Is 100K+ GPUs for Grok 3 value it?

With 3.3M+ folks watching the launch, Elon Musk and his crew launched the world to “Grok 3”, essentially the most succesful and highly effective mannequin by x.AI up to now. The corporate that began in 2023 and bought its final mannequin (Grok 2) out in 2024, is now difficult fashions by prime firms like OpenAI, Google, and Meta which have been within the AI race for the final 5-7 years. All because of over 100K H100 NVIDIA GPUs! However DeepSeek, which additionally began its work in 2023, achieved o3-mini degree capabilities with only a fraction of GPUs that Grok 3 did! On this weblog, we’ll discover if Grok 3 is value using 100K+ H100 NVIDIA GPUs.

What’s NVIDIA H100 GPU?

The NVIDIA H100 GPU is a high-performance processor constructed for AI coaching, inference, and high-performance computing (HPC). Being a successor to A100, it delivers quicker processing, higher effectivity, and improved scalability, making it a essential instrument for contemporary AI purposes. It’s utilized by AI firms and analysis establishments, together with OpenAI, Google, Meta, Tesla, and AWS, who depend on the NVIDIA H100 for growing cutting-edge AI options.

Additionally Learn: Intel’s Gaudi 3: Setting New Requirements with 40% Sooner AI Acceleration than Nvidia H100

Why Do AI Corporations Want It?

There are a number of explanation why main tech and AI firms all over the world are investing within the NVIDIA H100 Chips:

1. AI Coaching & Inference: The H100 is behind many superior AI fashions like GPT-4, Grok 3, and Gemini, because it minimizes coaching time and improves inference efficiency.
2. Excessive-Pace Processing: Geared up with 80GB of HBM3 reminiscence and a 3 TB/s bandwidth, together with NVLink (900 GB/s), the H100 ensures speedy knowledge motion and seamless multi-GPU operations.
3. Optimized for AI: That includes FP8 & TF32 precision with its Transformer Engine, it accelerates deep studying duties whereas sustaining effectivity and accuracy.
4. Cloud & HPC Purposes: Broadly utilized by cloud suppliers resembling AWS, Google Cloud, and Microsoft Azure, the H100 helps large-scale AI workloads and enterprise purposes.
5. Value & Power Effectivity: Constructed for top efficiency per watt, it reduces operational prices whereas maximizing computational energy, making it a sustainable alternative for AI infrastructure.

What Can 100K H100 GPUs Do?

100,000 H100 GPUs can break down large issues (like coaching refined AI fashions or operating advanced simulations) into many small duties, and work on them all of sudden. This extraordinary parallel processing energy means duties that will usually take a really very long time might be accomplished extremely quick.

Think about a easy process that takes 10 days to finish on a single H100 GPU. Now, let’s convert 10 days to seconds:

10 days ≈ 10 × 24 × 3600 = 864,000 seconds

If the duty scales completely, with 100,000 GPUs the time required can be:

Time = 864,000 seconds ÷ 100,000 = 8.64 seconds

So a job that will have taken 10 days on one GPU might, in idea, be accomplished in lower than 10 seconds with 100K GPUs working collectively!

Why Did Grok 3 Want 100K H100?

Grok 3 is a successor to Grok 2, a mannequin that did include options like picture era on prime of textual content. Nevertheless, as an entire, it was subpar when in comparison with prime fashions by OpenAI, Google, and Meta. That’s the reason for Grok 3, Elon Musk’s x.AI wished to catch up or in actual fact beat all the present opponents within the area. That’s the reason x.AI went massive! They created an information middle consisting of over 100K GPUs and expanded it additional to 200K GPUs. That’s the reason, in lower than a 12 months, they’ve been in a position to create Grok 3 – a mannequin able to superior reasoning, enhanced considering in addition to deep analysis.

The efficiency distinction between Grok 3 to Grok 2 is a transparent signifies this leap.

Virtually a 10-point soar throughout all main benchmarks together with Math, Science, and Coding! Spectacular proper? However is it spectacular sufficient for the computing energy of 100K H100 GPUs?

Additionally Learn: Grok 3 is Right here! And What It Can Do Will Blow Your Thoughts!

Grok 3 Comparability with DeepSeek-R1

When DeepSeek-R1 was launched, it took the world by storm! All main AI firms might really feel the warmth as a result of their falling inventory costs and reducing consumer base as folks flocked in the direction of the open supply marvel that challenged OpenAI’s better of the perfect! However to do that, did DeepSeek-R1 use 100K GPUs?

Properly, not even a fraction of it! DeepSeek-R1 has been fine-tuned on prime of the DeepSeek-V3 base mannequin. DeepSeek-V3 has been educated on simply 2048 NVIDIA H800 GPUs. (H800 GPUs are a China-specific variant of NVIDIA’s H100 GPUs, designed to adjust to U.S. export restrictions with a smaller inference time). This primarily implies that DeepSeek-R1 has been educated utilizing simply 2% of the computation in comparison with Grok 3.

As per the benchmarks, Grok 3 is considerably higher than DeepSeek-R1 throughout all main fronts.

However is it true? Is Grok 3 really higher than DeepSeek-R1 and the remainder of the opposite fashions because the benchmarks declare? Have been 100K H100 GPUs actually value it?

