Can starcoder2 15b instruct v0.1 run on RTX 4070 Super 12GB?

Q: What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on RTX 4070 Super 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D40Poor

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.0 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 13.0 GB, 27.5 tok/s, Very compromised (needs ~0.7 GB host RAM)

13.0 GB required12.0 GB available

108% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

27.5 tok/s

TTFT

7052 ms

Safe context

Memory

13.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 4070 Super 12GB
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: 27.5 tok/s decode · 7.1s TTFT (warm) · 69 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.1 GB host RAM)	31.8 tok/s	3320 ms	7K
Coding	D	Very compromised (needs ~0.7 GB host RAM)	27.5 tok/s	7052 ms	7K
Agentic Coding	F	Too heavy	21.0 tok/s	13394 ms	7K
Reasoning	D	Very compromised (needs ~0.7 GB host RAM)	27.5 tok/s	8334 ms	7K
RAG	F	Too heavy	21.0 tok/s	16743 ms

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C52
Q3_K_S	3	7.4 GB	Low	C51
NVFP4Best for your GPU

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.28.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$625 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run starcoder2 15b instruct v0.1?

Yes, RTX 4070 Super 12GB can run starcoder2 15b instruct v0.1 with a D grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 27.5 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.0 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, starcoder2 15b instruct v0.1 achieves approximately 27.5 tokens per second decode speed with a time-to-first-token of 7052ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 4070 Super 12GB receives a D grade with 27.5 tok/s and 7K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, starcoder2 15b instruct v0.1 can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GB See all hardware for starcoder2 15b instruct v0.1

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