Slides: The Small Model Infrastructure Nobody Built (So We Did) — Filip Makraduli, Superlinked
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The Small Model Infrastructure Nobody Built (So We Did) — Filip Makraduli, Superlinked
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Extracted Slides
OCR text:
PLATINUM SPONSORS
Braintrust WorkOS OpenAI
OCR text:
TheSmall-Model
Infrastructure
Nobody Built
(sowe did）
FilipMakraduli·Superlinked
AlEngineer
EUROPE
OCR text:
A WRITER'S DIARY ON AI
What Actually Makes Embedding
Model Inference Fast?
From Flash Attention to Quantization, where is the inference bottleneck? Is it the architecture, the maths, or will writing everything in Rust solve all my problems? (Hint: It's Not Rust)
FILIP MAKRADULI
JAN 22, 2026
Not quite.
Google DeepMind
OCR text:
I realised... I had to learn
AI Engineer
EUROPE
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Traming & re tening area pose DCMT RL stenet a gern
va Eva‘vation & bencnmark:ng GPU uti! zat on & schedu .ng
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Best way to learn?
Join the team building it.
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Context management
Taxonomy classification
1OK categories Shopify product catalogue text + images
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Switching model type = changing one parameter, not
rebuilding infra
] Engineering the future of Al
OCR text:
Model Performance V5 Size
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OCR text:
SO, LET'S SUPPORT 85+ MODELS WITH 35+ RUNTIMES AND INFERENCE BACKENDS
Every architecture is different under the hood
bert_flash
Fixed position IDs (0,1,2,...)
Each token knows its absolute slot
qwen2_flash
RoPE: rotates Q/K vectors by position
GQA: fewer KV heads = less memory
modernbert_flash
Fused QKV: one matrix, not three
Pre-normalize before attention
colbert_*
Keeps every token's vector (not pooled)
Late interaction: match token-by-token
cross_encoder
Takes a pair (query + doc) as input
Outputs a score, not a vector
clip_siglip_florence2
Image encoder + text encoder
Shared embedding space
splade_flash
Sparse 250K-dim vocabulary vector
Learned term weights, not dense floats
sglang
48-88 embedding models
KV cache = paged memory
No universal engine
BERT + Qwen2 + ModernBERT.
Each needs its own inference path.
An agentic workflow
We wrote each adapter using AI agents
until we had coverage across 90 models.
Re-implementation
Not wrappers. We re-implement
the forward pass and pooling for each arch.
15 of 35 adopters use Flash Attention 2 with variable-length sequence packing
OCR text:
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Attention GGA (fewer KV)
Output
Allon packed sequences with = zero padding waste
Engineering the future of Al
ES) >
OCR text:
We open sourced both the yin (model inference) and the
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