MacBook Pro M4 Max 36GBコスパ重視
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 117%.65.9 tok/sデコード
Raises estimated decode speed by about 117%.
〜$2,499 MSRP
Can internlm2 math plus 7b IMat run on MacBook Pro M1 Pro 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
internlm2 math plus 7b IMat needs ~9.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.4 GB, 30.4 tok/s, Runs well
9.4 GB required23.0 GB available
41% VRAM used
Fit status
Runs well
Decode
30.4 tok/s
TTFT
6359 ms
Safe context
281K
Memory
9.4 GB / 23.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsinternlm2 math plus 7b IMat on MacBook Pro M1 Pro 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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 | C | Runs well | 30.4 tok/s | 3469 ms | 281K |
| Coding | C | Runs well | 30.4 tok/s | 6359 ms | 281K |
| Agentic Coding | C | Runs well | 30.4 tok/s | 9249 ms | 281K |
| Reasoning | C | Runs well | 30.4 tok/s | 7515 ms | 281K |
| RAG | C | Runs well | 30.4 tok/s | 11562 ms | 281K |
Quantization options
How internlm2 math plus 7b IMat (7B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C47 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run internlm2 math plus 7b IMat on your machine.
Run
lms load hf-legraphista--internlm2-math-plus-7b-imat-gguf && lms server startアップグレードオプション
internlm2 math plus 7b IMatを快適に動かすハードウェア
MacBook Pro M4 Max 48GBコスパ最良
48 GB Unified (+16)546 GB/s (+346)
C
Raises estimated decode speed by about 189%.87.8 tok/sデコード
Raises estimated decode speed by about 189%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
MacBook Pro M3 Max 48GBAppleアップグレード
48 GB Unified (+16)400 GB/s (+200)
C
Raises estimated decode speed by about 85%.56.2 tok/sデコード
Raises estimated decode speed by about 85%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run internlm2 math plus 7b IMat?
Yes, MacBook Pro M1 Pro 32GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 30.4 tok/s.
How much VRAM does internlm2 math plus 7b IMat need?
internlm2 math plus 7b IMat (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 math plus 7b IMat?
The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 math plus 7b IMat run at on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, internlm2 math plus 7b IMat achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6359ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run internlm2 math plus 7b IMat for coding?
For coding workloads, internlm2 math plus 7b IMat on MacBook Pro M1 Pro 32GB receives a C grade with 30.4 tok/s and 281K context.
What context window can internlm2 math plus 7b IMat use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, internlm2 math plus 7b IMat can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for internlm2 math plus 7b IMat?
Not always. MacBook Pro M1 Pro 32GB 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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