Can StarCoder2 3B run on RTX 4080 Super 16GB?

YES — Runs Great

C45Usable

Estimated from fit model

StarCoder2 3B needs ~4.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 4.8 GB, 48.0 tok/s, Runs well

4.8 GB required16.0 GB available

30% VRAM used

Fit status

Runs well

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

16K

Memory

4.8 GB / 16.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 3B on RTX 4080 Super 16GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 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	48.0 tok/s	2200 ms	16K
Coding	C	Runs well	48.0 tok/s	4033 ms	16K
Agentic Coding	C	Runs well	48.0 tok/s	5867 ms	16K
Reasoning	C	Runs well	48.0 tok/s	4767 ms	16K
RAG	C	Runs well	48.0 tok/s	7333 ms	16K

Inference speed

StarCoder2 3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 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	57.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	48.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	48.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	42.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	42.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	42.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	42.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	42.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	42.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	42.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.0	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 3B (3B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C44
Q3_K_S	3	1.5 GB	Low	C44
NVFP4	4	1.7 GB	Medium	C44
Q4_K_M	4	1.8 GB	Medium	C45
Q5_K_M	5	2.2 GB	High	C45
Q6_K	6	2.5 GB	High	C45
Q8_0	8	3.2 GB	Very High	C46
F16Best for your GPU	16	6.1 GB	Maximum	C48

Get started

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

Run

ollama run starcoder2:3b

Upgrade options

Hardware that runs StarCoder2 3B well

MacBook Air M4 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.42 tok/s decode

~$1,099 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.40.4 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can RTX 4080 Super 16GB run StarCoder2 3B?

Yes, RTX 4080 Super 16GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 48.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 4.8 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 4080 Super 16GB?

On RTX 4080 Super 16GB, StarCoder2 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on RTX 4080 Super 16GB receives a C grade with 48.0 tok/s and 16K context.

What context window can StarCoder2 3B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, StarCoder2 3B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 4080 Super 16GB See all hardware for StarCoder2 3B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starcoder2-3b-on-rtx-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

48.0 tok/s

2200 ms

16K

Coding

Runs well

48.0 tok/s

4033 ms

16K

Agentic Coding

Runs well

48.0 tok/s

5867 ms

16K

Reasoning

Runs well

48.0 tok/s

4767 ms

16K

RAG

Runs well

48.0 tok/s

7333 ms

16K

StarCoder2 3B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

57.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

48.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

48.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

42.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

42.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

42.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

42.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

42.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

42.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

42.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

42.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

42.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

42.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

42.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

42.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

42.0

Fits

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C44
Q3_K_S	3	1.5 GB	Low	C44
NVFP4	4	1.7 GB	Medium	C44
Q4_K_M	4	1.8 GB	Medium	C45
Q5_K_M	5	2.2 GB	High	C45
Q6_K	6	2.5 GB	High	C45
Q8_0	8	3.2 GB	Very High	C46
F16Best for your GPU	16	6.1 GB	Maximum	C48

Frequently asked questions

Can RTX 4080 Super 16GB run StarCoder2 3B?

Yes, RTX 4080 Super 16GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 48.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 4.8 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 4080 Super 16GB?

On RTX 4080 Super 16GB, StarCoder2 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on RTX 4080 Super 16GB receives a C grade with 48.0 tok/s and 16K context.

What context window can StarCoder2 3B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, StarCoder2 3B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.