Can StarCoder2 7B run on RTX 4050 Laptop 6GB?

YES — With Offload

C49Usable

Estimated from fit model

StarCoder2 7B needs ~6.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 6.3 GB, 22.6 tok/s, Runs with offload (needs ~0.2 GB host RAM)

6.3 GB required6.0 GB available

105% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

22.6 tok/s

TTFT

8557 ms

Safe context

Memory

6.3 GB / 6.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 4050 Laptop 6GB
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: 22.6 tok/s decode · 8.6s TTFT (warm) · 57 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0 GB host RAM)	24.6 tok/s	4292 ms	8K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	22.6 tok/s	8557 ms	8K
Agentic Coding	D	Very compromised (needs ~0.5 GB host RAM)	19.3 tok/s	14578 ms	8K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	22.6 tok/s	10112 ms	8K
RAG	D	Very compromised (needs ~0.5 GB host RAM)	19.3 tok/s	18223 ms	8K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

lms load starcoder2-7b && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)224 GB/s (+32)

Raises estimated decode speed by about 40%.31.7 tok/s decode

Raises estimated decode speed by about 40%.

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

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+256)

Raises estimated decode speed by about 209%.69.9 tok/s decode

Raises estimated decode speed by about 209%.

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

~$299 MSRP

RTX 5050 8GBNVIDIA upgrade

8 GB VRAM (+2)224 GB/s (+32)

Raises estimated decode speed by about 113%.48.1 tok/s decode

Raises estimated decode speed by about 113%.

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

~$299 MSRP

Frequently asked questions

Can RTX 4050 Laptop 6GB run StarCoder2 7B?

Yes, RTX 4050 Laptop 6GB can run StarCoder2 7B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 22.6 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.3 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 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, StarCoder2 7B achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8557ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 4050 Laptop 6GB receives a C grade with 22.6 tok/s and 8K context.

What context window can StarCoder2 7B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, StarCoder2 7B can safely use up to 8K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 7B feels slow on RTX 4050 Laptop 6GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4050 Laptop 6GB See all hardware for StarCoder2 7B

Embed this result▼

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Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0 GB host RAM)	24.6 tok/s	4292 ms	8K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	22.6 tok/s	8557 ms	8K
Agentic Coding	D	Very compromised (needs ~0.5 GB host RAM)	19.3 tok/s	14578 ms	8K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	22.6 tok/s	10112 ms	8K
RAG	D	Very compromised (needs ~0.5 GB host RAM)	19.3 tok/s	18223 ms	8K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C53

Q3_K_SBest for your GPU

3.4 GB

Low

C53

NVFP4

3.9 GB

Medium

Q4_K_M

4.3 GB

Medium

Q5_K_M

5.0 GB

High

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can RTX 4050 Laptop 6GB run StarCoder2 7B?

Yes, RTX 4050 Laptop 6GB can run StarCoder2 7B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 22.6 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.3 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 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, StarCoder2 7B achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8557ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 4050 Laptop 6GB receives a C grade with 22.6 tok/s and 8K context.

What context window can StarCoder2 7B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, StarCoder2 7B can safely use up to 8K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 7B feels slow on RTX 4050 Laptop 6GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.