FP32 Monastic

Shipping LLM systems: RAG, agents, serving, eval. Attention seeker (self-attention only). No vibes, just logits.

[FP32 Monastic @kv_cached]

@atsohom Thank you so much

[FP32 Monastic @kv_cached]

you are
a developer trying to run GPT-3 locally
your GPU catches fire
your AWS bill looks like a phone number
decide to learn the dark arts of optimization
18 months later i can make a 70B model run on a gaming laptop
here's the forbidden speedrun guide

the problem:
LLMs are fat and slow
175B parameters = 700GB in FP32
one forward pass = your GPU crying
inference speed measured in geological time
but the nerds fixed it

quantization = the ultimate life hack
take your fancy 32-bit floats
crush them to 8-bit ints
model gets 75% smaller
still works fine
it's like JPEG for neural networks
"lossy compression but nobody notices"

4-bit quantization:
yes, FOUR bits
0-15, that's all you get
somehow still generates shakespeare
GGUF, AWQ, GPTQ formats
pick your poison
they all work, nobody knows why

KV-cache quantization:
the cache that stores your conversation
normally bloats like your node_modules
quantize it too
suddenly you can chat for hours
memory usage: "what memory usage?"

flash attention = GPU go brrrr
normal attention: O(n²) memory
flash attention: "what if we just... didn't?"
compute in tiny blocks
use SRAM like a speed demon
4x faster, uses 10x less memory
dao-aielab/flash-attention changed the game

sparse attention patterns:
not every token needs to see every token
local: "i only talk to my neighbors"
strided: "i check in every 8 tokens"
learned: "the model decides who's worth talking to"
attention budget: managed

paged attention:
virtual memory but for transformers
breaks KV-cache into pages
no more memory fragmentation
vLLM uses this
suddenly you can serve 100 users on one GPU
"MMU for AI" energy

speculative decoding = parallel universe speedrun:
small model: "i think it says 'the cat sat'"
big model: "let me verify... yep, 2/3 correct"
accept the good, regenerate the bad
2-3x speedup
free performance, no quality loss

LoRA = fine-tuning for broke people:
don't retrain 70B params
add tiny 1M param adapters
they modify the big model's behavior
switch them like cartridges
one base model, infinite personalities
PEFT library makes it trivial

pruning = marie kondo for weights:
structured: delete entire layers
unstructured: delete random connections
magnitude pruning: "if weight < 0.01, yeet"
90% sparsity still works
your model on a diet

knowledge distillation:
big model teaches small model
teacher: "here's my probability distribution"
student: "i'll mimic that"
10x smaller, 95% as good
how edge devices run "GPT"

dynamic batching:
GPUs hate single requests
batch them like a subway at rush hour
continuous batching: add/remove on the fly
orca/vllm pioneered this
throughput goes 10x

tensor parallelism:
split matrices across GPUs
GPU 1: "i'll do columns 0-1024"
GPU 2: "i'll take 1024-2048"
requires NVLink or you die waiting
how you run 175B models at all

pipeline parallelism:
assembly line for layers
GPU 1 does layers 1-10
GPU 2 does 11-20
microbatching keeps everyone busy
deepspeed makes this "easy"

mixed precision = have your cake:
compute in FP16
accumulate in FP32
store in INT8
tensor cores go brrr
2x speedup for free

early exit strategies:
easy tokens exit early
"the" doesn't need 80 layers
complex tokens get full computation
adaptive compute budgets
30-50% faster on average

model serving stack that actually works:
vLLM for throughput
TensorRT-LLM for latency
llama.cpp for edge
everyone else is marketing
pick one, tune it, ship it

the multiplication effect:
quantization: 4x smaller
flash attention: 4x faster
batching: 10x throughput
sparse attention: 2x faster
speculative decoding: 2x faster
compound them all: 320x better

secret sauce combinations:
INT4 quantization + flash attention + paged attention
= 70B model on consumer GPU
LoRA + pruning + distillation
= custom 7B that beats GPT-3.5
all the techniques stack

forbidden truth:
OpenAI uses all of these
Anthropic too
the "magic" is just good engineering
frontier models = same architecture + better optimization
scale is a compiler flag

what nobody tells you:
inference optimization > training optimization
most "breakthroughs" are engineering
the papers are public
the code is on github
you can build this

tools of the trade:
bitsandbytes for quantization
flash-attention (obviously)
vLLM for serving
PEFT for LoRA
tensorRT for nvidia supremacy
llama.cpp for running on your toaster

final boss wisdom:
you don't need a PhD
you don't need H100s
you need obsession and arxiv
start with llama.cpp
work your way up
in 6 months you'll be explaining speculative decoding at parties

the endgame:
gpt-4 class models on your laptop
real-time inference on phones
open source catching up monthly
optimization democratizing AI
the moat is evaporating

they said LLMs need datacenters
they lied
with these techniques
you run them on edge
build your own
ship to users
no API keys
no rate limits
just pure, optimized inference

now you know
the sacred techniques
the forbidden optimizations
stop asking for API access
start quantizing
welcome to the resistance

