Can DeepSeek Coder V2 16B run on RTX 3060 Ti 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek Coder V2 16B needs ~15.1 GB but RTX 3060 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

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

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) — 15.1 GB, exceeds 8.0 GB available

15.1 GB required8.0 GB available

189% VRAM needed

7.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.7 tok/s

TTFT

13147 ms

Safe context

Memory

15.1 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek Coder V2 16B on RTX 3060 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: 14.7 tok/s decode · 13.1s TTFT (warm) · 37 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 15.1 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	18.8 tok/s	5618 ms	4K
Coding	F	Too heavy	14.7 tok/s	13147 ms	4K
Agentic Coding	F	Too heavy	11.1 tok/s	25261 ms	4K
Reasoning	F	Too heavy	14.7 tok/s	15537 ms	4K
RAG	F	Too heavy	11.1 tok/s	31576 ms	4K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	F0
Q3_K_S	3	7.8 GB	Low	F0
NVFP4	4	9.0 GB	Medium	F0
Q4_K_M	4	9.8 GB	Medium	F0
Q5_K_M	5	11.5 GB	High	F0
Q6_K	6	13.1 GB	High	F0
Q8_0	8	17.1 GB	Very High	F0
F16	16	32.8 GB	Maximum	F0

アップグレードオプション

DeepSeek Coder V2 16Bを快適に動かすハードウェア

RTX 5060 Ti 16GBコスパ重視

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.67.8 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

RTX 4060 Ti 16GBコスパ最良

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.51.3 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

RTX 2000 Ada 16GBNVIDIAアップグレード

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.53.4 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.

〜$625 MSRP

Frequently asked questions

Can RTX 3060 Ti 8GB run DeepSeek Coder V2 16B?

No, DeepSeek Coder V2 16B requires more memory than RTX 3060 Ti 8GB provides.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek Coder V2 16B achieves approximately 14.7 tokens per second decode speed with a time-to-first-token of 13147ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RTX 3060 Ti 8GB receives a F grade with 14.7 tok/s and 4K context.

What context window can DeepSeek Coder V2 16B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek Coder V2 16B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 16B feels slow on RTX 3060 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 3060 Ti 8GB See all hardware for DeepSeek Coder V2 16B

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