KV Cache Explains Memory Differences | Generated by AI

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Question: Why does Gemma-4 E2B use less memory than A26B MoE at the same context length and same quantization, and what context length can run on mobile (32k vs 65k)?

Answer:

This confusion usually comes from MoE vs dense model memory behavior and KV cache scaling. Even if quantization bits are the same, runtime memory is dominated by KV cache, not just weights.

The key relationship:

\text{KV cache memory} \approx 2 \times L \times H \times T \times \text{bytes per element}

Where:

• (L) = number of layers
• (H) = hidden size
• (T) = context length (32k, 65k, etc.)
• factor 2 = K + V cache
• bytes = depends on fp16 / q8 / q4 etc.

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1. Why Gemma-4 E2B uses LESS memory than A26B MoE

Even though A26B is MoE, KV cache is NOT sparse.

Important:

• MoE reduces compute
• MoE does NOT reduce KV cache
• KV cache depends on hidden size, not number of active experts

So if:

• A26B has larger hidden dimension
• more heads
• more layers

then its KV cache becomes much larger.

This is why:

• same quantization
• same context length
• A26B MoE still uses more RAM

because:

MoE sparsity only applies to FFN weights, but KV cache stores full attention states.

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2. Why E2B is especially memory efficient

Gemma-4 E2B likely has:

• smaller hidden size
• fewer layers
• smaller head dimension

So KV cache is much smaller.

Example intuition:

model	hidden size	layers	KV memory
E2B	smaller	fewer	small
A26B MoE	bigger	more	large

Even if A26B activates only 2 experts:

KV cache still full size.

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3. Context length on mobile (32k vs 65k)

Context length scales linearly:

32k → baseline 65k → ~2× memory

So if:

32k fits → 65k likely OOM

Typical mobile / small RAM:

• 7B / E2B → 32k OK
• 65k → borderline / OOM
• A26B → even 32k heavy

Because:

KV cache dominates after ~16k.

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4. Why quantization doesn’t help much here

Quantization reduces:

• model weights

But KV cache usually stays fp16 / fp8

So memory ≈ dominated by KV cache

Therefore:

same quantization but different hidden size → big memory difference

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5. Simple mental model

Dense model memory:

weights + KV cache

MoE model memory:

weights (smaller active) + same KV cache

So:

MoE saves compute NOT context memory

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Final intuition

E2B smaller attention dimension → smaller KV cache → longer context fits

A26B MoE larger attention dimension → huge KV cache → memory explodes

So E2B can run:

• 32k easier
• maybe 65k

while A26B struggles even at 32k.

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