Can InternLM 20B run on MacBook Pro M4 Max 96GB?

YES — Runs Great

B60Good

Estimated from fit model

InternLM 20B needs ~44.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q5_K_M quantization, expect ~31 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

Q5_K_M (High quality) — 44.0 GB, 30.7 tok/s, Runs well

44.0 GB required69.1 GB available

64% VRAM used

Fit status

Runs well

Decode

30.7 tok/s

TTFT

6301 ms

Safe context

Memory

44.0 GB / 69.1 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsInternLM 20B on MacBook Pro M4 Max 96GB
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: 30.7 tok/s decode · 6.3s TTFT (warm) · 77 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	30.7 tok/s	3437 ms	8K
Coding	B	Runs well	30.7 tok/s	6301 ms	8K
Agentic Coding	B	Tight fit	30.7 tok/s	9165 ms	8K
Reasoning	B	Runs well	30.7 tok/s	7447 ms	8K
RAG	B	Tight fit	30.7 tok/s	11457 ms	8K

Quantization options

How InternLM 20B (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	C48
Q3_K_S	3	9.8 GB	Low	C49
NVFP4	4	11.2 GB	Medium	C49
Q4_K_M	4	12.2 GB	Medium	C49
Q5_K_M	5	14.4 GB	High	C50
Q6_K	6	16.4 GB	High	C50
Q8_0	8	21.4 GB	Very High	C51
F16Best for your GPU	16	41.0 GB	Maximum	B56

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

NVIDIA A100 80GBコスパ重視

2039 GB/s (+1493)

Raises estimated decode speed by about 295%.121.3 tok/sデコード

Raises estimated decode speed by about 295%.

〜$15,000 MSRP

NVIDIA A800 80GBコスパ最良

1935 GB/s (+1389)

Raises estimated decode speed by about 248%.106.9 tok/sデコード

Raises estimated decode speed by about 248%.

〜$15,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run InternLM 20B?

Yes, MacBook Pro M4 Max 96GB can run InternLM 20B with a B grade (Runs well). Expected decode speed: 30.7 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 44.0 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 M4 Max 96GB?

On MacBook Pro M4 Max 96GB, InternLM 20B achieves approximately 30.7 tokens per second decode speed with a time-to-first-token of 6301ms using Q5_K_M quantization.

Can MacBook Pro M4 Max 96GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on MacBook Pro M4 Max 96GB receives a B grade with 30.7 tok/s and 8K context.

What context window can InternLM 20B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, 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 M4 Max 96GB as fast as VRAM for InternLM 20B?

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.

See all results for MacBook Pro M4 Max 96GB See all hardware for InternLM 20B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/internlm-20b-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: