MacBook Pro M4 32GBコスパ重視
32 GB Unified (+8)120 GB/s (+20)
A
Adds memory headroom for longer context windows and future model growth.18.6 tok/sデコード
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
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 | 3.9 GB | Medium | B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B68 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A71 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 99アップグレードオプション
InternLM 7Bを快適に動かすハードウェア
RX 7900 XT 20GB最大の飛躍
800 GB/s (+700)
A
Raises estimated decode speed by about 516%.98 tok/sデコード
Raises estimated decode speed by about 516%.
〜$899 MSRP
Mac mini M4 32GBコスパ最良
32 GB Unified (+8)120 GB/s (+20)
A
Adds memory headroom for longer context windows and future model growth.18.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
MacBook Pro M4 Pro 64GBAppleアップグレード
64 GB Unified (+40)273 GB/s (+173)
B
Raises estimated decode speed by about 185%.45.3 tok/sデコード
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can MacBook Air M3 24GB run InternLM 7B?
Yes, MacBook Air M3 24GB can run InternLM 7B with a B grade (Tight fit). Expected decode speed: 15.9 tok/s.
How much VRAM does InternLM 7B need?
InternLM 7B (7B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
What is the best quantization for InternLM 7B?
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will InternLM 7B run at on MacBook Air M3 24GB?
On MacBook Air M3 24GB, InternLM 7B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12157ms using Q4_K_M quantization.
Can MacBook Air M3 24GB run InternLM 7B for coding?
For coding workloads, InternLM 7B on MacBook Air M3 24GB receives a B grade with 15.9 tok/s and 8K context.
What context window can InternLM 7B use on MacBook Air M3 24GB?
On MacBook Air M3 24GB, InternLM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on MacBook Air M3 24GB as fast as VRAM for InternLM 7B?
Not always. MacBook Air M3 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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