Can Ternary Bonsai 27B run on Intel Arc B570 10GB?

YES — With Offload

S86Excellent

Estimated from fit model

Ternary Bonsai 27B needs ~10.1 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q2_0_G128 quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 10.1 GB, 16.7 tok/s, Runs with offload (needs ~0.1 GB host RAM)

10.1 GB required10.0 GB available

101% VRAM needed

100 MB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.1 GB host RAM)

Decode

16.7 tok/s

TTFT

11603 ms

Safe context

15K

Memory

10.1 GB / 10.0 GB

Memory breakdown

Weights7.2 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsTernary Bonsai 27B on Intel Arc B570 10GB
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: 16.7 tok/s decode · 11.6s TTFT (warm) · 42 tok/s prefill

What limits this setup

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

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	22.2 tok/s	4757 ms	15K
Coding	S	Runs with offload (needs ~0.1 GB host RAM)	16.7 tok/s	11603 ms	15K
Agentic Coding	A	Very compromised (needs ~0.7 GB host RAM)	13.8 tok/s	20379 ms	15K
Reasoning	S	Runs with offload (needs ~0.1 GB host RAM)	16.7 tok/s	13713 ms	15K
RAG	A	Very compromised (needs ~0.7 GB host RAM)	13.8 tok/s	25474 ms	15K

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 Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128Best for your GPU	1.125	3.9 GB	Very Low	S90
Q2_0_G128	1.71	7.2 GB	Low	F0
Q2_K	2	10.5 GB	Low	F0
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 Intel Arc B570 10GB run Ternary Bonsai 27B?

Yes, Intel Arc B570 10GB can run Ternary Bonsai 27B with a S grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 16.7 tok/s.

How much VRAM does Ternary Bonsai 27B need?

Ternary Bonsai 27B (27B parameters) requires approximately 10.1 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 Intel Arc B570 10GB?

On Intel Arc B570 10GB, Ternary Bonsai 27B achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11603ms using Q2_0_G128 quantization.

Can Intel Arc B570 10GB run Ternary Bonsai 27B for coding?

For coding workloads, Ternary Bonsai 27B on Intel Arc B570 10GB receives a S grade with 16.7 tok/s and 15K context.

What context window can Ternary Bonsai 27B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Ternary Bonsai 27B can safely use up to 15K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Ternary Bonsai 27B feels slow on Intel Arc B570 10GB?

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.

Would CUDA be a better path than Intel Arc B570 10GB 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.

See all results for Intel Arc B570 10GB See all hardware for Ternary Bonsai 27B

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