I have been putting my new Apple M6 Mac Mini through its paces, and I wanted to see how it handles a larger local language model compared with my M1 Max Mac Studio.
The Mac Mini is tiny, quiet and very easy to underestimate. The Mac Studio has a Max chip, a wider memory system and a much larger chassis. On paper, this should be an easy win for the Studio when running a larger LLM.
The interesting question was whether the new M6 Mac Mini could run a 7.5B model comfortably while still leaving enough memory for macOS and Ollama.
The answer is yes. It handled the model without crashing or running out of memory, although this is getting close to the practical limit for a 16 GB Mini.
The hardware
The model
I used the Ollama model gemma4:e4b-it-qat.
Before starting, I stopped every other Ollama runner on both Macs. I then confirmed that only gemma4:e4b-it-qat was loaded during the benchmark.
Generation speed
This is where the older Mac Studio fights back. It generated answer tokens around 50% faster than the new Mini in the single request test. The Studio also kept its lead when handling two requests at the same time.
That result makes sense given the M1 Max memory system. Local LLM generation is not just about CPU speed. Memory bandwidth and GPU throughput matter a lot once the model is loaded.
Prompt processing
The Mini was much faster at reading and processing the prompt before generating an answer. This is useful for document analysis, retrieval workloads and long instructions where the model has to absorb a lot of input.
Can the 16 GB Mac Mini handle it?
Yes, but I would describe this as the upper end of the sensible range for an always-on 16 GB machine.
After the test, with only the Gemma model loaded, the Mini reported 24% system-wide free memory. It used around 300 MB of swap, but the test completed normally and the machine remained responsive.
For a small always-on local AI server, I would be comfortable using this model on the Mini for moderate workloads. I would not move much larger without accepting more swapping, slower context handling and less room for other services.
My conclusion
The new M6 Mac Mini is a very capable little machine. It handled a 7.5B local model in a small, quiet enclosure and still had some memory headroom left.
The M1 Max Mac Studio remains the better choice if the main job is generating lots of tokens from a local LLM. It was consistently faster at generation and concurrent requests.
The M6 Mini was surprisingly strong at prompt processing and cold model loading. For automation, document analysis, coding assistants and a modest always-on Ollama service, it looks like a very good fit.
The wider picture is becoming clear from these tests. The new M6 Mac Mini has the stronger general CPU, while the older M1 Max Mac Studio still has advantages for memory-heavy local AI work.
I also ran a smaller Qwen comparison, which is available here: Qwen3.5:2b benchmarks on the M6 Mac Mini and M1 Max Mac Studio.
If anyone wants the benchmark scripts or would like me to test another model that can realistically fit inside 16 GB of unified memory, let me know.
The Mac Mini is tiny, quiet and very easy to underestimate. The Mac Studio has a Max chip, a wider memory system and a much larger chassis. On paper, this should be an easy win for the Studio when running a larger LLM.
The interesting question was whether the new M6 Mac Mini could run a 7.5B model comfortably while still leaving enough memory for macOS and Ollama.
The answer is yes. It handled the model without crashing or running out of memory, although this is getting close to the practical limit for a 16 GB Mini.
The hardware
- New Apple Mac Mini M6: 12 logical CPUs and 16 GB unified memory
- Mac Studio with Apple M1 Max: 10 logical CPUs
- Both machines were accessed remotely over Tailscale
- Only the benchmark model was loaded during testing
The model
I used the Ollama model gemma4:e4b-it-qat.
- 7.5B parameters
- Q4_0 quantization
- Ollama digest: ee6656371218
- Approximately 5.4 GB resident model size
- 4,096-token test context
- Temperature set to 0
- GPU acceleration enabled on both machines
Before starting, I stopped every other Ollama runner on both Macs. I then confirmed that only gemma4:e4b-it-qat was loaded during the benchmark.
Generation speed
| Test | New Mac Mini M6 | M1 Max Mac Studio | Result |
|---|---|---|---|
| 128-token generation | 43.0 tokens/sec | 64.7 tokens/sec | M1 Max +50% |
| Two simultaneous requests | 41.7 aggregate tokens/sec | 59.8 aggregate tokens/sec | M1 Max +43% |
| Cold model load | 1.89 seconds | 3.60 seconds | M6 faster |
This is where the older Mac Studio fights back. It generated answer tokens around 50% faster than the new Mini in the single request test. The Studio also kept its lead when handling two requests at the same time.
That result makes sense given the M1 Max memory system. Local LLM generation is not just about CPU speed. Memory bandwidth and GPU throughput matter a lot once the model is loaded.
Prompt processing
| Prompt size | New Mac Mini M6 | M1 Max Mac Studio | Result |
|---|---|---|---|
| About 1,563 prompt tokens | 1,196 tok/sec | 611 tok/sec | M6 +96% |
| About 2,051 prompt tokens | 1,250 tok/sec | 612 tok/sec | M6 +104% |
The Mini was much faster at reading and processing the prompt before generating an answer. This is useful for document analysis, retrieval workloads and long instructions where the model has to absorb a lot of input.
Can the 16 GB Mac Mini handle it?
Yes, but I would describe this as the upper end of the sensible range for an always-on 16 GB machine.
After the test, with only the Gemma model loaded, the Mini reported 24% system-wide free memory. It used around 300 MB of swap, but the test completed normally and the machine remained responsive.
For a small always-on local AI server, I would be comfortable using this model on the Mini for moderate workloads. I would not move much larger without accepting more swapping, slower context handling and less room for other services.
My conclusion
The new M6 Mac Mini is a very capable little machine. It handled a 7.5B local model in a small, quiet enclosure and still had some memory headroom left.
The M1 Max Mac Studio remains the better choice if the main job is generating lots of tokens from a local LLM. It was consistently faster at generation and concurrent requests.
The M6 Mini was surprisingly strong at prompt processing and cold model loading. For automation, document analysis, coding assistants and a modest always-on Ollama service, it looks like a very good fit.
The wider picture is becoming clear from these tests. The new M6 Mac Mini has the stronger general CPU, while the older M1 Max Mac Studio still has advantages for memory-heavy local AI work.
I also ran a smaller Qwen comparison, which is available here: Qwen3.5:2b benchmarks on the M6 Mac Mini and M1 Max Mac Studio.
If anyone wants the benchmark scripts or would like me to test another model that can realistically fit inside 16 GB of unified memory, let me know.