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pepperu96 (@pepperu96) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mla Analysis by pepperu96 — Agent Skill

Name: Mla Analysis
Author: pepperu96

Mla Analysis

MLA (Multi-Latent Attention) cost models, regime analysis, and kernel selection guide. Use when: (1) reasoning about which kernel approach to use for a given regime, (2) understanding cost model tradeoffs between FlashMLA, FlashAttention, and MLAvar6+, (3) analyzing roofline behavior across decode/speculative/prefill regimes, (4) setting optimization targets, (5) understanding MLA math and absorption trick.

pepperu96 1931ced 3.5 KB Updated Sep 17, 2026

File contents

MLA Cost Analysis & Regime Guide

Regime Selection

Regime	s Range	Best Kernel	Why
Decode	s=1	FlashMLA	16x latency reduction vs FlashAttention (compressed KV)
Speculative	s=2-32	MLAvar6+ or FlashMLA	MLAvar6+ should be able to beat FlashMLA and FlashAttention
Prefill	s>128	FlashAttention	Avoids 4x FLOP penalty of latent-space compute

Crossover point: FlashAttention becomes faster than FlashMLA at approximately s=16-32 for DeepSeek-V3 parameters.

Cost Models (DeepSeek-V3-like: h=128, d=128, k=512, p=64)

FlashAttention

FLOPs: 2bhst(2d + p) = 2bhst * 320
Bytes: w * bh(s+t)(2d + p) = w * bh(s+t) * 320
At s=1: AI ≈ 1 FLOP/byte (deeply memory-bound)
At s=1024: AI ≈ 819 FLOP/byte (deeply compute-bound)

FlashMLA (latent-space attention via absorption)

FLOPs: 2bhst(2k + p) = 2bhst * 1088
Bytes: w * (bhs(2k+p) + bt(k+p))
At s=1: AI ≈ 228 FLOP/byte (compute-bound even at decode)
Problem: FLOPs grow linearly with s at k=512 instead of d=128 — 3.4x more FLOPs per token

MLAvar6+ (split latent + decompressed)

FLOPs: 2bhstp + 4bhsnd + 4bhsok
Bytes: w * (bhsp + bhsd + bhsk + bok + btp + bhnd + bhnd + bhsd + bhsk)
Key: choosing n tunes operational intensity near roofline ridge point

Absorption Trick (FlashMLA)

Score:  Q @ K^T = Q @ (Z @ Wk)^T = (Q @ Wk^T) @ Z^T = Qz @ Z^T
Value:  softmax(A) @ V = softmax(A) @ (Z @ Wv) = (softmax(A) @ Z) @ Wv

Operate entirely in latent space (k-dim), with output decompression O_latent @ W_kvb2^T done by a separate kernel.

MLAvar6+ Design

Split KV cache into:

o old tokens: latent Z (b, o, k) — compute-heavy, low bandwidth
n new tokens: decompressed K,V (b, n, h, d) — bandwidth-heavy, low compute
t = o + n

Interpolates: n=0 → FlashMLA-like, o=0 → FlashAttention-like.

Historical Baselines (RTX 5090, b=32, t=4096, bfloat16)

Note: These results are from RTX 5090 development. Reprofile on the current device before using as optimization targets. Ridge point, bandwidth, and compute ceilings differ across devices — see src/mla_var3/conf/devices.json.

Config	FlashMLA	FlashAttention	MLAvar6+ V3 (best)
s=1	419 μs	6,781 μs	829 μs (V2)
s=16	5,161 μs	6,727 μs	4,444 μs

MLAvar6+ V3 is the current best for speculative decoding (s=16), beating FlashMLA by 14%.

Tensor Shapes (DeepSeek-V3 defaults)

Symbol	Description	Default
b	Batch size	64
h	Number of heads	128
s	Query sequence length	varies
t	KV context length	4096
d	Head dimension	128
p	Positional embedding dim	64
k	Latent (compressed) dim	512

Tensor layouts:

Q, Qc: [B, H, S, D] or [B, H, S, K]
K, V: [B, T, H, D] (decompressed)
Z: [B, T, K] (latent KV cache)
QPE, KPE: [B, H, S, P] or [B, T, P]

