Local AI Advisor

Qwen3-32B on local hardware

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

Qwen3-32BOpen source

Qwen · apache-2.0 · 2,905,251 downloads

Hugging Face
Reasoning / thinking mode
Parameters
33B exact
Context window
40K tokens
Attention
Standard (full attention)
Layers · KV heads · head dim
64 · 8 · 128
KV cache
256 KB/token · 8.0 GB at 32K
Tool calling
good (from chat template)
Thinking
good (from chat template)
Vision
basic (inferred — no supporting signal)
Commercial use
allowedRetain the required license and attribution notices
Import confidence
medium · capabilities estimated

Weights by quantization

Q3_K_M

15 GB

gguf file size

Q4_K_M

18 GB

gguf file size

Q5_K_M

22 GB

gguf file size

Q6_K

25 GB

gguf file size

Q8_0

32 GB

gguf file size

F16 / BF16

61 GB

gguf file size

  • • vision: basic, inferred from missing signals (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.

Qwen3-32B — 4-bit

Borderline for your workload

Borderline

Speed

7–10 tok/s

Too slow

Memory left

9.8 GB

of 48 GB

First word in

≈3 min

Too slow

All the details
Your setup
MacBook Pro M4 Pro 48 GB
Workload
Repository coding with Aider
Runtime → tool
LM Studio → Aider
Memory used
27 GB
Context
32K · up to 8K practical
Tool support
Official
Use-case fit
Partly suited

Why?

  • Model fits with approximately 9.8 GB of memory headroom.
  • LM Studio supports hardware acceleration on this machine (Metal (Apple GPU)).
  • Aider can connect to LM Studio (officially supported).
  • 32K window; generation drops to 6–9 tok/s when it is full.
  • Coding capability is below what interactive coding ideally needs — expect more mistakes or retries.
  • Generation (7–10 tok/s) is slow for interactive coding.
  • Ingesting a prompt of 14K tokens takes ≈3 min and each step ≈3 min; across ~4 calls a task takes ≈14 min.

Why “Borderline” rather than “Acceptable”?

  • Prompt processing: Ingesting the first prompt (14K tokens) takes ≈3 min; each of the ~4 follow-up turns takes ≈3 min (≈14 min per task).
  • Generation: Expected 7–10 tok/s at ~16K tokens of context. Coding with repository context is comfortable from about 28 tok/s.
  • Context: 32K window; generation drops to 6–9 tok/s when it is full.
  • Workload suitability: Coding capability is below what interactive coding ideally needs — expect more mistakes or retries.

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.
Medium confidence
Check with your hardware and workload