mla-analysis

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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.

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Sourcepepperu96/hyper-mla

Added on2026-04-22

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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