Detailed Analysis

Full cost model derivations and roofline analysis: docs/cost-analysis.md

---name: mla-analysisdescription: MLA (Multi-Latent Attention) cost models, regime analysis, and kernel selection guide. Use when: (1) reasoning about which kernel approach to use for a given regime, (2) understanding cost model tradeoffs between FlashMLA, FlashAttention, and MLAvar6+, (3) analyzing roofline behavior across decode/speculative/prefill regimes, (4) setting optimization targets, (5) understanding MLA math and absorption trick.---# MLA Cost Analysis & Regime Guide## Regime Selection| Regime | s Range | Best Kernel | Why ||--------|---------|------------|-----|| Decode | s=1 | FlashMLA | 16x latency reduction vs FlashAttention (compressed KV) || Speculative | s=2-32 | MLAvar6+ or FlashMLA | MLAvar6+ should be able to beat FlashMLA and FlashAttention || Prefill | s>128 | FlashAttention | Avoids 4x FLOP penalty of latent-space compute |**Crossover point**: FlashAttention becomes faster than FlashMLA at approximately s=16-32 for DeepSeek-V3 parameters.## Cost Models (DeepSeek-V3-like: h=128, d=128, k=512, p=64)### FlashAttention- FLOPs: `2bhst(2d + p)` = `2bhst * 320`- Bytes: `w * bh(s+t)(2d + p)` = `w * bh(s+t) * 320`- At s=1: AI ≈ 1 FLOP/byte (deeply memory-bound)- At s=1024: AI ≈ 819 FLOP/byte (deeply compute-bound)### FlashMLA (latent-space attention via absorption)- FLOPs: `2bhst(2k + p)` = `2bhst * 1088`- Bytes: `w * (bhs(2k+p) + bt(k+p))`- At s=1: AI ≈ 228 FLOP/byte (compute-bound even at decode)- **Problem**: FLOPs grow linearly with s at k=512 instead of d=128 — 3.4x more FLOPs per token### MLAvar6+ (split latent + decompressed)- FLOPs: `2bhstp + 4bhsnd + 4bhsok`- Bytes: `w * (bhsp + bhsd + bhsk + bok + btp + bhnd + bhnd + bhsd + bhsk)`- **Key**: choosing n tunes operational intensity near roofline ridge point## Absorption Trick (FlashMLA)```Score: Q @ K^T = Q @ (Z @ Wk)^T = (Q @ Wk^T) @ Z^T = Qz @ Z^TValue: softmax(A) @ V = softmax(A) @ (Z @ Wv) = (softmax(A) @ Z) @ Wv```Operate entirely in latent space (k-dim), with output decompression `O_latent @ W_kvb2^T` done by a separate kernel.## MLAvar6+ DesignSplit KV cache into:- **o old tokens**: latent Z (b, o, k) — compute-heavy, low bandwidth- **n new tokens**: decompressed K,V (b, n, h, d) — bandwidth-heavy, low compute- t = o + nInterpolates: n=0 → FlashMLA-like, o=0 → FlashAttention-like.## Historical Baselines (RTX 5090, b=32, t=4096, bfloat16)> **Note**: These results are from RTX 5090 development. Reprofile on the current device before using as optimization targets. Ridge point, bandwidth, and compute ceilings differ across devices — see `src/mla_var3/conf/devices.json`.| Config | FlashMLA | FlashAttention | MLAvar6+ V3 (best) ||--------|----------|----------------|---------------------|| s=1 | **419 μs** | 6,781 μs | 829 μs (V2) || s=16 | 5,161 μs | 6,727 μs | **4,444 μs** |MLAvar6+ V3 is the current best for speculative decoding (s=16), beating FlashMLA by 14%.## Tensor Shapes (DeepSeek-V3 defaults)| Symbol | Description | Default ||--------|-------------|---------|| b | Batch size | 64 || h | Number of heads | 128 || s | Query sequence length | varies || t | KV context length | 4096 || d | Head dimension | 128 || p | Positional embedding dim | 64 || k | Latent (compressed) dim | 512 |**Tensor layouts**:- Q, Qc: `[B, H, S, D]` or `[B, H, S, K]`- K, V: `[B, T, H, D]` (decompressed)- Z: `[B, T, K]` (latent KV cache)- QPE, KPE: `[B, H, S, P]` or `[B, T, P]`## Detailed AnalysisFull cost model derivations and roofline analysis: `docs/cost-analysis.md`

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