MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
A
Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Can InternLM 7B run on MacBook Air M3 24GB?
YES — Tight Fit
B69Good
○Estimated from fit model
InternLM 7B needs ~15.6 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 15.6 GB, 15.9 tok/s, Tight fit
15.6 GB required17.3 GB available
90% VRAM used
Fit status
Tight fit
Decode
15.9 tok/s
TTFT
12157 ms
Safe context
8K
Memory
15.6 GB / 17.3 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsInternLM 7B on MacBook Air M3 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 15.9 tok/s decode · 12.2s TTFT (warm) · 40 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 15.9 tok/s | 6631 ms | 8K |
| Coding | B | Tight fit | 15.9 tok/s | 12157 ms | 8K |
| Agentic Coding | F | Too heavy | 10.5 tok/s | 26856 ms | 8K |
| Reasoning | B | Tight fit | 15.9 tok/s | 14367 ms | 8K |
| RAG | F | Too heavy | 10.5 tok/s | 33570 ms | 8K |
Quantization options
How InternLM 7B (7B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B67 |
NVFP4 | 4 |
Get started
Copy-paste commands to run InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs InternLM 7B well
RX 7900 XT 20GBBiggest leap
800 GB/s (+700)
A
Raises estimated decode speed by about 516%.98 tok/s decode
Raises estimated decode speed by about 516%.
~$899 MSRP
Mac mini M4 32GBBest value
32 GB Unified (+8)120 GB/s (+20)
A
Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M4 Pro 64GBApple upgrade
64 GB Unified (+40)273 GB/s (+173)
B
Raises estimated decode speed by about 185%.45.3 tok/s decode
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP