Can StarCoder 7B run on RTX A4000 16GB?

YES — Tight Fit

A76Great

Estimated from fit model

StarCoder 7B needs ~14.4 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~73 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: 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) — 14.4 GB, 73.4 tok/s, Tight fit

14.4 GB required16.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

73.4 tok/s

TTFT

2636 ms

Safe context

Memory

14.4 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder 7B on RTX A4000 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: 73.4 tok/s decode · 2.6s TTFT (warm) · 184 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	A	Runs well	73.4 tok/s	1438 ms	8K
Coding	A	Tight fit	73.4 tok/s	2636 ms	8K
Agentic Coding	F	Too heavy	29.0 tok/s	9727 ms	8K
Reasoning	A	Tight fit	73.4 tok/s	3115 ms	8K
RAG	F	Too heavy	29.0 tok/s	12159 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A71
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A73
Q8_0Best for your GPU	8	7.5 GB	Very High	A75
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

lms load starcoder-7b && lms server start

Your hardware

More models your RTX A4000 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	61.4 tok/s
Qwen 3 14B	14B	S	39.7 tok/s
Qwen 3 8B	8B	S	69.1 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	37.6 tok/s
GPT-OSS 20B	21B	A	35 tok/s

Frequently asked questions

Can RTX A4000 16GB run StarCoder 7B?

Yes, RTX A4000 16GB can run StarCoder 7B with a A grade (Tight fit). Expected decode speed: 73.4 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder 7B run at on RTX A4000 16GB?

On RTX A4000 16GB, StarCoder 7B achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.

Can RTX A4000 16GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RTX A4000 16GB receives a A grade with 73.4 tok/s and 8K context.

What context window can StarCoder 7B use on RTX A4000 16GB?

On RTX A4000 16GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX A4000 16GB See all hardware for StarCoder 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starcoder-7b-on-a4000-16gb" 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

73.4 tok/s

1438 ms

Coding

Tight fit

73.4 tok/s

2636 ms

Agentic Coding

Too heavy

29.0 tok/s

9727 ms

Reasoning

Tight fit

73.4 tok/s

3115 ms

RAG

Too heavy

29.0 tok/s

12159 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A70

Q3_K_S

3.4 GB

Low

A71

NVFP4

3.9 GB

Medium

A71

Q4_K_M

4.3 GB

Medium

A71

Q5_K_M

5.0 GB

High

A72

Q6_K

5.7 GB

High

A73

Q8_0Best for your GPU

7.5 GB

Very High

A75

F16

14.3 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

61.4 tok/s

Qwen 3 14B

14B

39.7 tok/s

Qwen 3 8B

69.1 tok/s

Phi-4-reasoning-plus 14B

14.7B

37.6 tok/s

GPT-OSS 20B

21B

35 tok/s

Frequently asked questions

Can RTX A4000 16GB run StarCoder 7B?

Yes, RTX A4000 16GB can run StarCoder 7B with a A grade (Tight fit). Expected decode speed: 73.4 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder 7B run at on RTX A4000 16GB?

On RTX A4000 16GB, StarCoder 7B achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.

Can RTX A4000 16GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RTX A4000 16GB receives a A grade with 73.4 tok/s and 8K context.

What context window can StarCoder 7B use on RTX A4000 16GB?

On RTX A4000 16GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.