Can Ternary Bonsai 27B run on RTX 4090 Laptop 16GB?

YES — Tight Fit

S90Excellent

Estimated from fit model

Ternary Bonsai 27B needs ~14.1 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q2_0_G128 quantization, expect ~61 tok/s.

Runtime: LM StudioCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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

Q2_0_G128 (Low quality) — 11.2 GB, 65.8 tok/s, Runs well

11.2 GB required16.0 GB available

70% VRAM used

Fit status

Runs well

Decode

65.8 tok/s

TTFT

2943 ms

Safe context

95K

Memory

11.2 GB / 16.0 GB

Memory breakdown

Weights7.2 GB

KV Cache1.0 GB

Runtime1.4 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsTernary Bonsai 27B on RTX 4090 Laptop 16GB
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: 65.8 tok/s decode · 2.9s TTFT (warm) · 165 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	60.7 tok/s	1739 ms	24K
Coding	S	Tight fit	60.7 tok/s	3188 ms	24K
Agentic Coding	A	Very compromised	35.5 tok/s	7943 ms	24K
Reasoning	S	Tight fit	60.7 tok/s	3768 ms	24K
RAG	A	Very compromised	35.5 tok/s	9929 ms	24K

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 RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	3.9 GB	Very Low	S86
Q2_0_G128	1.71	7.2 GB	Low	S89
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 RTX 4090 Laptop 16GB run Ternary Bonsai 27B?

Yes, RTX 4090 Laptop 16GB can run Ternary Bonsai 27B with a S grade (Tight fit). Expected decode speed: 60.7 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 14.1 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 RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Ternary Bonsai 27B achieves approximately 60.7 tokens per second decode speed with a time-to-first-token of 3188ms using Q2_0_G128 quantization.

Can RTX 4090 Laptop 16GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on RTX 4090 Laptop 16GB receives a S grade with 60.7 tok/s and 24K context.

What context window can Ternary Bonsai 27B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Ternary Bonsai 27B can safely use up to 24K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for Ternary Bonsai 27B

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