards (like the 4090) dominate raw TPS speeds due to extreme memory bandwidth (GDDR6X), Apple's M-Series chips offer a different advantage: Unified Memory. An M3 Max can run massive 70B parameter models that would otherwise require multiple RTX cards. However, if you are configuring a Mac for AI, you must carefully review the Mac RAM requirements for local LLMs on Apple Silicon to ensure you have enough unified memory to prevent severe TPS throttling.
2026 AI Hardware Benchmarks (Llama-3 8B)
Below is the average real-world performance for running an 8B parameter model at Q4_K_M quantization.
| Hardware | VRAM | Avg Speed | Experience |
|---|---|---|---|
| CPU Only (Intel/AMD) | N/A | 2 - 6 T/s | Unusable |
| MacBook Air M2/M3 | Unified | 18 - 25 T/s | Smooth Reading |
| NVIDIA RTX 4060 Ti | 16GB | 40 - 50 T/s | Fast |
| NVIDIA RTX 4090 / 5090 | 24GB+ | 85 - 120 T/s | Instant |
Frequently Asked Questions (AI TPS)
What is a good TPS for conversational AI?
For a natural chat experience, you need a minimum of 15-20 TPS. This perfectly matches the average human reading speed, allowing you to read the output comfortably as it streams on the screen. Anything below 10 TPS will feel noticeably slow.
Why is my RTX 4090 generating text so slowly?
If a high-end GPU is generating at less than 10 TPS, you are likely experiencing "CPU Offloading." This happens when the model (plus its context window) exceeds your GPU's total VRAM capacity, forcing the system to fall back on your much slower system RAM (DDR5). Try using a smaller, highly quantized model.
Does system RAM speed affect Tokens Per Second?
System RAM speed (e.g., DDR4 vs DDR5) only affects TPS if your GPU runs out of VRAM and relies on CPU offloading. If your model fits entirely inside your GPU's VRAM, your system RAM speed is completely irrelevant to generation speed. VRAM memory bandwidth is the ultimate bottleneck.
What TPS do I need for coding and RAG workflows?
For automated coding, Retrieval-Augmented Generation (RAG), or summarizing large documents, you should aim for 40+ TPS. In these workflows, you aren't reading the text as it generates; you are waiting for the final output block. Faster TPS significantly reduces wait times for complex agentic tasks.
What is the difference between Prompt Processing and Generation TPS?
When you look at terminal logs, you will often see two speeds. Prompt Processing (Prefill) is how fast the AI reads and ingests your initial question—this is usually incredibly fast (often 500+ TPS). Token Generation (Decoding) is the speed at which the AI actually types out its answer one word at a time. When people discuss "Local AI speed," they are almost always referring to the slower Generation TPS.
Will my TPS drop if multiple users connect to my local AI server?
Yes. If you host a local model using software like Ollama or vLLM and multiple people prompt it simultaneously, your GPU has to process a larger "batch size." It must split its compute power and memory bandwidth between the active requests. A hardware setup that generates 40 TPS for a single user might drop to 10 TPS per person if four users are generating text at the exact same time.
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