Can StarCoder2 15B run on NVIDIA A16 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

StarCoder2 15B needs ~18.5 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 18.5 GB, 51.1 tok/s, Runs well

18.5 GB required64.0 GB available

29% VRAM used

Fit status

Runs well

Decode

51.1 tok/s

TTFT

3785 ms

Safe context

430K

Memory

18.5 GB / 64.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on NVIDIA A16 64GB
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: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 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	51.1 tok/s	2065 ms	430K
Coding	C	Runs well	51.1 tok/s	3785 ms	430K
Agentic Coding	C	Runs well	51.1 tok/s	5506 ms	430K
Reasoning	C	Runs well	51.1 tok/s	4473 ms	430K
RAG	C	Runs well	51.1 tok/s	6882 ms	430K

Inference speed

StarCoder2 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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	131.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	83.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	75.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	71.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	68.3	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	48.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	26.7	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	24.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	15.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.9	Too big

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 15B (15B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C41
Q3_K_S	3	7.4 GB	Low	C41
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C41
Q6_K	6	12.3 GB	High	C42
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C46

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 15B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.164.5 tok/s decode

Raises estimated decode speed by about 222%.

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

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 187%.146.6 tok/s decode

Raises estimated decode speed by about 187%.

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

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 311%.210 tok/s decode

Raises estimated decode speed by about 311%.

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

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run StarCoder2 15B?

Yes, NVIDIA A16 64GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 51.1 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, StarCoder2 15B achieves approximately 51.1 tokens per second decode speed with a time-to-first-token of 3785ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A16 64GB receives a C grade with 51.1 tok/s and 430K context.

What context window can StarCoder2 15B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, StarCoder2 15B can safely use up to 430K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for StarCoder2 15B

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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-15b-gguf-on-a16-64gb" 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

51.1 tok/s

2065 ms

430K

Coding

Runs well

51.1 tok/s

3785 ms

430K

Agentic Coding

Runs well

51.1 tok/s

5506 ms

430K

Reasoning

Runs well

51.1 tok/s

4473 ms

430K

RAG

Runs well

51.1 tok/s

6882 ms

430K

StarCoder2 15B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

131.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

83.7

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

75.5

Fits

RTX 3090 24GB

24 GB

Q4_K_M

71.6

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

68.3

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

60.9

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

50.7

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

48.1

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

34.7

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

34.7

Fits

RTX 4070 12GB

12 GB

Q4_K_M

26.7

Heavy offload

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

26.2

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

24.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

21.2

Fits

RTX 3060 12GB

12 GB

Q4_K_M

15.7

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

5.9

Too big

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C41
Q3_K_S	3	7.4 GB	Low	C41
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C41
Q6_K	6	12.3 GB	High	C42
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C46

Frequently asked questions

Can NVIDIA A16 64GB run StarCoder2 15B?

Yes, NVIDIA A16 64GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 51.1 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, StarCoder2 15B achieves approximately 51.1 tokens per second decode speed with a time-to-first-token of 3785ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A16 64GB receives a C grade with 51.1 tok/s and 430K context.

What context window can StarCoder2 15B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, StarCoder2 15B can safely use up to 430K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.