Srujan

Excited about all sorts of engineering

[Corey Quinn @QuinnyPig]

Been thinking about what an "agent-native cloud" actually needs to look like. Mentioned this, and @Vercel's CEO replied that it'll be them. Cool! Here's the spec they (or @Cloudflare, or some startup not yet invented) actually have to hit.

It won't be @awscloud.

Thread...

[Leonie @helloiamleonie]

Spent the weekend crossing one thing off my "to learn" list:

GRPO

In this blog, we walk through:
• What is GRPO and how does it work
• Fine-tune @liquidai's LFM2.5-1.2B-Instruct
• using @UnslothAI and some free @kaggle T4s

Blog: https://t.co/vv3VK4GF1j

Kaggle Notebook: https://t.co/hXOV9z4mK3

[fin @fi56622380]

AI Semiconductor Endgame 2026 (Part 1)

New Token Economics Computing Paradigm Shifts from GPU Compute to HBM

This article starts from the essence of GPU architectural evolution to address a question the market has long worried about:

Why must each GPU's HBM memory demand grow exponentially, and why won't this exponential growth in HBM demand stall?

It then derives the first principle of token economics under the current architecture: token throughput = HBM size × HBM BW (bandwidth)

It also discusses why the GPU ceiling is determined by HBM's two dimensions of progress.

The topic of HBM cyclicality has long been controversial. Optimists argue that AI-driven demand is much greater than before, but the market mainstream still believes that previous up-cycles also saw 20%+ annual demand growth — so what's different this time? AI doesn't change the fact that HBM, like traditional DRAM, has commodity attributes. Once capacity expansion at the demand peak meets a downturn, history will repeat itself. We can take the perspective of compute-chip architecture, start from first principles, and unpack and reason through this question:

why this time is genuinely different.

———————————————————————————————

History: The Era of CPU Compute

For a very long time, we lived in the era of CPU-dominated compute. The CPU's top-level KPI was performance — running faster — and so each generation of CPUs deployed every method imaginable to push benchmark scores higher. First it was rising clock frequencies, then it was architectural evolution: superscalar designs, and so on.

During this period, why didn't DDR need to advance technologically at high speed? DDR3 to DDR5 took a full 15 years.

Because in this era, DDR's role was purely auxiliary — and only weakly so. By industry experience, even doubling DDR speed would generally only raise CPU performance by less than 20%.

Why did improvements in DDR bandwidth and speed matter so little? Two reasons:

1. CPUs designed all kinds of architectural tricks to hide DDR latency — superscalar designs, wider issue widths, massive ROBs and register renaming to extract parallelism and hide latency, L1 caches, L2 caches — all of which weakened the demand for DDR bandwidth and speed.

2. CPU workloads don't have particularly demanding bandwidth requirements. For most everyday workloads — say, opening a webpage — DDR bandwidth is severely overprovisioned. Even cloud workloads often look the same.

In other words, in the CPU era, DDR bandwidth and speed didn't really matter. There was virtually no difference between DDR4 and DDR5 except in a handful of games — and even the JEDEC standard advanced slowly.

On top of that, only a small portion of any given app needs to permanently sit in DDR. Whatever is needed can be paged in from the hard drive on demand. App size grew slowly, and so DDR capacity demand grew slowly as well.

That's why, over the past decade, the average PC went from 7–8GB of DDR to about 23GB — only 3× growth in ten years.

This slow upgrade pace directly affected revenue. Capacity-based pricing was the main way of making money; speed improvements were just a technological upgrade that raised the unit price of capacity. With both of these dimensions advancing slowly, growth could only come from increases in PC/phone unit volumes.

So along both dimensions — bandwidth/speed and capacity — DRAM was always a “nice-to-have” appendage to the chip industry. The marginal utility of DDR upgrades was very low, and almost completely disconnected from the CPU era's top-level KPI.

———————————————————————————————

The Paradigm Shift: GenAI's Top-Level KPI

When we entered the era of GenAI large models, the computing paradigm shifted, and the top-level KPI changed fundamentally.

By the time GPUs evolved into AI inference engines, the top-level KPI was no longer compute alone (TOPS/FLOPS), as it had been for CPUs — it became the cost of a token. Specifically: overall token throughput per unit cost / per unit power.

A close second is token throughput speed — because in the agent era, many tasks have become serial, and token output speed has become a critical bottleneck for user experience.

This is exactly why Jensen invented the concept of the AI factory: to produce the most tokens at the lowest cost, while pushing token throughput speed as high as possible.

In the AI training era, Jensen's economics were TCO (Total Cost of Ownership): the more GPUs you buy, the more you save.

In the inference era, Jensen's token economics flip the logic:

AI inference has very healthy gross margins, so the logic now becomes: the NVIDIA GPU is the GPU that produces the cheapest token in the world, so the more you buy, the more you earn.

The top-level KPI has become a Pareto frontier: along the two dimensions of token throughput and token speed, optimize as far as possible.

Each generation of NVIDIA's token factory is essentially pushing the entire Pareto frontier up and to the right. This is the most important KPI of the AI inference era.

———————————————————————————————

From Token Throughput to HBM: The Core Logic Chain

Below is the most important logical chain of this article: how to start from the exponential growth of token throughput and derive that the ceiling bottleneck lies in the exponential growth of HBM size and HBM speed.

In the era of single-GPU inference with single-thread batch size = 1, token throughput had only one dimension: HBM bandwidth speed. Higher bandwidth = higher token throughput.

But once we entered the NVL72 era, inference is no longer single-GPU. It is a system-level token factory composed of 72 GPUs + 36 CPUs, designed to fully saturate HBM bandwidth and compute simultaneously, in pursuit of the ultimate token throughput.

Token throughput growth depends on two things: the number of requests batched simultaneously × the average token speed per request.

That is: batch size × token speed.

Take Rubin NVL72 as an example. At an average token speed of 100 tokens/s, processing 1,920 simultaneous requests yields a token throughput of 192,000 tokens/s. A Rubin NVL72 draws roughly 120kW (0.12MW), so per MW it can handle 1.6M tokens/s.

So we need to find ways to push both parameters up: batch size and average token speed. Their product is our top-level KPI — token throughput.

Parameter 1: Batch growth — bottleneck is HBM size

Every request in the batch carries its own KV cache, which has to live in HBM, with sizes ranging from a few GB to tens of GB. Because hot KV cache must be read at high frequency and high speed at any moment, it must reside in HBM. For a model with, say, 80 layers, every token generation step requires reading the KV cache 80 times from HBM.

As batch size grows, hot KV cache grows linearly.

And because the hot KV cache for every request in the batch must sit in HBM, HBM size must grow linearly with batch size.

Like an airport shuttle bus: the gate wants to move passengers to the plane as fast as possible. If HBM size is small, the shuttle is small, so you have to make extra trips.

Conclusion: batch size growth bottlenecks on HBM size growth.

Parameter 2: Average token speed per request — bottleneck is HBM bandwidth

The decode-phase speed of a large model bottlenecks on HBM bandwidth, because every token generated requires reading the activated weights and KV cache many times over.

The emergence of LPUs has, in cases where batch size isn't very large, moved the activated weights portion onto SRAM — but every generated token still requires many reads of the KV cache from HBM. The higher the HBM bandwidth, the faster each token is generated, in essentially linear correspondence.

Like the airport shuttle bus: HBM bandwidth is like the width of the door — wider doors mean passengers board faster.

The rest of the GPU's configuration is essentially adapted to support batch growth and to keep token compute speed in step with HBM growth. In some cases the GPU even spends excess compute to recover effective bandwidth (e.g., bandwidth compression techniques).

—-------

To return to the shuttle bus analogy:

• Shuttle bus cabin size = HBM Size (capacity): determines how many passengers can fit at once (i.e., how many requests' KV caches can sit in HBM simultaneously). Bigger cabin = more passengers (higher batch size) per trip. If the bus is too small, moving 100 people takes two trips — and total throughput suffers.

• Shuttle bus door width = HBM Bandwidth: determines how fast passengers get on and off. A wide door, and everyone piles on at once (decode/token generation is fast). A narrow door, and even with a giant cabin, people queue up and most of the time is spent boarding.

