Can StarCoder2 3B run on RTX 5050 8GB?

YES — Runs Great

C51Usable

Estimated from fit model

StarCoder2 3B needs ~3.9 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 3.9 GB, 57.0 tok/s, Runs well

3.9 GB required8.0 GB available

49% VRAM used

Fit status

Runs well

Decode

57.0 tok/s

TTFT

3396 ms

Safe context

203K

Memory

3.9 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsStarCoder2 3B on RTX 5050 8GB
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: 57.0 tok/s decode · 3.4s TTFT (warm) · 143 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	57.0 tok/s	1853 ms	203K
Coding	C	Runs well	57.0 tok/s	3396 ms	203K
Agentic Coding	C	Runs well	57.0 tok/s	4940 ms	203K
Reasoning	C	Runs well	57.0 tok/s	4014 ms	203K
RAG	C	Runs well	57.0 tok/s	6175 ms	203K

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C50
Q3_K_S	3	1.5 GB	Low	C51
NVFP4	4	1.7 GB	Medium	C51
Q4_K_M	4	1.8 GB	Medium	C51
Q5_K_M	5	2.2 GB	High	C52
Q6_K	6	2.5 GB	High	C53
Q8_0Best for your GPU	8	3.2 GB	Very High	C54
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 3B on your machine.

Run

lms load hf-second-state--starcoder2-3b-gguf && lms server start

Frequently asked questions

Can RTX 5050 8GB run StarCoder2 3B?

Yes, RTX 5050 8GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 57.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 3.9 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on RTX 5050 8GB?

On RTX 5050 8GB, StarCoder2 3B achieves approximately 57.0 tokens per second decode speed with a time-to-first-token of 3396ms using Q4_K_M quantization.

Can RTX 5050 8GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on RTX 5050 8GB receives a C grade with 57.0 tok/s and 203K context.

What context window can StarCoder2 3B use on RTX 5050 8GB?

On RTX 5050 8GB, StarCoder2 3B can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5050 8GB See all hardware for StarCoder2 3B

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