Can InternLM 20B run on Mac mini M4 64GB?

YES — Tight Fit

C54Usable

Estimated — low-sample bucket· few comparable runs

InternLM 20B needs ~40.5 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q5_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 40.5 GB, 8.0 tok/s, Tight fit

40.5 GB required46.1 GB available

88% VRAM used

Fit status

Tight fit

Decode

8.0 tok/s

TTFT

24334 ms

Safe context

Memory

40.5 GB / 46.1 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsInternLM 20B on Mac mini M4 64GB
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: 8.0 tok/s decode · 24.3s TTFT (warm) · 20 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	8.0 tok/s	13273 ms	8K
Coding	C	Tight fit	8.0 tok/s	24334 ms	8K
Agentic Coding	F	Too heavy	5.6 tok/s	50156 ms	8K
Reasoning	C	Tight fit	8.0 tok/s	28758 ms	8K
RAG	F	Too heavy	5.6 tok/s	62695 ms	8K

Inference speed

InternLM 20B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for InternLM 20B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~45 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	44.5	Heavy offload
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	39.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	32.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	31.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	30.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	30.7	Tight
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	17.0	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	16.1	Heavy offload
RX 7900 XTX 24GB	24 GB	Q5_K_M	15.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	15.6	Tight
RTX 4090 24GB	24 GB	Q5_K_M	14.0	Too big
RTX 3090 24GB	24 GB	Q5_K_M	12.9	Too big
RTX 4080 Super 16GB	16 GB	Q5_K_M	5.3	Too big
RTX 4070 12GB	12 GB	Q5_K_M	3.3	Too big
RTX 3060 12GB	12 GB	Q5_K_M	2.2	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.0	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How InternLM 20B (20B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C50
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4	11.2 GB	Medium	C51
Q4_K_M	4	12.2 GB	Medium	C52
Q5_K_M	5	14.4 GB	High	C52
Q6_K	6	16.4 GB	High	C53
Q8_0Best for your GPU	8	21.4 GB	Very High	C55
F16	16	41.0 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien InternLM 20B

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+32)546 GB/s (+426)

Sube la velocidad estimada de decodificación alrededor de un 284%.30.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 284%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

MacBook Pro M3 Max 128GBMejor relación calidad-precio

128 GB Unified (+64)400 GB/s (+280)

Sube la velocidad estimada de decodificación alrededor de un 113%.17 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 113%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejora Apple

96 GB Unified (+32)819 GB/s (+699)

Sube la velocidad estimada de decodificación alrededor de un 393%.39.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 393%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run InternLM 20B?

Yes, Mac mini M4 64GB can run InternLM 20B with a C grade (Tight fit). Expected decode speed: 8.0 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 40.5 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 Mac mini M4 64GB?

On Mac mini M4 64GB, InternLM 20B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24334ms using Q5_K_M quantization.

Can Mac mini M4 64GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on Mac mini M4 64GB receives a C grade with 8.0 tok/s and 8K context.

What context window can InternLM 20B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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 Mac mini M4 64GB as fast as VRAM for InternLM 20B?

Not always. Mac mini M4 64GB 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 Mac mini M4 64GB See all hardware for InternLM 20B

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