Can StarCoder2 7B run on GTX 1650 4GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder2 7B needs ~6.4 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 6.4 GB, exceeds 4.0 GB available

6.4 GB required4.0 GB available

160% VRAM needed

2.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

44885 ms

Safe context

Memory

6.4 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 7B on GTX 1650 4GB
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: 4.3 tok/s decode · 44.9s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.4 GB, but this setup only exposes 4.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.7 tok/s	22466 ms	4K
Coding	F	Too heavy	4.3 tok/s	44885 ms	4K
Agentic Coding	F	Too heavy	3.7 tok/s	76805 ms	4K
Reasoning	F	Too heavy	4.3 tok/s	53046 ms	4K
RAG	F	Too heavy	3.7 tok/s	96007 ms	4K

Inference speed

StarCoder2 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	96.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.7	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	F0
Q3_K_S	3	3.4 GB	Low	F0
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

Opciones de mejora

Hardware que ejecuta bien StarCoder2 7B

GTX 1660 Super 6GBMejora NVIDIA

6 GB VRAM (+2)336 GB/s (+208)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 558%.

~$229 MSRP

RTX 3050 8GBOpción económica

8 GB VRAM (+4)224 GB/s (+96)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.37.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$249 MSRP

RTX 5060 8GBMejor relación calidad-precio

8 GB VRAM (+4)448 GB/s (+320)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.69.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$299 MSRP

Frequently asked questions

Can GTX 1650 4GB run StarCoder2 7B?

No, StarCoder2 7B requires more memory than GTX 1650 4GB provides.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.4 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 1650 4GB?

On GTX 1650 4GB, StarCoder2 7B achieves approximately 4.3 tokens per second decode speed with a time-to-first-token of 44885ms using Q4_K_M quantization.

Can GTX 1650 4GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on GTX 1650 4GB receives a F grade with 4.3 tok/s and 4K context.

What context window can StarCoder2 7B use on GTX 1650 4GB?

On GTX 1650 4GB, StarCoder2 7B can safely use up to 4K 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 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1650 4GB See all hardware for StarCoder2 7B

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