Can StarCoder 7B run on RTX 4070 12GB?

BARELY — Tight on Memory

B65Good

○Estimated from fit model

StarCoder 7B needs ~14.0 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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) — 14.0 GB, 48.0 tok/s, Very compromised (needs ~0.6 GB host RAM)

14.0 GB required12.0 GB available

117% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.6 GB host RAM)

Decode

48.0 tok/s

TTFT

4030 ms

Safe context

Memory

14.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder 7B on RTX 4070 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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 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 {ram} 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	A	Tight fit	88.5 tok/s	1193 ms	8K
Coding	B	Very compromised	48.0 tok/s	4030 ms	8K
Agentic Coding	F	Too heavy	19.8 tok/s	14219 ms	8K
Reasoning	B	Very compromised	48.0 tok/s	4763 ms	8K
RAG	F	Too heavy	19.8 tok/s	17773 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A72
Q3_K_S	3	3.4 GB	Low	A73
NVFP4	4

Get started

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

Run

lms load starcoder-7b && lms server start

Upgrade options

Hardware that runs StarCoder 7B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.65 tok/s decode

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

Raises estimated decode speed by about 35%.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.49.2 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

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.51.3 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

See all results for RTX 4070 12GB See all hardware for StarCoder 7B

URL: https://willitrunai.com/can-run/starcoder-7b-on-rtx-4070-12gb

⇱

Can StarCoder 7B run on RTX 4070 12GB?

BARELY — Tight on Memory

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs StarCoder 7B well

Frequently asked questions