Can InternLM 20B run on MacBook Pro M3 Pro 36GB?

YES — With Q2_K

D38Poor

○Estimated from fit model

InternLM 20B needs ~30.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q2_K quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

InternLM 20B at Q5_K_M needs 37.5 GB — too much for MacBook Pro M3 Pro 36GB (25.9 GB). Runs at Q2_K (30.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 37.5 GB, exceeds 25.9 GB available

37.5 GB required25.9 GB available

145% VRAM needed

11.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.7 tok/s

TTFT

40958 ms

Safe context

Memory

37.5 GB / 25.9 GB

Offload

30%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsInternLM 20B on MacBook Pro M3 Pro 36GB
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: 4.7 tok/s decode · 41.0s TTFT (warm) · 12 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Very compromised (needs ~1.2 GB host RAM)	6.7 tok/s	15828 ms	6K
Coding	F	Too heavy	4.7 tok/s	40958 ms	6K
Agentic Coding	F	Too heavy	3.5 tok/s	80680 ms	6K
Reasoning	F	Too heavy	4.7 tok/s	48405 ms	6K
RAG	F	Too heavy	3.5 tok/s	100851 ms	6K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C55
Q3_K_S	3	9.8 GB	Low	B56
NVFP4	4

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

Upgrade options

Hardware that runs InternLM 20B well

Mac mini M4 64GBBudget pick

64 GB Unified (+28)

Makes the model fit on the accelerator instead of staying completely out of reach.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+28)273 GB/s (+123)

Makes the model fit on the accelerator instead of staying completely out of reach.19.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

MacBook Pro M4 Max 96GBApple upgrade

96 GB Unified (+60)546 GB/s (+396)

Makes the model fit on the accelerator instead of staying completely out of reach.30.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

Frequently asked questions

See all results for MacBook Pro M3 Pro 36GB See all hardware for InternLM 20B

URL: https://willitrunai.com/can-run/internlm-20b-on-m3-pro-36gb

⇱

Can InternLM 20B run on MacBook Pro M3 Pro 36GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs InternLM 20B well

Frequently asked questions