Uploaded August 2026 | Updated September 2026, 2 weeks ago
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
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I tested NVIDIA Nemotron 3.5 Lightning against Meta's Muse Glimmer 30B on my own machine and ran a full benchmark suite on both.
kgptalkie.com/tutorials/generative-ai/nemotron-3-5-lightning-vs-muse-glimmer-30b
Both models fit on a single RTX 5090 with 32 GB of VRAM, so everything ran locally at a 64,000 token context window. The suite covers needle in a haystack retrieval at 40,000 tokens, math, word problems, a logic riddle, JSON extraction, a knowledge check and a small coding task. Both models answered correctly. The differences showed up in generation speed, VRAM usage and how many tokens each one burns thinking before it replies. I also explain why the architecture causes those gaps.
Everything in this video runs on your own hardware. No cloud API, no paid account.
📺 Full playlist: youtube.com/playlist?list=PLc2rvfiptPSReropGbvDFpB6dneNBwqhD
⏱ Chapters:
0:00 Two new local models this week
0:50 Why both fit on a single RTX 5090
1:34 Nemotron vs Muse Glimmer: mixture of experts vs dense
2:58 How I built the benchmark
4:13 Needle in a 40,000 token haystack
4:48 Generation speed: 120 vs 76 tokens per second
5:30 What happens when the context grows
6:04 Why Mamba 2 hybrid holds its speed
7:27 VRAM: 17 GB vs filling all 32 GB
8:28 Thinking tokens: who overthinks the easy tasks
10:15 Which model you should pick
11:16 Request a model for the next benchmark
🔗 What I tested:
Nemotron 3.5 Lightning (NVIDIA): mixture of experts, Mamba 2 hybrid, text only
Muse Glimmer 30B (Meta): dense transformer, text and image, 128K context
GPU: single RTX 5090, 32 GB VRAM
Context window: 64,000 tokens on every run
The numbers: Nemotron held 120 to 125 tokens per second and did not slow down as context grew. Muse Glimmer sat around 76 to 78 and fell to roughly 20 on long context, because it keeps a standard KV cache that grows with the input. Muse Glimmer used about 17 GB of VRAM. Nemotron filled the whole 32 GB card, so a 24 GB GPU like a 4090 will offload to CPU.
📺 Watch next:
NVIDIA Nemotron 3 Nano Omni: youtube.com/watch?v=G8vPTtWeTzw
🎓 Go deeper, my Udemy course:
Master Langchain v1 and Ollama, Chatbot, RAG and AI Agents: kgptalkie.com/langchain
Hit like if this saved you a 20 GB download. Tell me in the comments which model you want benchmarked next and I will run it on this machine. Subscribe and turn on the bell if you follow the local LLM series.
#Nemotron #MuseGlimmer #LocalLLM #RTX5090
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
----------
I tested NVIDIA Nemotron 3.5 Lightning against Meta's Muse Glimmer 30B on my own machine and ran a full benchmark suite on both.
kgptalkie.com/tutorials/generative-ai/nemotron-3-5-lightning-vs-muse-glimmer-30b
Both models fit on a single RTX 5090 with 32 GB of VRAM, so everything ran locally at a 64,000 token context window. The suite covers needle in a haystack retrieval at 40,000 tokens, math, word problems, a logic riddle, JSON extraction, a knowledge check and a small coding task. Both models answered correctly. The differences showed up in generation speed, VRAM usage and how many tokens each one burns thinking before it replies. I also explain why the architecture causes those gaps.
Everything in this video runs on your own hardware. No cloud API, no paid account.
📺 Full playlist: youtube.com/playlist?list=PLc2rvfiptPSReropGbvDFpB6dneNBwqhD
⏱ Chapters:
0:00 Two new local models this week
0:50 Why both fit on a single RTX 5090
1:34 Nemotron vs Muse Glimmer: mixture of experts vs dense
2:58 How I built the benchmark
4:13 Needle in a 40,000 token haystack
4:48 Generation speed: 120 vs 76 tokens per second
5:30 What happens when the context grows
6:04 Why Mamba 2 hybrid holds its speed
7:27 VRAM: 17 GB vs filling all 32 GB
8:28 Thinking tokens: who overthinks the easy tasks
10:15 Which model you should pick
11:16 Request a model for the next benchmark
🔗 What I tested:
Nemotron 3.5 Lightning (NVIDIA): mixture of experts, Mamba 2 hybrid, text only
Muse Glimmer 30B (Meta): dense transformer, text and image, 128K context
GPU: single RTX 5090, 32 GB VRAM
Context window: 64,000 tokens on every run
The numbers: Nemotron held 120 to 125 tokens per second and did not slow down as context grew. Muse Glimmer sat around 76 to 78 and fell to roughly 20 on long context, because it keeps a standard KV cache that grows with the input. Muse Glimmer used about 17 GB of VRAM. Nemotron filled the whole 32 GB card, so a 24 GB GPU like a 4090 will offload to CPU.
📺 Watch next:
NVIDIA Nemotron 3 Nano Omni: youtube.com/watch?v=G8vPTtWeTzw
🎓 Go deeper, my Udemy course:
Master Langchain v1 and Ollama, Chatbot, RAG and AI Agents: kgptalkie.com/langchain
Hit like if this saved you a 20 GB download. Tell me in the comments which model you want benchmarked next and I will run it on this machine. Subscribe and turn on the bell if you follow the local LLM series.
#Nemotron #MuseGlimmer #LocalLLM #RTX5090
