Would CUDA be a better path than Intel Arc Pro B50 16GB for Ternary Bonsai 27B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Ternary Bonsai 27B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Ternary Bonsai 27B feels slow on Intel Arc Pro B50 16GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — Tight Fit

A85Great

Estimated from fit model

Ternary Bonsai 27B needs ~13.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q2_0_G128 quantization, expect ~12 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) — 10.7 GB, 13.1 tok/s, Runs well

10.7 GB required16.0 GB available

67% VRAM used

Fit status

Runs well

Decode

13.1 tok/s

TTFT

14793 ms

Safe context

103K

Memory

10.7 GB / 16.0 GB

Memory breakdown

Weights7.2 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsTernary Bonsai 27B on Intel Arc Pro B50 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: 13.1 tok/s decode · 14.8s TTFT (warm) · 33 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	12.1 tok/s	8741 ms	26K
Coding	A	Tight fit	12.1 tok/s	16026 ms	26K
Agentic Coding	A	Very compromised	7.7 tok/s	36689 ms	26K
Reasoning	A	Tight fit	12.1 tok/s	18940 ms	26K
RAG	A	Very compromised	7.7 tok/s	45862 ms	26K

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

Quantization options

How Ternary Bonsai 27B (27B params) fits at each quantization level on Intel Arc Pro B50 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

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 Intel Arc Pro B50 16GB run Ternary Bonsai 27B?

Yes, Intel Arc Pro B50 16GB can run Ternary Bonsai 27B with a A grade (Tight fit). Expected decode speed: 12.1 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 13.6 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 Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Ternary Bonsai 27B achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 16026ms using Q2_0_G128 quantization.

Can Intel Arc Pro B50 16GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on Intel Arc Pro B50 16GB receives a A grade with 12.1 tok/s and 26K context.

What context window can Ternary Bonsai 27B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Ternary Bonsai 27B can safely use up to 26K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for Ternary Bonsai 27B

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