Will It Run AI

Can starcoder2 15b instruct v0.1 run on RTX 5060 Ti 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

starcoder2 15b instruct v0.1 needs ~12.9 GB but RTX 5060 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.9 GB, exceeds 8.0 GB available
12.9 GB required8.0 GB available
161% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.7 tok/s

TTFT

22131 ms

Safe context

4K

Memory

12.9 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.2 GB
KV Cache1.8 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 instruct v0.1 on RTX 5060 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: 8.7 tok/s decode · 22.1s TTFT (warm) · 22 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.9 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy10.1 tok/s10454 ms4K
CodingFToo heavy8.7 tok/s22131 ms4K
Agentic CodingFToo heavy6.7 tok/s41768 ms4K
ReasoningFToo heavy8.7 tok/s26155 ms4K
RAGFToo heavy6.7 tok/s52209 ms4K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 5060 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

アップグレードオプション

starcoder2 15b instruct v0.1を快適に動かすハードウェア

NVIDIARTX 3060 12GBコスパ最良
12 GB VRAM (+4)
D
Makes the model fit on the accelerator instead of staying completely out of reach.15.7 tok/sデコード

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

Raises estimated decode speed by about 80%.

〜$329 MSRP

NVIDIARTX 5060 Ti 16GBコスパ重視
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.30.4 tok/sデコード

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

NVIDIARTX 4060 Ti 16GBNVIDIAアップグレード
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/sデコード

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

Frequently asked questions

Can RTX 5060 Ti 8GB run starcoder2 15b instruct v0.1?

No, starcoder2 15b instruct v0.1 requires more memory than RTX 5060 Ti 8GB provides.

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

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 12.9 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 5060 Ti 8GB?

On RTX 5060 Ti 8GB, starcoder2 15b instruct v0.1 achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22131ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 5060 Ti 8GB receives a F grade with 8.7 tok/s and 4K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, starcoder2 15b instruct v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on RTX 5060 Ti 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5060 Ti 8GBSee all hardware for starcoder2 15b instruct v0.1
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