Additionally Learn: Grok 3 vs DeepSeek R1: Which is Higher?

Worth Verify: Grok 3 vs Different Main Fashions

We are going to check Grok 3 in opposition to the highest fashions together with o1, DeepSeek-R1, and Gemini fashions for numerous duties to see the way it performs. To do that I’ll evaluate Grok 3 with a distinct mannequin in every check, based mostly on the outputs I obtain from the 2 fashions. I will likely be evaluating the fashions on three completely different duties:

1. Deep Search
2. Superior Reasoning
3. Picture Evaluation

I’ll then choose the one which I discover higher based mostly on the outputs. 

Take a look at 1: Deep Search

Fashions: Grok 3 and Gemini 1.5 Professional with Deep Analysis

Immediate: “Give me an in depth report on the newest LLMs evaluating them on all of the obtainable benchmarks.”

Outcomes:

By Grok 3:

Report

By Gemini 1.5 Professional with Deep Search:

Report

Evaluation:

Clearly, on every criterion, the report generated by Gemini 1.5 Professional Deep Search was higher, extra inclusive,, and extra complete of all the main points round LLM benchmarks. 

Take a look at 2: Superior Reasoning

Fashions: Grok 3 and o1

Immediate: “If a wormhole and a black gap immediately come close to Earth from two opposing sides, what would occur?”

Outcomes:

Response by Grok 3:

Response by o1:

Evaluation:

The consequence generated by o1 is best as it’s extra detailed, scientific, and well-structured in comparison with the consequence given by Grok 3.

Additionally Learn: Grok 3 vs o3-mini: Which Mannequin is Higher?

Take a look at 3: Picture Evaluation

Fashions: Grok 3 and DeepSeek-R1

Immediate: “What’s the win likelihood of every crew based mostly on the picture?”

Outcomes:

Response by Grok 3:

Response by DeepSeek-R1:

Evaluation:

Though the consequence given by DeepSeek-R1 was extra correct, Grok 3 gave an excellent evaluation of the match based mostly on the picture.

Remaining End result: Grok 3 misplaced in 2 out of three duties when pitied in opposition to its opponents.

100K H100 GPUs: Was It Price It?

Now that we’ve seen how Grok 3 performs in opposition to opponents in numerous duties, the true query stays: Was the huge funding in over 100K H100 GPUs justified?

Whereas Grok 3 has demonstrated important enhancements over its predecessor and outperforms some fashions in particular areas, it constantly fails to dominate throughout the board. Different fashions, resembling DeepSeek-R1 and OpenAI’s o1, achieved comparable or superior outcomes whereas using considerably fewer computational sources.

Power Utilization

Past the monetary funding, powering and cooling an information middle with 100K+ H100 GPUs comes with an enormous vitality burden. Every H100 GPU consumes as much as 700W of energy beneath full load. Which means:

• 100K GPUs x 700W = 70 megawatts (MW) of energy consumption at peak utilization.
• That’s roughly equal to the electrical energy consumption of a small metropolis!
• Think about cooling necessities and the entire vitality consumption will increase considerably.

Grok 3’s energy-intensive strategy is probably not essentially the most sustainable. OpenAI & Google at the moment are focussing on smaller, extra environment friendly architectures and energy-optimized coaching methods, whereas x.AI has chosen brute-force computation.

Scalability and Effectivity Concerns

Coaching AI fashions at scale is an costly endeavor—not simply by way of {hardware} but additionally energy consumption and operational prices.

By comparability, firms like OpenAI and Google optimize their coaching pipelines by using mixture-of-experts (MoE) fashions, retrieval-augmented era (RAG), and fine-tuning methods to maximise effectivity whereas minimizing compute prices.

In the meantime, open-source communities are demonstrating that high-quality AI fashions might be constructed with considerably decrease sources. DeepSeek-R1 difficult business leaders whereas being educated on simply 2,048 H800 GPUs, is a chief instance of this.

Therefore, the event of a mannequin like Grok 3 raises main issues:

• Can x.AI maintain the monetary and environmental prices of operating a 200K-GPU infrastructure long-term?
• Might x.AI have achieved comparable outcomes with higher knowledge curation, coaching optimizations, or parameter effectivity reasonably than brute-forcing with GPUs?
• Would investing in additional environment friendly architectures have yielded higher outcomes?
• How sustainable is that this strategy in the long term, given the rising prices and competitors within the AI house?

Conclusion

Grok 3 marks a major leap for x.AI, demonstrating notable enhancements over its predecessor. Nevertheless, regardless of its 100K+ H100 GPU infrastructure, it did not constantly outperform opponents like DeepSeek-R1, o1, and Gemini 1.5 Professional, which achieved comparable outcomes with far fewer sources.

Past efficiency, the vitality and monetary prices of such large GPU utilization elevate issues about long-term sustainability. Whereas x.AI prioritized uncooked energy, rivals are reaching effectivity by optimized architectures and smarter coaching methods.

So, have been the 100K GPUs value it? We don’t assume so, at this level. If Grok 3 can’t constantly dominate, x.AI might have to rethink whether or not brute-force computation is the perfect path ahead within the AI race.

Ceaselessly Requested Questions