[FP32 Monastic @kv_cached]

@karpathy Hi @karpathy I've made the Arabic version of it
https://t.co/z0lt0Z2C2w

[FP32 Monastic @kv_cached]

smallest Arabic model ever

Built an Arabic RAG agent on @karpathy's nanochat

Trained on:

• ArabicText-Large (244M words)

• Hala-4.6M Arabic instructions

• MorphBPE tokenizer (76.8K vocab)

Features:

✓ RTL UI with citations

✓ Offline RAG + search

✓ Consensus decoding

✓ Runs on 4GB GPU

$0 cost. Fully offline.

https://t.co/ROtFsWP5uU

[FP32 Monastic @kv_cached]

@TheAhmadOsman Killer guide. One speed trick worth mentioning: speculative decoding with a draft model can get you 2-3x faster inference on consumer GPUs - llama.cpp and vLLM both support it now. Huge for interactive use cases.

[FP32 Monastic @kv_cached]

smallest Arabic model ever

Built an Arabic RAG agent on @karpathy's nanochat

Trained on:

• ArabicText-Large (244M words)

• Hala-4.6M Arabic instructions

• MorphBPE tokenizer (76.8K vocab)

Features:

✓ RTL UI with citations

✓ Offline RAG + search

✓ Consensus decoding

✓ Runs on 4GB GPU

$0 cost. Fully offline.

https://t.co/ROtFsWP5uU

[FP32 Monastic @kv_cached]

@idare Indeed

[FP32 Monastic @kv_cached]

This research argues that the field should stop chasing ever-bigger models for logic. For reasoning, smaller can do better.

They built a Tiny Recursive Model, a deliberately small network with two layers and about seven million parameters. Instead of trying to solve a problem in one pass, it guesses, checks itself, and refines the answer through recursion. The design is intentionally simple: no heavy mathematical machinery and no biological analogies—just a compact loop that improves its own output step by step.

Why it matters: the tiny model outperforms much larger language models on hard logic tasks. It reaches state-of-the-art test accuracy on puzzle benchmarks, including around 87.4% on Sudoku-Extreme, and it beats large models on ARC-AGI-2 (about 7.8% versus 4.9% for a top LLM). The core reason is error control. Big language models generate text token by token; one early mistake can derail the entire chain of thought. The recursive model revisits and corrects its work, so errors don’t snowball. The study also finds that cutting depth to two layers improves generalization and reduces overfitting for these constrained logic problems. In short: for analytical, well-scoped reasoning, deep recursion with a tiny network can be more reliable and efficient than scaling up parameter counts.

[FP32 Monastic @kv_cached]

Serving/inference knobs: Persistent batching, CUDA Graph capture, tokenizer prefetch/pinning, pinned host memory pools, page-locked staging, KV-cache placement policy, NCCL env tuning (P2P level/IB), speculative decode calibration windows, chunked prefill, multi-stream decode, graph-captured pipelines
GPU software stack: NCCL version pinning, CUDA/cuBLAS/cuDNN matrices, CUDA Graphs, MPS for concurrency, MIG (if datacenter SKUs), nvidia-container-toolkit, DCGM exporter, pinned driver + toolkit images, ROCm matrix if AMD, TensorRT-LLM engine build cache policy
Networking: 10/25/40GbE selection, MTU 9000, LACP, VLAN segregation (mgmt/data), IRQ/RSS queue pinning, iperf3 baselines, ROCEv2 + PFC/DCB only if you own the fabric, Mellanox firmware pinning, static IPMI, out-of-band path redundancy
Observability: Prometheus + Grafana, node_exporter + DCGM exporter, nvidia-smi dmon logging, IPMI sensors, disk SMART, power meters, alerting thresholds (temp, AER, XID, link retrains), log shipping, tracepoints/bpftrace for IRQ stalls

[FP32 Monastic @kv_cached]

The Journey from Human Attention to AI Revolution: How We Taught Machines to Focus
https://t.co/ZCKwoL0Pra

[FP32 Monastic @kv_cached]

Mindblowing paper
https://t.co/IgwA9E4SR7

[Alexia Jolicoeur-Martineau @jm_alexia]

New paper 📜: Tiny Recursion Model (TRM) is a recursive reasoning approach with a tiny 7M parameters neural network that obtains 45% on ARC-AGI-1 and 8% on ARC-AGI-2, beating most LLMs.

Blog: https://t.co/w5ZDsHDDPE

Code: https://t.co/7UgKuD9Yll

Paper: https://t.co/3m8ANhNMiw

[FP32 Monastic @kv_cached]

you’re

done treating attention like stage magic

here to make the mechanism legible

no mystique, no gatekeeping

map: attention variants + inference path, distilled

premises

text → tokens → vectors

attention = soft selection over history

everything else is plumbing

time encodings

absolute index: short-range bias; brittle past train span

RoPE: rotary phases; extendable via NTK/YaRN scaling; check order sensitivity

ALiBi: linear distance bias; cheap; stable for longer spans

head topology

MHA: feature bands in parallel

MQA/GQA: many Q heads, few K/V heads → smaller KV, faster decode, mild accuracy tax

exact kernels

flash-style attention: tile Q/K/V; keep stats in SRAM; exact softmax; IO-aware

big prefill gains; steady decode wins with small tiles

structured sparsity

sliding/dilated windows: O(n·w); strong locality; weak global recall

block-sparse (local + random + global): pattern choice matters; tune or degrade

linearized/low-rank (Performer/Linformer/Nyström): kernel/landmark shortcuts; audit reasoning drift

context extension

RoPE scaling: stretch frequencies; watch positional distortion

sink/primer tokens: early-step stabilizers

memory tokens/layers: learned scratchpad; capacity emergent, not guaranteed

KV cache engineering

shape ≈ [B, L, H, d_k]; bytes ≈ 2·B·L·H·d_k·dtype_size

paged KV: page the cache; kill fragmentation; enable continuous batching

eviction: sliding window baseline → trims by recency/entropy/salience

two regimes

prefill: FLOPs + bandwidth bound → flash kernels, fused MLPs, larger batch

decode: KV-read bound → MQA/GQA, KV locality, KV quantization, careful scheduling

serving levers

batching: continuous intake; chunk prefill to keep SMs utilized

quantization: W8/W4; KV 8/4-bit where stable; re-eval long-context tasks

scheduler: prioritize short jobs; cap context; allow preemption; track P95/P99

parallelism: request-level; speculative paths; shard KV if needed

speculative and guided decode

draft-then-verify: small proposer + large confirmer → fewer expensive steps/token

logit guidance: light penalties/rewards for safety/format; monitor diversity entropy

metrics that decide

TTFT, TPOT, tokens/s/GPU

P95/P99 latency

VRAM per token

throughput at fixed QoS

perplexity/task deltas per change

anti-use rules

no windowed attention for cross-doc reasoning without retrieval

no blind RoPE stretch beyond train regime; test order flips

no MQA/GQA on fragile domains without fresh eval

no sparse patterns in prod without failure audits

shop-floor truths

bandwidth rules; kernels are IO plays first

the fastest model is the one whose KV fits

batch size is a business constraint wearing a tensor

[FP32 Monastic @kv_cached]

https://t.co/Z6DrjQrqgR

[FP32 Monastic @kv_cached]

https://t.co/PdSldY90I1

[FP32 Monastic @kv_cached]

@Mo7amed_Ma3rouf @kwindla @pipecat_ai Thanks @Mo7amed_Ma3rouf happy to help, @kwindla can you please share the pipeline or details how to share the Arabic dataset?

[FP32 Monastic @kv_cached]

@JustinLin610 Hi @JustinLin610 know of a company that uses Qwen-14, fine-tunes it, and now sells it as its own product. What do you think of this unauthorized use?

[FP32 Monastic @kv_cached]

https://t.co/Bf8GGbYRxd

[FP32 Monastic @kv_cached]

https://t.co/TG8Y70SReT

[FP32 Monastic @kv_cached]

Bro… we’re so cooked.

[FP32 Monastic @kv_cached]

Why doesn't anyone include @Alibaba_Qwen in benchmarks? Is it because of the model's power, or are there other factors, perhaps political?
@JustinLin610

[Cohere @cohere]

Introducing Command R7B: the smallest, fastest, and final model in our R series of enterprise-focused LLMs!

It delivers a powerful combination of state-of-the-art performance in its class and efficiency to lower the cost of building AI applications.

https://t.co/e2ah5c5x5J

[FP32 Monastic @kv_cached]

@zaidalyafeai @huggingface No one can
So I joined @far__el to build a new open-source alternative to huggingface