moonshotai/Kimi-K2-Thinking
Text Generation
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Florian Zimmermeister PRO
flozi00
AI & ML interests
ASR,
German LLM
Recent Activity
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3 days ago
moonshotai/Kimi-K2-Thinking
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4 days ago
I just got asked about the differences between Blackwell systems and Grace Blackwell systems. What's the difference and how much of a performance gap is there between them?
https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison
Here's a summary of the key points from the article:
GB200 (Grace Blackwell) is a Superchip: It integrates a Grace CPU and two Blackwell GPUs into a single package.
B200 is a GPU-only module: It's designed to be paired with x86 or ARM CPUs in more traditional server setups.
Performance and Efficiency:
Based on MLPerf Training v5.0 benchmarks, the article concludes:
GB200 systems are approximately 42% more efficient than B200 systems on average. This is especially true in large-scale deployments (100+ GPUs), where the GB200's integrated design and high-speed NVLink interconnect provide a significant advantage.
In smaller, single-node systems (e.g., 8 GPUs), the performance difference is much smaller, around 10-15%.
Use Cases:
Choose GB200 for large-scale AI clusters, training massive models, and when maximum efficiency is the top priority.
Choose B200 for smaller deployments, when you need the flexibility to choose your own CPU, or for mixed AI and HPC workloads.
replied to
their
post
4 days ago
Some weeks ago, i've just decide its time to leave LinkedIn for me.
It got silent around my open source activities the last year, so i thought something has to change.
That's why my focus will move to share experiences and insights about hardware, drivers, kernels and linux. I won't post about how to use models, built agents or do prompting. I want to share about some deeper layers the actual hypes are built on.
I will start posting summarizations of my articles here on the hub.
English version:
https://flozi.net/en
German translated version:
https://flozi.net/de
Feel free to reach me if you want to read something specific.
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3 days ago
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I just got asked about the differences between Blackwell systems and Grace Blackwell systems. What's the difference and how much of a performance gap is there between them?
https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison
Here's a summary of the key points from the article:
GB200 (Grace Blackwell) is a Superchip: It integrates a Grace CPU and two Blackwell GPUs into a single package.
B200 is a GPU-only module: It's designed to be paired with x86 or ARM CPUs in more traditional server setups.
Performance and Efficiency:
Based on MLPerf Training v5.0 benchmarks, the article concludes:
GB200 systems are approximately 42% more efficient than B200 systems on average. This is especially true in large-scale deployments (100+ GPUs), where the GB200's integrated design and high-speed NVLink interconnect provide a significant advantage.
In smaller, single-node systems (e.g., 8 GPUs), the performance difference is much smaller, around 10-15%.
Use Cases:
Choose GB200 for large-scale AI clusters, training massive models, and when maximum efficiency is the top priority.
Choose B200 for smaller deployments, when you need the flexibility to choose your own CPU, or for mixed AI and HPC workloads.
https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison
Here's a summary of the key points from the article:
GB200 (Grace Blackwell) is a Superchip: It integrates a Grace CPU and two Blackwell GPUs into a single package.
B200 is a GPU-only module: It's designed to be paired with x86 or ARM CPUs in more traditional server setups.
Performance and Efficiency:
Based on MLPerf Training v5.0 benchmarks, the article concludes:
GB200 systems are approximately 42% more efficient than B200 systems on average. This is especially true in large-scale deployments (100+ GPUs), where the GB200's integrated design and high-speed NVLink interconnect provide a significant advantage.
In smaller, single-node systems (e.g., 8 GPUs), the performance difference is much smaller, around 10-15%.
Use Cases:
Choose GB200 for large-scale AI clusters, training massive models, and when maximum efficiency is the top priority.
Choose B200 for smaller deployments, when you need the flexibility to choose your own CPU, or for mixed AI and HPC workloads.
replied to
their
post
4 days ago
Definitely, a lot of ads, spam, blenders and political discussions. Huggingface papers, articles and x are my main sources for news.
published
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5 days ago
updated
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Space
5 days ago
posted
an
update
7 days ago
Post
3074
Some weeks ago, i've just decide its time to leave LinkedIn for me.
It got silent around my open source activities the last year, so i thought something has to change.
That's why my focus will move to share experiences and insights about hardware, drivers, kernels and linux. I won't post about how to use models, built agents or do prompting. I want to share about some deeper layers the actual hypes are built on.
I will start posting summarizations of my articles here on the hub.
English version:
https://flozi.net/en
German translated version:
https://flozi.net/de
Feel free to reach me if you want to read something specific.
It got silent around my open source activities the last year, so i thought something has to change.
That's why my focus will move to share experiences and insights about hardware, drivers, kernels and linux. I won't post about how to use models, built agents or do prompting. I want to share about some deeper layers the actual hypes are built on.
I will start posting summarizations of my articles here on the hub.
English version:
https://flozi.net/en
German translated version:
https://flozi.net/de
Feel free to reach me if you want to read something specific.
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