---
title: "Ollama vs LM Studio: Apple Silicon Benchmark"
description: "Ollama vs LM Studio benchmark on Apple Silicon: tok/s, TTFT, power, VRAM compared side by side on M4 Pro with real measurements."
type: article
date: 2026-03-28
updated: 2026-03-29
dataset:
  name: "Ollama vs LM Studio Benchmark on Apple Silicon M4 Pro"
  description: "Head-to-head benchmark comparing Ollama (llama.cpp) and LM Studio (MLX) on Mac Mini M4 Pro 64GB with Qwen3-Coder-30B. Metrics: tok/s, TTFT, GPU power, efficiency, VRAM."
  date: "2026-03"
---

# Ollama vs LM Studio: Apple Silicon Benchmark

Which inference engine is faster on your Mac? We benchmarked Ollama (llama.cpp backend) and LM Studio (MLX backend) head-to-head on the same model and hardware using asiai 1.4.0 in March 2026.

## Test Setup

| | |
|---|---|
| **Hardware** | Mac Mini M4 Pro, 64 GB unified memory |
| **Model** | Qwen3-Coder-30B (MoE architecture, Q4_K_M / MLX 4-bit) |
| **asiai version** | 1.4.0 |
| **Methodology** | 1 warmup + 1 measured run per engine, temperature=0, model unloaded between engines ([full methodology](methodology.md)) |

## Results

| Metric | LM Studio (MLX) | Ollama (llama.cpp) | Difference |
|--------|-----------------|-------------------|------------|
| **Throughput** | 102.2 tok/s | 69.8 tok/s | **+46%** |
| **TTFT** | 291 ms | 175 ms | Ollama faster |
| **GPU Power** | 12.4 W | 15.4 W | **-20%** |
| **Efficiency** | 8.2 tok/s/W | 4.5 tok/s/W | **+82%** |
| **Process Memory** | 21.4 GB (RSS) | 41.6 GB (RSS) | -49% |

!!! note "About memory numbers"
    Ollama pre-allocates KV cache for the full context window (262K tokens), which inflates its memory footprint. LM Studio allocates KV cache on demand. The process RSS reflects total memory used by the engine process, not just model weights.

## Key Findings

### LM Studio wins on throughput (+46%)

MLX's native Metal optimization extracts more bandwidth from Apple Silicon's unified memory. On MoE architectures, the advantage is significant. On the larger Qwen3.5-35B-A3B variant, we measured an even wider gap: **71.2 vs 30.3 tok/s (2.3x)**.

### Ollama wins on TTFT

Ollama's llama.cpp backend processes the initial prompt faster (175ms vs 291ms). For interactive use with short prompts, this makes Ollama feel snappier. For longer generation tasks, LM Studio's throughput advantage dominates total time.

### LM Studio is more power-efficient (+82%)

At 8.2 tok/s per watt vs 4.5, LM Studio generates nearly twice as many tokens per joule. This matters for laptops on battery and for sustained workloads on always-on servers.

### Memory usage: context matters

The large gap in process memory (21.4 vs 41.6 GB) is partly due to Ollama pre-allocating KV cache for its maximum context window. For a fair comparison, consider the actual context used during your workload, not the peak RSS.

## When to Use Each

| Use Case | Recommended | Why |
|----------|------------|-----|
| **Maximum throughput** | LM Studio (MLX) | +46% faster generation |
| **Interactive chat (low latency)** | Ollama | Lower TTFT (175 vs 291 ms) |
| **Battery life / efficiency** | LM Studio | 82% more tok/s per watt |
| **Docker / API compatibility** | Ollama | Broader ecosystem, OpenAI-compat API |
| **Memory-constrained (16GB Mac)** | LM Studio | Lower RSS, on-demand KV cache |
| **Multi-model serving** | Ollama | Built-in model management, keep_alive |

## Other Models

The throughput gap varies by model architecture:

| Model | LM Studio (MLX) | Ollama (llama.cpp) | Gap |
|-------|-----------------|-------------------|-----|
| Qwen3-Coder-30B (MoE) | 102.2 tok/s | 69.8 tok/s | +46% |
| Qwen3.5-35B-A3B (MoE) | 71.2 tok/s | 30.3 tok/s | +135% |

MoE models show the largest differences because MLX handles sparse expert routing more efficiently on Metal.

## Run Your Own Benchmark

```bash
pip install asiai
asiai bench --engines ollama,lmstudio --prompts code --runs 3 --card
```

asiai compares engines side by side with the same model, same prompts, and same hardware. Models are automatically unloaded between engines to prevent memory contention.

[View the full methodology](methodology.md) · [See the community leaderboard](leaderboard.md) · [How to benchmark LLMs on Mac](benchmark-llm-mac.md)
