Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for internlm2 math plus 7b IMat?

Not always. MacBook Pro M3 Max 128GB 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.

Can internlm2 math plus 7b IMat run on MacBook Pro M3 Max 128GB?

YES — Runs Great

C44Usable

Estimated from fit model

internlm2 math plus 7b IMat needs ~19.8 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.8 GB, 56.2 tok/s, Runs well

19.8 GB required92.2 GB available

21% VRAM used

Fit status

Runs well

Decode

56.2 tok/s

TTFT

3444 ms

Safe context

1.4M

Memory

19.8 GB / 92.2 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 7b IMat on MacBook Pro M3 Max 128GB
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: 56.2 tok/s decode · 3.4s TTFT (warm) · 141 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	56.2 tok/s	1879 ms	1.4M
Coding	C	Runs well	56.2 tok/s	3444 ms	1.4M
Agentic Coding	C	Runs well	56.2 tok/s	5010 ms	1.4M
Reasoning	C	Runs well	56.2 tok/s	4071 ms	1.4M
RAG	C	Runs well	56.2 tok/s	6263 ms	1.4M

Quantization options

How internlm2 math plus 7b IMat (7B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4

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

Upgrade options

Hardware that runs internlm2 math plus 7b IMat well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 74%.98 tok/s decode

Raises estimated decode speed by about 74%.

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBBest value

1792 GB/s (+1392)

Raises estimated decode speed by about 74%.98 tok/s decode

Raises estimated decode speed by about 74%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run internlm2 math plus 7b IMat?

Yes, MacBook Pro M3 Max 128GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 56.2 tok/s.

How much VRAM does internlm2 math plus 7b IMat need?

internlm2 math plus 7b IMat (7B parameters) requires approximately 19.8 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 M3 Max 128GB?

On MacBook Pro M3 Max 128GB, internlm2 math plus 7b IMat achieves approximately 56.2 tokens per second decode speed with a time-to-first-token of 3444ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run internlm2 math plus 7b IMat for coding?

For coding workloads, internlm2 math plus 7b IMat on MacBook Pro M3 Max 128GB receives a C grade with 56.2 tok/s and 1.4M context.

What context window can internlm2 math plus 7b IMat use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, internlm2 math plus 7b IMat can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 128GB See all hardware for internlm2 math plus 7b IMat

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