Latency vs cost isn’t just pricing—it’s hardware math. VRAM, context windows, and model density decide whether local GPUs beat API usage. For example, 4x RTX 5090 can hit about 167 t/s per user, while a single RTX PRO 6000 sits around 60 t/s if the model fits in memory [1]. Cache tricks matter too: offloading KV caches with LMCache can squeeze more parallelism across sessions [1]. Gemma 3 27B Q4 shows how density changes the break-even point across setups [1].
Hardware constraints - VRAM and context window limits gate throughput; dense models run faster when they stay on one card, MoE models benefit from more cards [1]. - The same math drives decisions at scale, whether you’re coding for 32 users or thousands.
Cost vs API economics - If you buy a second RTX 5090, break-even can range from around 1,000 to 20 days depending on the model, with daily electricity costs often cited as roughly $1/day in simple scenarios [2]. - API costs stack up too: Claude 4.5 Sonnet, GLM 4.6, Deepseek 3.1, Qwen3 Next Instruct, and GPT-OSS-120B each have daily price profiles; many teams compare API bills to the hardware’s operating cost [2]. - For long-term play, training your own LoRA with H100-class compute can tilt the math in favor of hybrids rather than pure on-device work [2].
Latency hacks and offline options - Speculative decoding—two-way ideas to draft tokens during typing—offers latency relief, but success varies by model and architecture [3]. - Real-world offline options exist: a local app runs on-device Qwen2-1.5B-Instruct-q4f16_1-MLC via a WebLLM stack, enabling offline responses [4].
Offline real-world example - The Flint app demonstrates an offline AI assistant path, pairing on-device models with a lightweight web app experience [4]. It’s a reminder that latency can be trimmed without cloud round-trips.
Closing thought: the winner isn’t one path—it's a blend of VRAM, context window sizing, and whether latency-first or cost-first wins in your use case [1][2][3][4].
References
Nice LLM calculator
Discussion of GPU VRAM limits, context windows, and model density affecting LLM throughput; Gemma 3 27B and Pro 6000 comparisons.
View sourceLLM recomendation
Discusses local GPUs vs API costs, model options, throughput, RAM, and LoRA training for structured outputs data extraction and automation.
View sourceWould it be theoretically possible to create a two-way speculative decoder to infer the user's next token while they're typing and generate the LLM's draft tokens in real-time before the user finishes then finalize the response once sent?
Discusses speculative decoding to reduce latency by drafting user and agent tokens in real-time; considers architecture, sampling, and latency tradeoffs
View sourceFree Wilderness Survival AI App w/ WebLLM Qwen
A free offline survival app using Qwen LLM supports guidance; debates hallucination risks, data-first approach, and cloud vs local models.
View source