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gemma-4-E2B-it · Q6_K

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Apple M1 Prollama.cpp v0.40L2 Cross-framework verified

This page aggregates 1 real-world runs of gemma-4-E2B-it (Q6_K) on Apple M1 Pro with llama.cpp, contributed by 1 independent source platforms; metrics are averages of published measurements.

93.78 tok/s

Decode

Decode speed

304.74 tok/s

Prefill

Prefill speed

1.57 s

TTFT

Time to first token

6.3 GB

VRAM

VRAM usage

L2 Cross-framework verified

1

Measured runs

1

Independent sources

Other

Source platforms

2 days ago

Last verified

Performance

  1. llama.cpp · Q3_K_M Decode 141.09 · Prefill 627.28 ·
  2. llama.cpp · Q6_K (current)Decode 93.78 · Prefill 304.74 ·
  3. Ollama · Q3_K_L Decode 126.39 · Prefill 536.59 ·
  4. Ollama · Q5_K_M Decode 87.78 · Prefill 394.72 ·

Core figures

Decode (avg)
93.78 tok/s
Prefill (avg)
304.74 tok/s
TTFT (avg)
1.57 s
VRAM (avg)
6.3 GB
MTP acceptance rate
—
TTFB
0.75 GB
Power draw
19 W

Configuration

Member-level fields are taken from the most recent run

Model
gemma-4-E2B-it
Quantization
Q6_K
Framework
llama.cpp
Version
v0.40
Context length
16384 tokens
Batch size
256
GPU layers
99
Flash Attention
On

Hardware

Nominal and measured figures are shown side by side; whether it runs is the reader's call

GPU
Apple M1 Pro
Measured VRAM (avg)
6.3 GB
OS
macOS 15.0
Driver
Apple M1 Pro
CUDA
N/A
Power draw
19 W

Sources & evidence

1 measured records in total, each traceable to its original source

  1. L2 Cross-framework verifiedOtherOriginal link Verified on 2026-10-16

    v0.40 · macOS 15.0 · CUDA N/A · 16384 ctx

    93.78 tok/s

    Decode

    304.74 tok/s

    Prefill

    1.57 s

    TTFT

    6.3 GB

    VRAM

    —

    MTP

    19 W

    Power draw

    L2 batch seed 2026-10-16: llama.cpp on hw_id=100 running model_id=66 at Q6_K