Can StarCoder2 7B run on GTX 1660 Ti 6GB?

Q: What should I upgrade first if StarCoder2 7B feels slow on GTX 1660 Ti 6GB?

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

YES — With Offload

C49Usable

Estimated from fit model

StarCoder2 7B needs ~6.3 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 6.3 GB, 26.9 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

26.9 tok/s

TTFT

7190 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 GTX 1660 Ti 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: 26.9 tok/s decode · 7.2s TTFT (warm) · 67 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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)	29.4 tok/s	3594 ms	8K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	26.9 tok/s	7190 ms	8K
Agentic Coding	D	Very compromised (needs ~0.5 GB host RAM)	22.8 tok/s	12334 ms	8K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	26.9 tok/s	8497 ms	8K
RAG	D	Very compromised (needs ~0.5 GB host RAM)	22.8 tok/s

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on GTX 1660 Ti 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

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)

Adds memory headroom for longer context windows and future model growth.31.7 tok/s decode

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

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+160)

Raises estimated decode speed by about 160%.69.9 tok/s decode

Raises estimated decode speed by about 160%.

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

~$299 MSRP

RTX 5050 8GBNVIDIA upgrade

8 GB VRAM (+2)

Raises estimated decode speed by about 79%.48.1 tok/s decode

Raises estimated decode speed by about 79%.

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

~$299 MSRP

Frequently asked questions

Can GTX 1660 Ti 6GB run StarCoder2 7B?

Yes, GTX 1660 Ti 6GB can run StarCoder2 7B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 26.9 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 GTX 1660 Ti 6GB?

On GTX 1660 Ti 6GB, StarCoder2 7B achieves approximately 26.9 tokens per second decode speed with a time-to-first-token of 7190ms using Q4_K_M quantization.

Can GTX 1660 Ti 6GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on GTX 1660 Ti 6GB receives a C grade with 26.9 tok/s and 8K context.

What context window can StarCoder2 7B use on GTX 1660 Ti 6GB?

On GTX 1660 Ti 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 GTX 1660 Ti 6GB?

See all results for GTX 1660 Ti 6GB See all hardware for StarCoder2 7B

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