Llama
also referred to as: llama models
includes Llama 3, Llama 2, Llama 4, Llama 3.1, Llama 1, Llama 70B, Llama 405B, Llama Stack, Llama 3.2, Llama 3.3, Llama 7B, Llama Guard and 5 more
23 statements across 21 episodes · 7 bullish · 7 bearish · 20 people on the record · first statement Dec 5, 2023 by Dylan Patel · said 455 times in 94 episodes since 2023 · across every show →
Mentions by year, the whole family
brought up most by Shawn Wang (63), Alessio Fanelli (35), Thomas Scialom (22), Nathan Lambert (21), Soumith Chintala (15), George Hotz (13), Yining Zhang (9), Lin Qiao (7)
tap a year for its mentions
2026 28 mentions in 15 episodes 2 per episode
- 🔬 "The Most Innovative Diffusion Research Is Happening in Drug Discovery, Not Image Generation"
- Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
- The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI
- AI Security After Codex and Claude Code — Zico Kolter & Matt Fredrikson, Gray Swan
- Mistral: Voxtral TTS, Forge, Leanstral, & Mistral 4 — w/ Pavan Kumar Reddy & Guillaume Lample
- Agent Inference at the "Speed of Light" — How NVIDIA moves like a $4.3 Trillion Startup
- The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
- The Future of AI Infra: from Kubernetes to Agent Sandboxes — Akshat Bubna, Modal CTO
- 7 more episodes that year, every mention in 2026 →
2025 90 mentions in 28 episodes 3 per episode
- DeepSeek V3, SGLang, and the state of Open Model Inference in 2025 (Quantization, MoEs, Pricing)
- Better Data is All You Need — Ari Morcos, Datology
- 2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents
- SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
- ⚡️Ranking Agentic LLMs — Pratik Bhavsar, Galileo
- The Utility of Interpretability — Emmanuel Amiesen
- The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)
- The Magic of LLM Distillation — Rishabh Agarwal, Google DeepMind
- 20 more episodes that year, every mention in 2025 →
2024 299 mentions in 43 episodes 7 per episode
- Training Llama 2, 3 & 4: The Path to Open Source AGI — with Thomas Scialom of Meta AI
- [LLM Paper Club] Llama 3.1 Paper: The Llama Family of Models
- Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI
- The Winds of AI Winter (Q2 Four Wars of the AI Stack Recap)
- The 10,000x Yolo Researcher Metagame — with Yi Tay of Reka
- The Origin and Future of RLHF: the secret ingredient for ChatGPT - with Nathan Lambert
- How to train a Million Context LLM — with Mark Huang of Gradient.ai
- A Brief History of the Open Source AI Hacker - with Ben Firshman of Replicate
- 35 more episodes that year, every mention in 2024 →
2023 38 mentions in 8 episodes 5 per episode
- Ep 18: Petaflops to the People — with George Hotz of tinycorp
- FlashAttention-2: Making Transformers 800% faster AND exact
- RAG is a hack - with Jerry Liu of LlamaIndex
- The End of Finetuning — with Jeremy Howard of Fast.ai
- The State of Silicon and the GPU Poors - with Dylan Patel of SemiAnalysis
- Beating GPT-4 with Open Source Models - with Michael Royzen of Phind
- RWKV: Reinventing RNNs for the Transformer Era
- Why AI Agents Don't Work (yet) - with Kanjun Qiu of Imbue
- every mention in 2023, scene by scene →
Everything said about Llama, oldest first
Dec 5, 2023 negative
Patel: Hugging Face libraries achieve only 15% MBU for inference
“Hugging Face's libraries are actually very inefficient, like incredibly inefficient for inference. You get like, 15% MBU on, on, on, on some configurations, like eight, eight, eight, eight, eight, eight, 100, and LLAMA-seventy-beat, you get like, 15%, which is…”
Dec 5, 2023
Jan 11, 2024 neutral
Lambert: RLHF Performance Depends on Data and Systems Over RL Details
“It really ends up being kind of, like, gibberish that I think is less important now, because it's more about data and infrastructure than RL details, than, like, value functions and everything. A lot of the papers have different terms in the equations. I think…”
Feb 7, 2024 bullish
Future Llama models will match frontier GPT models if progress slows
“As AI progress slows down, so if we get like Llama-IV, Llama-V for example, maybe it's a comparable at that point, like GPT-V or GPT-VI, like, It made it to the point where it was like, look, I just want to use Lama. Like, it's, you know, safe for me to, you k…”
Mar 6, 2024 neutral
Chintala: Time and data constrain Meta LLM releases more than GPUs
“So, I think the, it's all a matter of time. I think time is the biggest bottleneck. It's like, when do you stop training the previous one, and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, bet…”
Mar 27, 2024 bearish
Pure-play foundation model companies will be commoditized by Llama and big tech
“I think pure play foundation model companies are just gonna be pinched by how good The next couple of llamas are going to be, and the next, like, what next good open source thing, and then seeing the really big players put ridiculous amounts of compute behind …”
Jun 11, 2024 negative
Jul 5, 2024 neutral
Oct 18, 2024
Drew Houston brings an external GPU on planes to run Llama locally
“When I'm on a plane or something or where, like, you don't have access or the Internet's not reliable, I actually bring a gaming laptop on the plane with me. It's, like, a little, like, blue briefcase-looking thing, and then I, like, literally hook up a GPU, l…”
Nov 1, 2024 neutral
Angelopoulos: The Chatbot Arena leaderboard is currently not an apples-to-apples comparison
“None of the leaderboard currently is apples to apples, because you have, like, Gemini Flash, you have, you know, all sorts of tiny models, like Llama Like, eight B and four or five B are not apples to apples.”
Dec 7, 2024 positive
Cerebras WSE-3 runs Llama inference 70x faster than NVIDIA GPUs
“Cerebris came out that the wafer scale engine three can serve llama 70 B at 2.1 thousand sorry, 202,100 tokens per second and serves llama four or five B at nearly 1000 tokens per second. So this, you know, to give you an understanding, like this is about 70 t…”
Dec 23, 2024 negative
Soldani: Llama and Qwen models fail OSI open source AI definition
“Under this definition, for example, Lama or some of the Quen models are not open source because the license says you can, you can't use this model for this, or it says if you use this model, you have to name the output this way or derivative needs to be named …”
Dec 24, 2024 bullish
Dec 24, 2024 neutral
Jan 19, 2025 negative
Mar 23, 2025 positive
Agarwal: Synthetic data distillation bypasses model vocabulary and tokenizer mismatches
“The one nice thing about this kind of distillation is it doesn't matter if you have a vocabulary mismatch, because we're not using the next token distribution or probability labels. You can distill from one model which uses some random tokenizer to another mod…”
Jul 14, 2025 bearish
Jul 31, 2025 negative
Feb 5, 2026 positive
Mar 8, 2026 positive
DeepSeek 128k context fits in 8GB KV cache versus Llama's 80GB
“For context like the total I think the total context length of DeepSeq is a 128,000 tokens, or it might be 256,000 with rope extension. That entire context, I think it's a 128,000, fits into eight gigabytes. And previously context, like I think the Lama four o…”
Mar 30, 2026 positive
Lample: Post-training breakthroughs like DPO were impossible without open LLaMA
“And if you look at many of the techniques that were developed after, for instance, Temma was open source, like all these post-training approaches like even DPOD, like performance optimization, all of this were done by people that had access to this model, and …”
Jun 1, 2026 neutral