Apple M4 Pro 48GB: Local LLM Inference Benchmark

Benchmarking local LLM inference on Apple M4 Pro, comparing LM Studio vs Ollama backends across multiple models and context sizes.

Hardware

Spec	Detail
Chip	Apple M4 Pro
CPU	14-core (10P + 4E)
GPU	20-core
Memory	48GB Unified
Memory Bandwidth	~273 GB/s
OS	macOS Sequoia (Darwin 25.2.0)

Models Tested

Model	Type	Params (active)	Size	Backend
Qwen3-Coder-30B-A3B	MoE	30B (3B)	~18GB	MLX (both)
Qwen3.5-35B-A3B	MoE	35B (3B)	~24GB	GGUF (llama.cpp)*
Gemma 4 26B-A4B	MoE	26B (4B)	~18GB	GGUF (llama.cpp)*

* Qwen 3.5 and Gemma 4 MLX backends are not yet supported in Ollama/LM Studio as of April 2026. These models fall back to llama.cpp with Metal acceleration.

Results Summary

Generation Speed (tok/s) — All Models

Model	LM Studio	Ollama	Notes
Qwen3-30B-A3B	79.0	65.9	Both MLX
Gemma 4 26B-A4B	—	59.5 (short) / 56.4 (long)	GGUF, Ollama only
Qwen3.5-35B-A3B	—	27.5 (short) / 28.5 (long)	GGUF, Ollama only

Time to First Token — All Models

Model	LM Studio	Ollama
Qwen3-30B-A3B	0.179s	0.087s
Gemma 4 26B-A4B	—	0.210s
Qwen3.5-35B-A3B	—	0.410s

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Detailed Results

1. Qwen3-Coder-30B-A3B — LM Studio (MLX) vs Ollama (MLX)

Both backends use MLX. This is the purest comparison of LM Studio vs Ollama overhead.

Prompt	Metric	LM Studio	Ollama	Winner
Short (~100 tok)	Gen tok/s	87.3	64.5	LM Studio +35%
	TTFT	0.17s	0.09s	Ollama +47%
Medium (~500 tok)	Gen tok/s	83.3	68.2	LM Studio +22%
	TTFT	0.17s	0.08s	Ollama +51%
Long (~2K tok)	Gen tok/s	79.3	65.7	LM Studio +21%
	TTFT	0.15s	0.08s	Ollama +43%
Long ctx (~2K in)	Gen tok/s	66.2	65.2	~Tie
	TTFT	0.23s	0.09s	Ollama +61%

Verdict: LM Studio generates 20% faster, Ollama responds 51% sooner.

2. Gemma 4 26B-A4B — Ollama (GGUF/llama.cpp)

Prompt	Gen tok/s	TTFT	Prompt eval tok/s	Total time
Short (~100 tok)	59.5	0.21s	138	2.73s
Medium (~500 tok)	58.0	0.21s	183	14.00s
Long (~2K tok)	56.4	0.21s	223	36.55s
Context 4K	53.3	0.23s	21,020	9.84s
Context 8K	50.8	0.25s	37,972	10.34s
Context 16K	45.5	0.29s	66,870	11.54s
Context 32K	47.8	70.15s	467	74.23s

Key observations:

• Very consistent ~56-59 tok/s generation across short-to-long outputs
• Excellent prompt processing up to 16K context (67K tok/s at 16K!)
• 32K context causes a dramatic cliff: TTFT explodes to 70s, prompt eval drops from 67K to 467 tok/s. This suggests the KV cache exceeds available fast memory at 32K.
• Only generated 195/512 tokens at 32K context (model struggles with the filler content)

3. Qwen3.5-35B-A3B — Ollama (GGUF/llama.cpp)

Prompt	Gen tok/s	TTFT	Prompt eval tok/s	Total time
Short (~100 tok)	27.5	0.41s	83	5.87s
Medium (~500 tok)	26.7	0.37s	111	30.57s
Long (~2K tok)	28.5	0.42s	119	72.40s
Context 4K	28.0	4.18s	1,053	22.49s
Context 8K	27.8	0.54s	16,410	18.99s
Context 16K	25.6	1.97s	8,995	21.97s
Context 32K	9.3	85.11s	386	140.17s

Key observations:

• Consistent ~27-28 tok/s generation (about half of Gemma 4, despite similar MoE architecture)
• Running on GGUF/llama.cpp — Ollama's MLX backend does not yet support Qwen 3.5 architecture
• 32K context is painful: 85s TTFT, generation drops to 9.3 tok/s
• 8K prompt eval is surprisingly fast (16K tok/s) — likely KV cache hit from warmup

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Context Window Scaling

How TTFT scales with input context size:

Context    Gemma 4 26B        Qwen3.5 35B
   4K      0.23s              4.18s
   8K      0.25s              0.54s *
  16K      0.29s              1.97s
  32K      70.15s !!          85.11s !!

* 8K TTFT anomalously low for Qwen3.5 — likely KV cache reuse from warmup run.

Both models hit a wall at 32K context on 48GB. The KV cache for 32K tokens at these model sizes likely exceeds the available unified memory after model weights are loaded, causing heavy swapping.

Practical context limit on M4 Pro 48GB: ~16K tokens for these 18-24GB models.

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Analysis

MLX vs llama.cpp on Apple Silicon

Backend	Best for	Example
MLX	Supported models (Qwen3)	79 tok/s gen, 87ms TTFT
llama.cpp/GGUF	Broader model support	28-59 tok/s gen, 200ms+ TTFT

MLX delivers 2-3x better performance when the model architecture is supported. As of April 2026, MLX in Ollama only supports Qwen3/3.5-A3B (coding variant) and Gemma 3.

Model Comparison on Ollama

	Gemma 4 26B	Qwen3.5 35B
Gen speed	2x faster	Slower (GGUF)
TTFT	2x faster	Slower
Context scaling	Better up to 16K	Similar cliff at 32K
Model quality	Strong general	Latest Qwen reasoning

Recommendations

Use case	Best choice
Fast interactive chat	Qwen3-30B via LM Studio MLX
Coding assistance	Qwen3-Coder-30B via LM Studio MLX
General tasks (no MLX)	Gemma 4 26B via Ollama
Large context (up to 16K)	Gemma 4 26B — best TTFT scaling
Maximum context (32K+)	Needs more RAM or smaller model

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Reproduce

Quick start

# Run all benchmarks automatically
./run.sh

# List available model configs
./run.sh --list

# Test specific model
./run.sh --models 0 --runs 3

# Custom context sizes
./run.sh --context "4,8,16,32,64"

# Ollama only (skip LM Studio)
./run.sh --skip-lmstudio

# Quick test
./run.sh --models 0 --prompts short --runs 1

Manual benchmark

python3 benchmark.py \
  --ollama-model "gemma4:26b" \
  --context-sizes "4,8,16,32" \
  --runs 3 \
  --output results/my_test.json

Prerequisites

• Mac with Apple Silicon (32GB+ unified memory)
• Ollama (brew install ollama)
• LM Studio (optional)
• Python 3.10+ with httpx (pip install httpx)

Files

File	Description
benchmark.py	Python benchmark engine
run.sh	Automated orchestrator
results/	JSON result files per model
results.json	Legacy Qwen3 LM Studio vs Ollama results

License

MIT