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LLM 推理计算器 | LLM Inference Calculator
估算LLM推理所需的显存、延迟、TTFT、TPOT与吞吐量。
popopanda · HN
LLM Inference Calculator – Estimate VRAM, Latency, and Throughput
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估算LLM推理所需的显存、延迟、TTFT、TPOT与吞吐量。
popopanda · HN
LLM Inference Calculator – Estimate VRAM, Latency, and Throughput
追踪您的应用中使用的 AI 模型，并在其被弃用前获得警告。
taylorgt · HN
Find every AI model your code calls and warn before it's retired
LLM API支出分析仪表板，按模型和环境分类，含优化建议
ATsimbalistov · HN
Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to
用 LLM 评估 AI agent 对话质量，提供评分卡和成本分析。
@tech_maju · X
Format-agnostic LLM hub. Bring your own provider keys and route across Anthropic, OpenAI, ChatGPT/Codex, Kimi, Alibaba DashScope, and AWS Bedrock — with unified observability and c
@0xxmemo · X
查看LLM模型在10个基准问题上的评分和排名。
fristovic · HN
She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol
向多个前沿大模型提问，获得经过同行评审的综合答案。
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree
计算LLM微调和推理所需的GPU显存。
@FaiqAdzlan · X
Built a GPU VRAM calculator to try out Claude Code (#cursorfanboy). I wanted it as granular as possible so I based it off of LLMem) which really just covers fine-tuning tbh. Please tear it to shreds: GitHub:
用对抗测试检查LLM端点安全，获取OWASP审计报告。
@aryaan_sheth · X
- LLM security for small teams
对比450+个LLM API定价方案，计算实际月度成本，支持缓存和批处理定价。
u/Greywolff06 · Reddit
I built LLMPrice — a free calculator for comparing LLM API costs across 450+ pricing routes I kept running into the same problem when comparing LLM APIs: the headline token price doesn't always tell you what your actual workload will cost. Caching, batch pricing, reasoning tokens, retries, different endpoints, and OpenRouter routes can change the result quite a bit. So I built LLMPrice.com. You enter your workload once — requests, input/output tokens, caching, retries, etc. — and it com
使用一个 API 访问和切换多个 LLM 提供商，同时优化推理成本。
justin2025 · Product Hunt
Auriko Trading desk for LLM calls
Ornymo通过语义缓存减少LLM查询成本和延迟。
u/ornymo_official · Reddit
how to reduce ai costs there are lots of way to reduce costs but there all complex to setup i know this cause i tried one in production so i built ornymo we cache meaning not the exact string allowing us to give same awnsers thus reducing llm costs and latency check it out at ornymo.com free for a limited time and let me know your feedback submitted by /u/ornymo_official to r/buildinpublic [link] [comments]