mla-analysis

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