Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for internlm2 5 20b chat?

Not always. MacBook Pro M4 Max 96GB 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 5 20b chat run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C46Usable

Estimated from fit model

internlm2 5 20b chat needs ~25.8 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 25.8 GB, 35.6 tok/s, Runs well

25.8 GB required69.1 GB available

37% VRAM used

Fit status

Runs well

Decode

35.6 tok/s

TTFT

5445 ms

Safe context

312K

Memory

25.8 GB / 69.1 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on MacBook Pro M4 Max 96GB
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: 35.6 tok/s decode · 5.4s TTFT (warm) · 89 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	35.6 tok/s	2970 ms	312K
Coding	C	Runs well	35.6 tok/s	5445 ms	312K
Agentic Coding	C	Runs well	35.6 tok/s	7920 ms	312K
Reasoning	C	Runs well	35.6 tok/s	6435 ms	312K
RAG	C	Runs well	35.6 tok/s	9900 ms	312K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C40
Q3_K_S	3	9.8 GB	Low	C40
NVFP4	4

Get started

Copy-paste commands to run internlm2 5 20b chat on your machine.

Run

lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server start

Upgrade options

Hardware that runs internlm2 5 20b chat well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+1246)

Raises estimated decode speed by about 247%.123.4 tok/s decode

Raises estimated decode speed by about 247%.

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

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+1051)

Raises estimated decode speed by about 209%.110 tok/s decode

Raises estimated decode speed by about 209%.

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

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run internlm2 5 20b chat?

Yes, MacBook Pro M4 Max 96GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 35.6 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 25.8 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 5 20b chat?

The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 5 20b chat run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, internlm2 5 20b chat achieves approximately 35.6 tokens per second decode speed with a time-to-first-token of 5445ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on MacBook Pro M4 Max 96GB receives a C grade with 35.6 tok/s and 312K context.

What context window can internlm2 5 20b chat use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, internlm2 5 20b chat can safely use up to 312K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 96GB See all hardware for internlm2 5 20b chat

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