Can Ternary Bonsai 27B run on RTX 5080 16GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Ternary Bonsai 27B needs ~11.2 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q2_0_G128 quantization, expect ~89 tok/s.

Runtime: LM StudioCapacity: RoomyBandwidth: HighStack: 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, 89.1 tok/s, Runs well

11.2 GB required16.0 GB available

70% VRAM used

Fit status

Runs well

Decode

89.1 tok/s

TTFT

2173 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 5080 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: 89.1 tok/s decode · 2.2s TTFT (warm) · 223 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	89.1 tok/s	1185 ms	95K
Coding	S	Runs well	89.1 tok/s	2173 ms	95K
Agentic Coding	S	Runs well	89.1 tok/s	3161 ms	95K
Reasoning	S	Runs well	89.1 tok/s	2568 ms	95K
RAG	S	Runs well	89.1 tok/s	3951 ms	95K

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 5080 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 5080 16GB run Ternary Bonsai 27B?

Yes, RTX 5080 16GB can run Ternary Bonsai 27B with a S grade (Runs well). Expected decode speed: 89.1 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 11.2 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 5080 16GB?

On RTX 5080 16GB, Ternary Bonsai 27B achieves approximately 89.1 tokens per second decode speed with a time-to-first-token of 2173ms using Q2_0_G128 quantization.

Can RTX 5080 16GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on RTX 5080 16GB receives a S grade with 89.1 tok/s and 95K context.

What context window can Ternary Bonsai 27B use on RTX 5080 16GB?

On RTX 5080 16GB, Ternary Bonsai 27B can safely use up to 95K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 5080 16GB See all hardware for Ternary Bonsai 27B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/ternary-bonsai-27b-on-rtx-5080-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

89.1 tok/s

1185 ms

95K

Coding

Runs well

89.1 tok/s

2173 ms

95K

Agentic Coding

Runs well

89.1 tok/s

3161 ms

95K

Reasoning

Runs well

89.1 tok/s

2568 ms

95K

RAG

Runs well

89.1 tok/s

3951 ms

95K

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

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

Quantization options

How Ternary Bonsai 27B (27B params) fits at each quantization level on RTX 5080 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

Frequently asked questions

Can RTX 5080 16GB run Ternary Bonsai 27B?

Yes, RTX 5080 16GB can run Ternary Bonsai 27B with a S grade (Runs well). Expected decode speed: 89.1 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 11.2 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 5080 16GB?

On RTX 5080 16GB, Ternary Bonsai 27B achieves approximately 89.1 tokens per second decode speed with a time-to-first-token of 2173ms using Q2_0_G128 quantization.

Can RTX 5080 16GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on RTX 5080 16GB receives a S grade with 89.1 tok/s and 95K context.

What context window can Ternary Bonsai 27B use on RTX 5080 16GB?

On RTX 5080 16GB, Ternary Bonsai 27B can safely use up to 95K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.