• Passenger throughput = cabin size × door-width-determined boarding speed.

—-------

At this point, we've logically derived the first principle of token-economics hardware demand:

Token throughput = HBM size × HBM Bandwidth

The top-level KPI of the AI inference era is highly dependent on progress along both HBM dimensions.

If we want to maintain 2× token throughput growth per generation, that means each generation of single GPU must grow HBM size × HBM BW speed by 2×!

This is the first time in history that HBM memory size can influence the top-level KPI — token throughput.

To validate this thesis, we can put NVIDIA's token throughput from A100 to Rubin Ultra on the same chart as HBM size × HBM BW speed.

What you find is that the two curves track each other startlingly closely on log axes.

HBM size × speed actually grows even faster than token throughput — which makes sense, because HBM defines the ceiling, and in practice utilization of that ceiling is very hard to push to 100%. Even if HBM size × HBM speed grew by 1,000×, with the supporting compute and architecture, it would be very hard to wring out the full 1,000× of headroom.

This curve isn't a coincidence — it's the necessary solution of system optimization.

throughput = batch × speed. This is the unavoidable first principle of token factory economics.

—-------

What about software? Won't software optimization reduce bandwidth demand? Reduce HBM demand?

This is an independent dimension from hardware. It's like asking: if software on a CPU runs faster after optimization, does that mean the CPU doesn't need to advance for ten years? After all, software is faster now.

If that were the case, would CPU vendors still make money? For a CPU vendor to survive, there's only one path: in standardized benchmarks, ignoring software optimization, every new CPU generation must score higher — otherwise it doesn't sell.

GPUs are exactly the same. How well software is optimized, and the requirement that the GPU's own token-throughput KPI must improve dramatically every year, are two separate things.

As long as token demand keeps growing, the pursuit of higher token throughput will not stop — and so neither will the pursuit of higher HBM size × HBM speed.

If HBM size and HBM speed were to slow down, Jensen would personally fly to the Big Three and pressure them to accelerate, because that ishis GPU ceiling. If the ceiling stops rising, can his GPU still sell?

Of course, NVIDIA also needs to wrack its brains to extract performance beyond the HBM ceiling through heterogeneous architectural angles. The LPU is a great example — it improved the Pareto frontier substantially from a different angle (the right-hand high-token-speed portion).

—--------------------

HBM memory has now bid farewell to that old era of drifting with the tide. On this one-way road paved by exponential demand, it has, in something close to a destined fashion, walked onto the central stage of the industry's epic.

When the inference paradigm's first principles evolve to this point, as long as Jensen still wants to sell GPUs, HBM must double — and it must double every generation. This is endogenous pressure from the supply side. It has nothing to do with AI demand, nothing to do with macro cycles, and nothing to do with the moods of the hyperscalers.

The only remaining question is this:

When demand has been physically locked into exponential growth, will the three players on the supply side — like they have for the past thirty years — once again drag themselves back into the mire of the cycle by their own hands?

[Steve Hou @stevehou]

One of the best posts on AI compute I’ve read in a long time. It explains why semis stocks have become 40% of the market index weight from a technical perspective and lays out a roadmap for the tech evolution ahead.

One incredible (and unrelated) fact I learned a couple years ago that stuck with me was that some 90% (could be lower now) of the energy consumed by AI isn’t used on compute at all but by shuttling the model weights back and forth btwn the GPU and memory. This post explains using the analogy of airport shuttles btwn gates and airplanes and the strong complementarity btwn GPU throughput and HBM capacity and bandwidth.

The AI models have become powerful and genuinely useful for most everyday use incl enterprise production that requires high accuracy. We are now witnessing an explosion of “inference” or the actual usage or deployment of the models via AI agents or AI calling AI. The artificial intelligence is finally good enough in most contexts that we are going to see an explosive growth of the consumption of “intelligence”.

As long as we remain in this current architecture of LLM transformers running inference on discrete GPUs + off chip HBMs, AI compute will remain structurally “memory bound”. In fact, barring big architectural changes we can anticipate demand to persistently outstrip supply turning a historically strongly cyclical industry into one that’s well not cyclical anymore, an assertion that has drawn ridicule.

