With 3.3M+ folks watching the launch, Elon Musk and his staff launched the world to “Grok 3”, essentially the most succesful and highly effective mannequin by x.AI to this point. The corporate that began in 2023 and received 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 due to 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’ll discover if Grok 3 is price 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 sooner processing, higher effectivity, and improved scalability, making it a essential software 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 world wide 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-Velocity 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.
- Optimized for AI: That includes FP8 & TF32 precision with its Transformer Engine, it accelerates deep studying duties whereas sustaining effectivity and accuracy.
- Cloud & HPC Purposes: Extensively utilized by cloud suppliers resembling AWS, Google Cloud, and Microsoft Azure, the H100 helps large-scale AI workloads and enterprise purposes.
- Price & Power 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 subtle AI fashions or working advanced simulations) into many small duties, and work on them abruptly. This extraordinary parallel processing energy means duties that may usually take a really very long time will 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 could be:
Time = 864,000 seconds ÷ 100,000 = 8.64 seconds
So a job that may have taken 10 days on one GPU might, in principle, 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 actually beat all the present rivals within the discipline. That’s the reason x.AI went large! They created a knowledge heart 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 pondering 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 might really feel the warmth as a result of their falling inventory costs and reducing person base as folks flocked in direction of the open supply marvel that challenged OpenAI’s better of the most effective! However to do that, did DeepSeek-R1 use 100K GPUs?
Effectively, not even a fraction of it! DeepSeek-R1 has been fine-tuned on prime of the DeepSeek-V3 base mannequin. DeepSeek-V3 has been skilled 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 signifies that DeepSeek-R1 has been skilled 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 price it?
Additionally Learn: Grok 3 vs DeepSeek R1: Which is Higher?
Worth Examine: Grok 3 vs Different Main Fashions
We’ll take a look at Grok 3 towards the highest fashions together with o1, DeepSeek-R1, and Gemini fashions for varied duties to see the way it performs. To do that I’ll evaluate Grok 3 with a distinct mannequin in every take a look at, primarily based on the outputs I obtain from the 2 fashions. I will likely 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.
Check 1: Deep Search
Fashions: Grok 3 and Gemini 1.5 Professional with Deep Analysis
Immediate: “Give me an in depth report on the most recent LLMs evaluating them on all of the out there benchmarks.”
Outcomes:
By Grok 3:
By Gemini 1.5 Professional with Deep Search:
Overview:
| 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 Enviornment ELO rating. | Contains all main benchmarks + multilingual, software use and common reasoning, | Gemini |
| Depth of Efficiency Evaluation | Detailed benchmark-specific scores however lacks effectivity and deployment insights. | Offers broader efficiency evaluation, masking each uncooked scores and real-world usability. | Gemini |
| Effectivity Metrics (Context, Price, Latency, and many others.) | Not coated. | Contains API pricing, context window measurement, and inference latency. | Gemini |
| Actual-World Purposes | Focuses solely on benchmark numbers. | Covers sensible use instances 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 main points round LLM benchmarks.
Check 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:
Overview:
| Standards | Grok 3 (Assume) | 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 few potential hyperlink between the black gap and wormhole. | Describes gravitational tug-of-war and potential wormhole collapse. | o1 |
| Potential for Earth’s Survival | Suggests the wormhole might be an escape route however is extremely speculative. | Clearly states that survival is extremely unlikely as a result of black gap’s forces. | o1 |
| Scientific Depth | Extra common and sensible, much less detailed on physics. | Offers 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 end result generated by o1 is healthier as it’s extra detailed, scientific, and well-structured in comparison with the end result given by Grok 3.
Additionally Learn: Grok 3 vs o3-mini: Which Mannequin is Higher?
Check 3: Picture Evaluation
Fashions: Grok 3 and DeepSeek-R1
Immediate: “What’s the win likelihood of every staff primarily based on the picture?”
Outcomes:
Response by Grok 3:
Response by DeepSeek-R1:
Overview:
| 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 of | Contains historic tendencies, required run charge, staff 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 probability relying on their chase. | Afghanistan is in a powerful place, and Pakistan wants fast wickets. | Grok 3 |
Though the end result given by DeepSeek-R1 was extra correct, Grok 3 gave an excellent evaluation of the match primarily based on the picture.
Closing Consequence: 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 varied duties, the true query stays: Was the large funding in over 100K H100 GPUs justified?
Whereas Grok 3 has demonstrated vital 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 related or superior outcomes whereas using considerably fewer computational sources.
Power Utilization
Past the monetary funding, powering and cooling a knowledge heart with 100K+ H100 GPUs comes with a large power burden. Every H100 GPU consumes as much as 700W of energy below full load. Meaning:
- 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 overall power consumption will increase considerably.
Grok 3’s energy-intensive method might not be essentially 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 Issues
Coaching AI fashions at scale is an costly endeavor—not simply when it comes to {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 strategies to maximise effectivity whereas minimizing compute prices.
In the meantime, open-source communities are demonstrating that high-quality AI fashions will be constructed with considerably decrease sources. DeepSeek-R1 difficult business leaders whereas being skilled 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?
- Might x.AI have achieved related outcomes with higher knowledge curation, coaching optimizations, or parameter effectivity slightly 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 rising prices and competitors within the AI area?
Conclusion
Grok 3 marks a big leap for x.AI, demonstrating notable enhancements over its predecessor. Nevertheless, 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 sources.
Past efficiency, the power and monetary prices of such huge GPU utilization increase issues about long-term sustainability. Whereas x.AI prioritized uncooked energy, rivals are attaining effectivity via optimized architectures and smarter coaching methods.
So, had been the 100K GPUs price it? We don’t suppose 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 most effective path ahead within the AI race.
Incessantly 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 talents.
A. The estimated value of coaching and working 100K GPUs contains tens of millions of {dollars} in {hardware}, power consumption, and upkeep prices.
A. DeepSeek-R1 was skilled on simply 2,048 GPUs however achieved aggressive outcomes. This exhibits that environment friendly AI coaching strategies can rival brute-force computation.
A. Whereas extra GPUs velocity up coaching, AI firms like OpenAI and Google use optimized architectures, mixture-of-experts (MoE), and retrieval-augmented era (RAG) to realize related outcomes with fewer GPUs.
A. Regardless of utilizing huge computational sources, 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 robust AI mannequin, the excessive value, power 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. Presently, she engaged on creating and strategizing the content material curation and design round Generative AI and different upcoming expertise.