Can starcoder2 15b instruct v0.1 run on RTX 5000 Ada 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~15.3 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 15.3 GB, 50.4 tok/s, Runs well

15.3 GB required32.0 GB available

48% VRAM used

Fit status

Runs well

Decode

50.4 tok/s

TTFT

3844 ms

Safe context

168K

Memory

15.3 GB / 32.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 5000 Ada 32GB
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: 50.4 tok/s decode · 3.8s TTFT (warm) · 126 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	50.4 tok/s	2097 ms	168K
Coding	C	Runs well	50.4 tok/s	3844 ms	168K
Agentic Coding	C	Runs well	50.4 tok/s	5592 ms	168K
Reasoning	C	Runs well	50.4 tok/s	4543 ms	168K
RAG	C	Runs well	50.4 tok/s	6990 ms	168K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C44
Q3_K_S	3	7.4 GB	Low	C44
NVFP4	4	8.4 GB	Medium	C45
Q4_K_M	4	9.2 GB	Medium	C45
Q5_K_M	5	10.8 GB	High	C46
Q6_K	6	12.3 GB	High	C47
Q8_0Best for your GPU	8	16.1 GB	Very High	C49
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

NVIDIA A100 40GBBudget pick

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 183%.142.8 tok/s decode

Raises estimated decode speed by about 183%.

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

~$10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run starcoder2 15b instruct v0.1?

Yes, RTX 5000 Ada 32GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 50.4 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, starcoder2 15b instruct v0.1 achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3844ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 5000 Ada 32GB receives a C grade with 50.4 tok/s and 168K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, starcoder2 15b instruct v0.1 can safely use up to 168K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for starcoder2 15b instruct v0.1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

50.4 tok/s

2097 ms

168K

Coding

Runs well

50.4 tok/s

3844 ms

168K

Agentic Coding

Runs well

50.4 tok/s

5592 ms

168K

Reasoning

Runs well

50.4 tok/s

4543 ms

168K

RAG

Runs well

50.4 tok/s

6990 ms

168K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C44
Q3_K_S	3	7.4 GB	Low	C44
NVFP4	4	8.4 GB	Medium	C45
Q4_K_M	4	9.2 GB	Medium	C45
Q5_K_M	5	10.8 GB	High	C46
Q6_K	6	12.3 GB	High	C47
Q8_0Best for your GPU	8	16.1 GB	Very High	C49
F16	16	30.7 GB	Maximum	F0