Can Ternary Bonsai 27B run on MacBook Air M4 24GB?

YES — Tight Fit

A84Great

Estimated — low-sample bucket· few comparable runs

Ternary Bonsai 27B needs ~14.6 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q2_0_G128 quantization, expect ~9 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

Q2_0_G128 (Low quality) — 11.7 GB, 15.4 tok/s, Runs well

11.7 GB required17.3 GB available

68% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12552 ms

Safe context

108K

Memory

11.7 GB / 17.3 GB

Memory breakdown

Weights7.2 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsTernary Bonsai 27B on MacBook Air M4 24GB
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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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	S	Runs well	8.6 tok/s	12238 ms	27K
Coding	A	Tight fit	8.6 tok/s	22436 ms	27K
Agentic Coding	A	Runs with offload	7.6 tok/s	36856 ms	27K
Reasoning	A	Tight fit	8.6 tok/s	26516 ms	27K
RAG	A	Runs with offload	7.6 tok/s	46070 ms	27K

Inference speed

Ternary Bonsai 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ternary Bonsai 27B at Q2_0_G128 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 4080 Super 16GB at ~87 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 4080 Super 16GB	16 GB	Q2_0_G128	87.2	Fits
RTX 5090 32GB	32 GB	Q2_0_G128	76.2	Fits
RX 7900 XTX 24GB	24 GB	Q2_0_G128	64.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q2_0_G128	60.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q2_0_G128	59.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q2_0_G128	59.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q2_0_G128	50.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q2_0_G128	47.6	Fits
RTX 4090 24GB	24 GB	Q2_0_G128	43.8	Fits
RTX 3090 24GB	24 GB	Q2_0_G128	42.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q2_0_G128	37.5	Fits
RTX 4070 12GB	12 GB	Q2_0_G128	26.0	Tight
MacBook Pro M3 Max 64GB	64 GB	Q2_0_G128	26.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q2_0_G128	23.8	Fits
RTX 3060 12GB	12 GB	Q2_0_G128	17.5	Tight
RTX 4060 8GB	8 GB	Q2_0_G128	6.6	Too big

Estimates for single-stream decoding at Q2_0_G128; 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 Ternary Bonsai 27B (27B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	3.9 GB	Very Low	S85
Q2_0_G128	1.71	7.2 GB	Low	S88
Q2_KBest for your GPU	2	10.5 GB	Low	S88
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Ternary Bonsai 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "prism-ml/Ternary-Bonsai-27B-gguf" \
  --hf-file "Ternary-Bonsai-27B-gguf-Q2_0_G128.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can MacBook Air M4 24GB run Ternary Bonsai 27B?

Yes, MacBook Air M4 24GB can run Ternary Bonsai 27B with a A grade (Tight fit). Expected decode speed: 8.6 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 14.6 GB of memory with Q2_0_G128 quantization.

What is the best quantization for Ternary Bonsai 27B?

The recommended quantization for Ternary Bonsai 27B is Q2_0_G128, which balances quality and memory efficiency.

What speed will Ternary Bonsai 27B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Ternary Bonsai 27B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22436ms using Q2_0_G128 quantization.

Can MacBook Air M4 24GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on MacBook Air M4 24GB receives a A grade with 8.6 tok/s and 27K context.

What context window can Ternary Bonsai 27B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Ternary Bonsai 27B can safely use up to 27K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M4 24GB as fast as VRAM for Ternary Bonsai 27B?

Not always. MacBook Air M4 24GB 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 Air M4 24GB See all hardware for Ternary Bonsai 27B

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