Can InternLM 20B run on MacBook Pro M3 Max 128GB?

YES — Runs Great

B55Good

Estimated from fit model

InternLM 20B needs ~47.4 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q5_K_M quantization, expect ~17 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

Q5_K_M (High quality) — 47.4 GB, 17.0 tok/s, Runs well

47.4 GB required92.2 GB available

51% VRAM used

Fit status

Runs well

Decode

17.0 tok/s

TTFT

11388 ms

Safe context

Memory

47.4 GB / 92.2 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsInternLM 20B on MacBook Pro M3 Max 128GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 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	17.0 tok/s	6212 ms	8K
Coding	B	Runs well	17.0 tok/s	11388 ms	8K
Agentic Coding	B	Runs well	17.0 tok/s	16565 ms	8K
Reasoning	B	Runs well	17.0 tok/s	13459 ms	8K
RAG	B	Runs well	17.0 tok/s	20706 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C47
Q3_K_S	3	9.8 GB	Low	C47
NVFP4	4	11.2 GB	Medium	C48
Q4_K_M	4	12.2 GB	Medium	C48
Q5_K_M	5	14.4 GB	High	C48
Q6_K	6	16.4 GB	High	C48
Q8_0	8	21.4 GB	Very High	C49
F16Best for your GPU	16	41.0 GB	Maximum	C53

Get started

Copy-paste commands to run InternLM 20B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "internlm/internlm2_5-20b-chat" \
  --hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

InternLM 20Bを快適に動かすハードウェア

RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視

1792 GB/s (+1392)

Raises estimated decode speed by about 527%.106.6 tok/sデコード

Raises estimated decode speed by about 527%.

〜$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良

1597 GB/s (+1197)

Raises estimated decode speed by about 459%.95 tok/sデコード

Raises estimated decode speed by about 459%.

〜$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run InternLM 20B?

Yes, MacBook Pro M3 Max 128GB can run InternLM 20B with a B grade (Runs well). Expected decode speed: 17.0 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 47.4 GB of memory with Q5_K_M quantization.

What is the best quantization for InternLM 20B?

The recommended quantization for InternLM 20B is Q5_K_M, which balances quality and memory efficiency.

What speed will InternLM 20B run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, InternLM 20B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11388ms using Q5_K_M quantization.

Can MacBook Pro M3 Max 128GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on MacBook Pro M3 Max 128GB receives a B grade with 17.0 tok/s and 8K context.

What context window can InternLM 20B use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, InternLM 20B 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 Pro M3 Max 128GB as fast as VRAM for InternLM 20B?

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.

See all results for MacBook Pro M3 Max 128GB See all hardware for InternLM 20B

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