Can Granite Code 34B run on NVIDIA H20 96GB?

YES — Runs Great

A77Great

Estimated from fit model

Granite Code 34B needs ~35.2 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~169 tok/s.

Runtime: OllamaCapacity: 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

Q4_K_M (Medium quality) — 35.2 GB, 169.2 tok/s, Runs well

35.2 GB required96.0 GB available

37% VRAM used

Fit status

Runs well

Decode

169.2 tok/s

TTFT

1144 ms

Safe context

Memory

35.2 GB / 96.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsGranite Code 34B on NVIDIA H20 96GB
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: 169.2 tok/s decode · 1.1s TTFT (warm) · 423 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	A	Runs well	169.2 tok/s	624 ms	8K
Coding	A	Runs well	169.2 tok/s	1144 ms	8K
Agentic Coding	A	Runs well	169.2 tok/s	1664 ms	8K
Reasoning	A	Runs well	169.2 tok/s	1352 ms	8K
RAG	A	Runs well	169.2 tok/s	2080 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B67
Q3_K_S	3	16.7 GB	Low	B67
NVFP4	4

Get started

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

Run

ollama run granite-code:34b

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can NVIDIA H20 96GB run Granite Code 34B?

Yes, NVIDIA H20 96GB can run Granite Code 34B with a A grade (Runs well). Expected decode speed: 169.2 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 35.2 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 NVIDIA H20 96GB?

On NVIDIA H20 96GB, Granite Code 34B achieves approximately 169.2 tokens per second decode speed with a time-to-first-token of 1144ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on NVIDIA H20 96GB receives a A grade with 169.2 tok/s and 8K context.

What context window can Granite Code 34B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, 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.

See all results for NVIDIA H20 96GB See all hardware for Granite Code 34B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/granite-code-34b-on-h20-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: