Pranay Saha

breathe!!!

[Pranay Saha @pranay_0912]

It's not the model.

It's the infrastructure around it.

That's what my biggest takeaway was while designing a Multi-Camera Surveillance System for retail.

https://t.co/L9u6gTWpOp

[Pranay Saha @pranay_0912]

Your LLM fitting in GPU memory doesn't mean it's fast.
The bottleneck often isn't compute—it's memory bandwidth.
Why does generating longer responses require more GPUs, even with the same model?
I break it down with simple analogies and diagrams.
https://t.co/fALgWmxxk4

[Pranay Saha @pranay_0912]

🤔 Your LLM isn't slow because it's generating...
It's slow because it's still reading.
Before a model writes its first token,
it has to understand every token you send.
That's the Prefill stage.
https://t.co/r78VKdmbtI
#LLM #AI #GenAI #GPU #Inference #Transformers

[Pranay Saha @pranay_0912]

Day 1:
"My model fits on one GPU."
Day 30:
"Who ordered eight H200S?"
I wrote a practical deep dive on GPU capacity planning for LLMs, covering Fit, KV Cache, and why GPU requirements explode at scale.
👇 Blog below

https://t.co/mihrqQHek0

[Avi Chawla @_avichawla]

A tricky LLM interview question:

You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces.

So you add KV cache compression and evict 90% of the cached tokens.

VRAM usage stays as is and GPU still runs out of memory.

Why?

(answer below)

Evicting 90% of the KV cache can free almost none of the memory it was using.

This sounds counterintuitive, but it follows directly from how production servers store the cache today.

The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues.

This is the dominant memory cost for reasoning models.

If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU.

One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it.

But this does not solve the memory problem yet.

The reason is paged attention, which is the memory manager behind vLLM and most production servers.

Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens.

This block returns to the allocator only when every slot inside it is empty.

Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks...

...so despite eviction, almost every block is left with at least some survivor tokens.

For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token.

This means the allocator frees almost nothing.

Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout.

Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order.

Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds.

This introduces another bookkeeping cost that an in-order layout inherently avoids.

So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server.

There's another problem.

Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected).

But fast attention kernels used in production, like FlashAttention, never save those scores.

They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast.

So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide.

NVIDIA published a method called TriAttention to solve both these problems.

It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters.

For the memory problem, it runs a compaction pass every 128 decoded tokens.

The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order.

On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory.

KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens.

You can find the NVIDIA write-up here: https://t.co/ZwXv7VezVu

I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching.

Read it below.

[Pranay Saha @pranay_0912]

Same model
Same GPU
4× more throughput.
Here’s what changed 👇

https://t.co/cwrK1GwThQ

#AIEngineering
#MLOps
#ComputerVision
#DeepLearning
#TensorRT

[Pranay Saha @pranay_0912]

Your model works.
But can you afford to serve it at scale?
A short case study on:
→ Triton Inference Server

https://t.co/OSVnajoQcH

[Pranay Saha @pranay_0912]

@IndiGo6E @IndiGo6E, please share an estimated timeline for when this will be checked? That would be really helpful.

[Pranay Saha @pranay_0912]

@LemonTreeHotels I had a booking on 5 Dec at Lemon Tree Kalina BKC Mumbai, but my flight got cancelled. I raised a cancellation via @makemytrip & called the hotel multiple times — no help at all. Really disappointing. Is this the customer service expected from your chain?

[Liang Chen @liangchen5518]

Excited to introduce R1-V!

We use RL with verifiable rewards to incentivize VLMs to learn general counting abilities.

2B model surpasses the 72B with only 100 training steps, costing less than $3.

The project will be fully open source.

Stay tuned!

https://t.co/2lmRGbEmMH

[ollama @ollama]

DeepSeek's first-generation reasoning models are achieving performance comparable to OpenAI's o1 across math, code, and reasoning tasks!

Give it a try! 👇

7B distilled:
ollama run deepseek-r1:7b

More distilled sizes are available. 🧵

[Yann LeCun @ylecun]

To people who think
"China is surpassing the US in AI"
the correct thought is
"Open source models are surpassing closed ones"
See ⬇️⬇️⬇️

[ritwik @ritwik_raha]

Prediction 1: We will have agents to build agents by the end of this year.

[Pranay Saha @pranay_0912]

Meri ek taang nakli ha !! Meh cricket ka bohot bara champion tha 🤣🤣…….

[Trendulkar @Trendulkar]

Congratulations, Afghanistan 🇦🇫

Also, Gulbadin Naib is running the fastest 😂

[Karthik Balachandran @karthik2k2]

🥹

[Pranay Saha @pranay_0912]

🥶🥶🥶
#indvspak

[Samuel Thompson 🥽 @samuelthompson]

😂