Local AI Advisor

gemma-4-26B-A4B-it on local hardware

Estimated comfort for repository coding with Aider on a MacBook Pro M4 Pro 48GB.

gemma-4-26B-A4B-itOpen source

google · apache-2.0 · 10,291,857 downloads

Hugging Face
Mixture of expertsVisionReasoning / thinking mode
Parameters
26B exact
Active per token
≈3.7B (8 of 128 experts)
Context window
256K tokens
Attention
Sliding-window + global layers
Layers · KV heads · head dim
30 · 8 · 256
KV cache
20 KB/token · 0.8 GB at 32K
Tool calling
good (from chat template)
Thinking
good (from chat template)
Vision
good (from config)
Commercial use
allowedRetain the required license and attribution notices
Import confidence
medium · capabilities estimated

Weights by quantization

Q3_K_M

12 GB

gguf file size

Q4_K_M

16 GB

gguf file size

Q5_K_M

20 GB

gguf file size

Q6_K

22 GB

gguf file size

Q8_0

25 GB

gguf file size

F16 / BF16

47 GB

gguf file size

MXFP4

15 GB

gguf file size

  • • vision: good, inferred from config (low confidence).
  • • Capability ratings are estimated from model size and name — this model hasn't been assessed by hand.
  • • GGUF sizes come from community conversions matched by model name; equivalence and runtime support are not verified. MLX conversion availability is unverified.

gemma-4-26B-A4B-it — MXFP4

Acceptable for your workload

Acceptable

Speed

55–75 tok/s

Excellent

Memory left

22 GB

of 48 GB

First word in

≈20 s

Comfortable

All the details
Your setup
MacBook Pro M4 Pro 48 GB
Workload
Repository coding with Aider
Runtime → tool
LM Studio → Aider
Memory used
15 GB
Context
32K · up to 128K practical
Tool support
Official
Use-case fit
Poorly suited

Why?

  • Model fits with approximately 22 GB of memory headroom.
  • LM Studio supports hardware acceleration on this machine (Metal (Apple GPU)).
  • Aider can connect to LM Studio (officially supported).
  • Generation (55–75 tok/s) should be appropriate for interactive coding.
  • Ingesting a prompt of 14K tokens takes ≈20 s; prompt processing keeps up with interactive coding.
  • 32K working context is reasonable.
  • Weak coding capability for interactive coding.

Why “Acceptable” rather than “Comfortable”?

  • Workload suitability: Weak coding capability for interactive coding.

How sure are we?

Medium confidence

Estimated from bandwidth and compute, calibrated against 2 verified benchmarks of other models on this same chip (measured with a different engine, so calibration is partial).

  • Calibrated with verified benchmarks of other models on this chip.
  • MoE performance varies more between runtimes.
Medium confidence
Check with your hardware and workload