MacBook Pro M4 32GBコスパ重視
32 GB Unified (+8)
B
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 MPT-7B-Instruct run on MacBook Air M4 24GB?
YES — Tight Fit
B65Good
Estimated — low-sample bucket· few comparable runs
MPT-7B-Instruct needs ~15.6 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~19 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, 18.6 tok/s, Tight fit
15.6 GB required17.3 GB available
90% VRAM used
Fit status
Tight fit
Decode
18.6 tok/s
TTFT
10400 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 feelsMPT-7B-Instruct on MacBook Air M4 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 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 | B | Runs well | 18.6 tok/s | 5673 ms | 8K |
| Coding | B | Tight fit | 18.6 tok/s | 10400 ms | 8K |
| Agentic Coding | F | Too heavy | 12.3 tok/s | 22975 ms | 8K |
| Reasoning | B | Tight fit | 18.6 tok/s | 12291 ms | 8K |
| RAG | F | Too heavy | 12.3 tok/s | 28718 ms | 8K |
Quantization options
How MPT-7B-Instruct (7B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B62 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 | 3.9 GB | Medium | B63 |
Q4_K_M | 4 | 4.3 GB | Medium | B64 |
Q5_K_M | 5 | 5.0 GB | High | B64 |
Q6_K | 6 | 5.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B66 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run MPT-7B-Instruct on your machine.
Run
lms load mpt-7b-instruct && lms server startアップグレードオプション
MPT-7B-Instruct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 (+680)
A
Raises estimated decode speed by about 427%.98 tok/sデコード
Raises estimated decode speed by about 427%.
〜$899 MSRP
Mac mini M4 32GBコスパ最良
32 GB Unified (+8)
B
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 (+153)
B
Raises estimated decode speed by about 144%.45.3 tok/sデコード
Raises estimated decode speed by about 144%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can MacBook Air M4 24GB run MPT-7B-Instruct?
Yes, MacBook Air M4 24GB can run MPT-7B-Instruct with a B grade (Tight fit). Expected decode speed: 18.6 tok/s.
How much VRAM does MPT-7B-Instruct need?
MPT-7B-Instruct (7B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
What is the best quantization for MPT-7B-Instruct?
The recommended quantization for MPT-7B-Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will MPT-7B-Instruct run at on MacBook Air M4 24GB?
On MacBook Air M4 24GB, MPT-7B-Instruct achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10400ms using Q4_K_M quantization.
Can MacBook Air M4 24GB run MPT-7B-Instruct for coding?
For coding workloads, MPT-7B-Instruct on MacBook Air M4 24GB receives a B grade with 18.6 tok/s and 8K context.
What context window can MPT-7B-Instruct use on MacBook Air M4 24GB?
On MacBook Air M4 24GB, MPT-7B-Instruct 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 M4 24GB as fast as VRAM for MPT-7B-Instruct?
Not always. MacBook Air M4 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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