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URL: https://willitrunai.com/can-run/starcoder2-15b-on-rtx-3070-ti-8gb

⇱ StarCoder2 15B on RTX 3070 Ti 8GB? No — Alternatives

Can StarCoder2 15B run on RTX 3070 Ti 8GB?

YES — With Q2_K

C43Usable
○Estimated from fit model

StarCoder2 15B needs ~9.1 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q2_K quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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.

StarCoder2 15B at Q5_K_M needs 14.0 GB — too much for RTX 3070 Ti 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.
Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 14.0 GB, exceeds 8.0 GB available
14.0 GB required8.0 GB available
175% VRAM needed

6.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.4 tok/s

TTFT

18633 ms

Safe context

4K

Memory

14.0 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights10.8 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder2 15B on RTX 3070 Ti 8GB
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: 10.4 tok/s decode · 18.6s TTFT (warm) · 26 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy11.4 tok/s9255 ms4K
CodingFToo heavy10.4 tok/s18633 ms4K
Agentic CodingFToo heavy8.7 tok/s32310 ms4K
ReasoningFToo heavy10.4 tok/s22021 ms4K
RAGFToo heavy8.7 tok/s40387 ms4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowF0
Q3_K_S
3
7.4 GB
LowF0
NVFP4
4
8.4 GB
MediumF0
Q4_K_M
4
9.2 GB
MediumF0
Q5_K_M
5
10.8 GB
HighF0
Q6_K
6
12.3 GB
HighF0
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "bigcode/starcoder2-15b" \ --hf-file "starcoder2-15b-Q5_K_M.gguf" \ -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.28.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.21.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.22.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$625 MSRP

👁 NVIDIA
RTX 4090 24GBBiggest leap
24 GB VRAM (+16)1008 GB/s (+400)
B
Makes the model fit on the accelerator instead of staying completely out of reach.79 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,599 MSRP

Frequently asked questions

See all results for RTX 3070 Ti 8GBSee all hardware for StarCoder2 15B