Now, “nature always finds a way”. And shortage is always the mother of innovations. We are now seeing a wide range of attempts to overcome the structural memory bound. Attempts are being made on both the hardware and the software fronts notably on-chip static RAM or SRAM such as the integration of Groq by NVidia, Amazon + Cerebras, and Google’s TPU (and TurboQuant).

Beyond that efforts are being made in adoption of optical interconnects to further disaggregate memory allowing for more efficient compute as well as potentially doing photonic compute directly in memory. These are ofc developments and possibilities that further out. For now the dynamic of heavy memory bound compute that @fi56622380 lays out still strictly dominates.

All of the above relates to the supply side ofc and the implicit key assumptions. There’s also importantly the demand side. There’s a presumption among AI hardware enthusiasts that we are in a paradigm of persistent “supply shortage”. That may well be the case as demand and adoption for machine intelligence grow exponentially while supply is constrained by supply chain bottlenecks.

However, it’s worth considering the ways in which demand may indeed fluctuate. After all oil demand grew secularly but oil prices also fluctuated significantly, notwithstanding the big differences in oil discovery/refining vs GPU/CPU/memory supply. Demand for AI could indeed slow or grow slower than expected if enterprise adoption ran into frictions or if the ROI proved initially elusive esp if the prices of AI inference keep growing to reflect their true economic costs let alone their economic value created. Demand curve slopes downwards. That logic hasn’t been tested yet. That’ll have to be for another Ackman-esque long post on a different sleepless morning. I hate pollens.

[Srujan @srujan_here]

@calcsam Book

[Srujan @srujan_here]

@bigdatasumit SQL superstar

[Srujan @srujan_here]

@mikefutia REELS

[Srujan @srujan_here]

@eyad_khrais @perplexity_ai ADS

[Eyad @eyad_khrais]

Use AI to find winning ads for your product

I built a Comet prompt for @perplexity_ai that harvests the top Meta + TikTok ads, ranks them and then returns the top 10 performers

What you’ll get:
- Swipefile with links to winning ads
- Public Score ranking (Days active, variant count, view/CTR signals)
- Frame-by-frame scripts, captions, CTAs

Fill out the template and hit run

Follow + Like + RT + comment “ADS” and I’ll DM you the template. MUST BE FOLLOWING so I can DM you.

[Srujan @srujan_here]

@eyad_khrais @perplexity_ai Prompt

[Eyad @eyad_khrais]

You don’t to spend $1000s on market research. You just need the right prompt.

I built a competitor research template that runs on @perplexity_ai comet browser that finds the top 10 rivals for your product, pulls pricing/features/proof, and auto-builds a positioning brief.

What you get:
- Side by side feature comparison
- Pricing landscape real sources
- Momentum read (last 12 months)
- 3 gaps you could exploit
- Looks like a $5K report-without the report.

What you need to do:
- Download comet browser
- Fill in the template & run the prompt

Like + RT + comment “PROMPT” and I’ll DM you the exact prompt template

[Srujan @srujan_here]

@DsMatie BLUEPRINT

[Srujan @srujan_here]

@JulianGoldieSEO HOW

[Srujan @srujan_here]

@DeangeloMa28303 Steal

[Srujan @srujan_here]

@anymanfitness FAST

[Srujan @srujan_here]

@DsMatie 👋

[niki parmar @nikiparmar09]

Today is as good a day as any to share that I joined Anthropic last Dec :)
Claude 3.7 is a remarkable model at complex tasks, especially coding, and I'm thrilled to have contributed to its development. From winning Pokémon badges to vibes coding, Claude's got you covered!

[Srujan @srujan_here]

@tunguz @RolloutAI

[Srujan @srujan_here]

@Sabrina_Ramonov Prompts

[Sabrina Ramonov 🍄 @Sabrina_Ramonov]

I built a Free Prompts Library that contains 100s of AI prompts for content creation, planning, research, marketing, engineering, and more.

When I first started making free AI educational content, I noticed people were paying for ChatGPT prompt packs and frequently disappointed.