Can StarCoder2 7B run on RTX 6000 Ada 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

StarCoder2 7B needs ~11.1 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 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) — 11.1 GB, 98.0 tok/s, Runs well

11.1 GB required48.0 GB available

23% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

736K

Memory

11.1 GB / 48.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 6000 Ada 48GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	C	Runs well	98.0 tok/s	1078 ms	736K
Coding	C	Runs well	98.0 tok/s	1976 ms	736K
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	736K
Reasoning	C	Runs well	98.0 tok/s	2335 ms	736K
RAG	C	Runs well	98.0 tok/s	3592 ms	736K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.87.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.98 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can RTX 6000 Ada 48GB run StarCoder2 7B?

Yes, RTX 6000 Ada 48GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, StarCoder2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 6000 Ada 48GB receives a C grade with 98.0 tok/s and 736K context.

What context window can StarCoder2 7B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, StarCoder2 7B can safely use up to 736K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for StarCoder2 7B

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