I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-4o-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

**What I shipped**

* **New Voice Agent** in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
* Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
* A **Voice Playground**: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

**Talk to it (free, in the browser)** — 4 demos, tap the mic:

* **Aria** — customer support triage
* **Nova** — B2B discovery caller
* **Kai** — Spanish conversation tutor
* **Echo** — daily standup coach

Open one, talk to it, and fork it into your own workspace if you like it.

* Voice Playground → [https://agentswarms.fyi/voice-playground\](https://agentswarms.fyi/voice-playground)
* Build your own (New Voice Agent) → [https://agentswarms.fyi/agents\](https://agentswarms.fyi/agents)
* Docs → [https://agentswarms.fyi/docs/voice\](https://agentswarms.fyi/docs/voice)

*Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.*


I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

What I shipped

• New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
• Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
• A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

Talk to it (free, in the browser) — 4 demos, tap the mic:

• Aria — customer support triage
• Nova — B2B discovery caller
• Kai — Spanish conversation tutor
• Echo — daily standup coach

Open one, talk to it, and fork it into your own workspace if you like it.

• Voice Playground → https://agentswarms.fyi/voice-playground
• Build your own (New Voice Agent) → https://agentswarms.fyi/agents
• Docs → https://agentswarms.fyi/docs/voice

Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

What I shipped

• New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
• Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
• A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

Talk to it (free, in the browser) — 4 demos, tap the mic:

• Aria — customer support triage
• Nova — B2B discovery caller
• Kai — Spanish conversation tutor
• Echo — daily standup coach

Open one, talk to it, and fork it into your own workspace if you like it.

• Voice Playground → https://agentswarms.fyi/voice-playground
• Build your own (New Voice Agent) → https://agentswarms.fyi/agents
• Docs → https://agentswarms.fyi/docs/voice

Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

What I shipped

• New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
• Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
• A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

Talk to it (free, in the browser) — 4 demos, tap the mic:

• Aria — customer support triage
• Nova — B2B discovery caller
• Kai — Spanish conversation tutor
• Echo — daily standup coach

Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

What I shipped

• New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
• Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
• A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

Talk to it (free, in the browser) — 4 demos, tap the mic:

• Aria — customer support triage
• Nova — B2B discovery caller
• Kai — Spanish conversation tutor
• Echo — daily standup coach

Open one, talk to it, and fork it into your own workspace if you like it.

• Voice Playground → https://agentswarms.fyi/voice-playground
• Build your own (New Voice Agent) → https://agentswarms.fyi/agents
• Docs → https://agentswarms.fyi/docs/voice

Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.

Hey everyone,

A few months ago, We shared the visual canvas we built for AgentSwarms. The response was incredible, but the most common piece of feedback was: "The visual canvas is great for architecture, but I need to see the actual code to really understand how to deploy this."

You wanted deep-dive, code-first labs—the kind you see on DeepLearning.ai—but for multi-agent systems, faster and with more flexibility.

We’ve spent the last few weeks heads-down engineering a completely new Interactive Notebooks section. As of today, we have 67 TypeScript-based notebooks live on the site (with more dropping soon).

What’s in the library: We’ve covered everything from basic LangChain fundamentals to complex enterprise-level multi-agent workflows. Everything runs entirely in your browser using TypeScript—no Docker, no Python venv, no local dependencies.

A personal favorite: I’m particularly excited about the "Failure Mode & Error Handling" notebook.

We’ve all seen agents that work perfectly in a demo but crash in production the moment a tool times out or an LLM returns garbage. This notebook walks through:

• How to build deterministic validation gates between nodes.
• How to force an orchestrator to "catch" a worker failure and dynamically re-route or re-prompt.
• How to handle state recovery when a multi-agent loop gets stuck in a hallucination cycle.

Why we built this: I’m tired of seeing AI "tutorials" that are just static blog posts. To master Agentic AI, you need to be able to tweak a system prompt, break the code, watch the error trace, and fix the routing logic in real-time.

The entire library of 67 labs is 100% free to use.

If you’re currently wrestling with how to make your agents production-grade, I’d love for you to check them out and let me know if there’s a specific "failure mode" or architecture pattern you’d like us to add to the next batch of notebooks.

Try it out here: agentswarms.fyi

If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems.

That’s why we builtAgentSwarms.fyi—the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows.

🚀 The Core AgentSwarms Ecosystem:

• Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments.
• Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics.
• Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments.

💣 The New Drop: 60+ Browser-Native TypeScript Notebooks

We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction.

Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem:

• 🟢 LangChain.js (Fundamentals & Middleware Guardrails)
• 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration)
• 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals)
• ⚡ Vercel AI SDK (Streaming UI Integration)
• 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops)

Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms.

👉 Dive in for free: agentswarms.fyi/learn

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide