With 3.3M+ folks watching the launch, Elon Musk and his crew launched the world to “Grok 3”, probably the most succesful and highly effective mannequin by x.AI so far. The corporate that began in 2023 and acquired its final mannequin (Grok 2) out in 2024, is now difficult fashions by prime firms like OpenAI, Google, and Meta which were 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 stage capabilities with only a fraction of GPUs that Grok 3 did! On this weblog, we are going to 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 important 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 creating cutting-edge AI options.
Additionally Learn: Intel’s Gaudi 3: Setting New Requirements with 40% Quicker AI Acceleration than Nvidia H100
Why Do AI Firms Want It?
There are a number of the explanation why main tech and AI firms all over the world are investing within the NVIDIA H100 Chips:
- 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.
- 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 fast information motion and seamless multi-GPU operations.
- Optimized for AI: That includes FP8 & TF32 precision with its Transformer Engine, it accelerates deep studying duties whereas sustaining effectivity and accuracy.
- Cloud & HPC Functions: Broadly utilized by cloud suppliers similar to AWS, Google Cloud, and Microsoft Azure, the H100 helps large-scale AI workloads and enterprise purposes.
- Price & Vitality Effectivity: Constructed for prime efficiency per watt, it reduces operational prices whereas maximizing computational energy, making it a sustainable selection for AI infrastructure.
What Can 100K H100 GPUs Do?
100,000 H100 GPUs can break down huge issues (like coaching refined AI fashions or working complicated simulations) into many small duties, and work on them suddenly. 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 job 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 could be:
Time = 864,000 seconds ÷ 100,000 = 8.64 seconds
So a job that will have taken 10 days on one GPU may, 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. Nonetheless, 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 actually beat all the prevailing rivals within the subject. That’s the reason x.AI went large! 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 yr, they’ve been capable of 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.
| Benchmark | Grok 2 mini (Excessive) | Grok 3 (mini) |
| Math (AIME2 ’24) | 72 | 80 |
| Science (GPOA) | 68 | 78 |
| Coding (LCB Oct–Feb) | 72 | 80 |
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 may really feel the warmth resulting from 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? Had been 100K H100 GPUs actually value it?
Additionally Learn: Grok 3 vs DeepSeek R1: Which is Higher?
Worth Test: Grok 3 vs Different Main Fashions
We are going to take a look at Grok 3 towards 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 examine Grok 3 with a distinct mannequin in every take a look at, primarily based on the outputs I obtain from the 2 fashions. I shall be evaluating the fashions on three completely different duties:
- Deep Search
- Superior Reasoning
- Picture Evaluation
I’ll then choose the one which I discover higher primarily based 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:
By Gemini 1.5 Professional with Deep Search:
Assessment:
| Standards | Grok 3 (Deep Analysis) | Gemini 1.5 Professional with Deep Search | Which is Higher? |
| Protection of LLMs | Focuses on 5 fashions (Grok 3, GPT-4o, Claude 3.5, DeepSeek-R1, and Gemini 2.0 Professional). | Covers a wider vary of fashions, together with Grok 3, GPT-4o, Gemini Flash 2.0, Mistral, Mixtral, Llama 3, Command R+, and others. | Gemini |
| Benchmark Selection | Math (AIME, MATH-500), Science (GPQA), Coding (HumanEval), and Chatbot Area ELO rating. | Contains all main benchmarks + multilingual, instrument use and basic reasoning, | Gemini |
| Depth of Efficiency Evaluation | Detailed benchmark-specific scores however lacks effectivity and deployment insights. | Supplies broader efficiency evaluation, overlaying each uncooked scores and real-world usability. | Gemini |
| Effectivity Metrics (Context, Price, Latency, and so on.) | Not coated. | Contains API pricing, context window measurement, and inference latency. | Gemini |
| Actual-World Functions | Focuses solely on benchmark numbers. | Covers sensible use circumstances like AI assistants, enterprise productiveness, and enterprise instruments. | Gemini |
Clearly, on every criterion, the report generated by Gemini 1.5 Professional Deep Search was higher, extra inclusive,, and extra complete of all the small print 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:
Assessment:
| Standards | Grok 3 (Suppose) | o1 | Which is Higher? |
| Black Gap Results | Simplified clarification, specializing in occasion horizon and spaghettification. | Detailed clarification of tidal forces, orbital disruption, and radiation. | o1 |
| Wormhole Results | Briefly mentions stability and journey potential. | Discusses stability, gravitational affect, and theoretical properties. | o1 |
| Gravitational Influence on Earth | Mentions gravitational pull however lacks in-depth evaluation. | Explains how the black gap dominates with stronger tidal forces. | o1 |
| Interaction Between Each | Speculates a couple of attainable hyperlink between the black gap and wormhole. | Describes gravitational tug-of-war and attainable wormhole collapse. | o1 |
| Potential for Earth’s Survival | Suggests the wormhole could possibly be an escape route however is extremely speculative. | Clearly states that survival is extremely unlikely resulting from black gap’s forces. | o1 |
| Scientific Depth | Extra basic and sensible, much less detailed on physics. | Supplies a structured, theoretical dialogue on spacetime results. | o1 |
| Conclusion | Black gap dominates, and wormhole provides minor chaos. | Earth is destroyed by black gap forces. Wormhole’s function is unsure. | o1 |
The consequence generated by o1 is healthier 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 chance of every crew primarily based on the picture?”
Outcomes:
Response by Grok 3:
Response by DeepSeek-R1:
Assessment:
| Standards | Grok 3 | DeepSeek-R1 | Which is Higher? |
| Win Likelihood (Afghanistan) | 55-60% | 70% | DeepSeek-R1 |
| Win Likelihood (Pakistan) | 40-45% | 30% | Grok 3 |
| Key Elements Thought-about | Contains historic developments, required run charge, crew strengths, and pitch circumstances. | Focuses on the final-over state of affairs (9 runs wanted, 2 wickets left). | Grok 3 |
| Assumptions Made | Considers Pakistan’s capability to chase 316 and Afghanistan’s bowling assault. | Assumes Afghanistan will efficiently chase the goal. | Grok 3 |
| Total Conclusion | Afghanistan has a slight edge, however Pakistan has an inexpensive likelihood relying on their chase. | Afghanistan is in a robust place, and Pakistan wants fast wickets. | Grok 3 |
Though the consequence given by DeepSeek-R1 was extra correct, Grok 3 gave a superb evaluation of the match primarily based on the picture.
Ultimate Outcome: Grok 3 misplaced in 2 out of three duties when pitied towards its rivals.
100K H100 GPUs: Was It Price It?
Now that we’ve seen how Grok 3 performs towards rivals in numerous duties, the actual 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, similar to DeepSeek-R1 and OpenAI’s o1, achieved comparable or superior outcomes whereas using considerably fewer computational assets.
Vitality Utilization
Past the monetary funding, powering and cooling an information middle with 100K+ H100 GPUs comes with a large vitality burden. Every H100 GPU consumes as much as 700W of energy underneath 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!
- Consider cooling necessities and the whole vitality consumption will increase considerably.
Grok 3’s energy-intensive method is probably not probably the most sustainable. OpenAI & Google are actually focussing on smaller, extra environment friendly architectures and energy-optimized coaching strategies, 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 in addition 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 strategies 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 assets. DeepSeek-R1 difficult business leaders whereas being educated on simply 2,048 H800 GPUs, is a primary 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 working a 200K-GPU infrastructure long-term?
- May x.AI have achieved comparable outcomes with higher information curation, coaching optimizations, or parameter effectivity relatively than brute-forcing with GPUs?
- Would investing in additional environment friendly architectures have yielded higher outcomes?
- How sustainable is that this method in the long term, given the growing prices and competitors within the AI house?
Conclusion
Grok 3 marks a major leap for x.AI, demonstrating notable enhancements over its predecessor. Nonetheless, regardless of its 100K+ H100 GPU infrastructure, it didn’t constantly outperform rivals like DeepSeek-R1, o1, and Gemini 1.5 Professional, which achieved comparable outcomes with far fewer assets.
Past efficiency, the vitality and monetary prices of such huge GPU utilization increase issues about long-term sustainability. Whereas x.AI prioritized uncooked energy, rivals are reaching effectivity via optimized architectures and smarter coaching methods.
So, had been the 100K GPUs value it? We don’t assume so, at this level. If Grok 3 can’t constantly dominate, x.AI could have to rethink whether or not brute-force computation is the perfect path ahead within the AI race.
Steadily Requested Questions
A. Grok 3 is x.AI’s newest LLM able to performing duties like superior reasoning, enhanced reasoning and coding.
A. x.AI used 100K+ NVIDIA H100 GPUs to speed up Grok 3’s coaching and enhance its reasoning, analysis, and problem-solving skills.
A. The estimated price of coaching and working 100K GPUs contains hundreds of thousands of {dollars} in {hardware}, vitality consumption, and upkeep prices.
A. DeepSeek-R1 was educated on simply 2,048 GPUs however achieved aggressive outcomes. This reveals that environment friendly AI coaching strategies can rival brute-force computation.
A. Whereas extra GPUs pace up coaching, AI firms like OpenAI and Google use optimized architectures, mixture-of-experts (MoE), and retrieval-augmented era (RAG) to realize comparable outcomes with fewer GPUs.
A. Regardless of utilizing huge computational assets, Grok 3 didn’t constantly outperform rivals. Furthermore, it struggled in duties like superior reasoning and deep search evaluation.
A. Whereas Grok 3 is a strong AI mannequin, the excessive price, vitality consumption, and efficiency inconsistencies counsel {that a} extra environment friendly method could have been a greater technique.
Anu Madan has 5+ years of expertise in content material creation and administration. Having labored as a content material creator, reviewer, and supervisor, she has created a number of programs and blogs. At present, she engaged on creating and strategizing the content material curation and design round Generative AI and different upcoming expertise.