Can Granite Code 34B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A75Great

Estimated from fit model

Granite Code 34B needs ~38.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~105 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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

Q4_K_M (Medium quality) — 38.1 GB, 105.3 tok/s, Runs well

38.1 GB required128.0 GB available

30% VRAM used

Fit status

Runs well

Decode

105.3 tok/s

TTFT

1838 ms

Safe context

Memory

38.1 GB / 128.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsGranite Code 34B on Intel Data Center GPU Max 1550 128GB
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: 105.3 tok/s decode · 1.8s TTFT (warm) · 263 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	A	Runs well	105.3 tok/s	1003 ms	8K
Coding	A	Runs well	105.3 tok/s	1838 ms	8K
Agentic Coding	A	Runs well	105.3 tok/s	2674 ms	8K
Reasoning	A	Runs well	105.3 tok/s	2173 ms	8K
RAG	A	Runs well	105.3 tok/s	3343 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B66
Q3_K_S	3	16.7 GB	Low	B66
NVFP4	4	19.0 GB	Medium	B66
Q4_K_M	4	20.7 GB	Medium	B66
Q5_K_M	5	24.5 GB	High	B66
Q6_K	6	27.9 GB	High	B67
Q8_0	8	36.4 GB	Very High	B68
F16Best for your GPU	16	69.7 GB	Maximum	A74

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s
Qwen 3.6 35B A3B	35B	S	256.2 tok/s
Qwen 3.5 35B A3B	35B	S	278.6 tok/s
Mistral Small 4 119B	119B	S	87.9 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Granite Code 34B?

Yes, Intel Data Center GPU Max 1550 128GB can run Granite Code 34B with a A grade (Runs well). Expected decode speed: 105.3 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 38.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite Code 34B achieves approximately 105.3 tokens per second decode speed with a time-to-first-token of 1838ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on Intel Data Center GPU Max 1550 128GB receives a A grade with 105.3 tok/s and 8K context.

What context window can Granite Code 34B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite Code 34B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on Intel Data Center GPU Max 1550 128GB?

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 Data Center GPU Max 1550 128GB for Granite Code 34B?

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 Data Center GPU Max 1550 128GB See all hardware for Granite Code 34B

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