Can DeepSeek Coder V2 16B run on RTX 2080 Ti 11GB?

YES — With Q2_K

A72Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~11.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~81 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.

DeepSeek Coder V2 16B at Q4_K_M needs 15.4 GB — too much for RTX 2080 Ti 11GB (11.0 GB). Runs at Q2_K (11.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.4 GB, exceeds 11.0 GB available

15.4 GB required11.0 GB available

140% VRAM needed

4.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

34.2 tok/s

TTFT

5653 ms

Safe context

Memory

15.4 GB / 11.0 GB

Offload

30%

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek Coder V2 16B on RTX 2080 Ti 11GB
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: 34.2 tok/s decode · 5.7s TTFT (warm) · 86 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 0.4 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	F	Too heavy	43.9 tok/s	2403 ms	4K
Coding	F	Too heavy	34.2 tok/s	5653 ms	4K
Agentic Coding	F	Too heavy	22.3 tok/s	12603 ms	4K
Reasoning	F	Too heavy	34.2 tok/s	6681 ms	4K
RAG	F	Too heavy	22.3 tok/s	15754 ms	4K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A81
Q3_K_SBest for your GPU	3	7.8 GB	Low	A80
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

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Upgrade options

Hardware that runs DeepSeek Coder V2 16B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

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

RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

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

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+5)

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

Frequently asked questions

Can RTX 2080 Ti 11GB run DeepSeek Coder V2 16B?

Yes, RTX 2080 Ti 11GB can run DeepSeek Coder V2 16B at Q2_K quantization (Runs with offload (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 15.4 GB which exceeds available memory, but at Q2_K it needs only 11.8 GB. Expected decode speed: 80.6 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.4 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q2_K using 11.8 GB.

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

The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q2_K, which uses 11.8 GB.

What speed will DeepSeek Coder V2 16B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, DeepSeek Coder V2 16B achieves approximately 80.6 tokens per second decode speed with a time-to-first-token of 2401ms using Q2_K quantization.

Can RTX 2080 Ti 11GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RTX 2080 Ti 11GB receives a F grade with 34.2 tok/s and 4K context.

What context window can DeepSeek Coder V2 16B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, DeepSeek Coder V2 16B can safely use up to 12K tokens of context at Q2_K quantization. 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 2080 Ti 11GB?

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.

See all results for RTX 2080 Ti 11GB See all hardware for DeepSeek Coder V2 16B

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Preview:

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

43.9 tok/s

2403 ms

Coding

Too heavy

34.2 tok/s

5653 ms

Agentic Coding

Too heavy

22.3 tok/s

12603 ms

Reasoning

Too heavy

34.2 tok/s

6681 ms

RAG

Too heavy

22.3 tok/s

15754 ms

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A81
Q3_K_SBest for your GPU	3	7.8 GB	Low	A80
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

Frequently asked questions

Can RTX 2080 Ti 11GB run DeepSeek Coder V2 16B?

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.4 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q2_K using 11.8 GB.

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

The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q2_K, which uses 11.8 GB.

What speed will DeepSeek Coder V2 16B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, DeepSeek Coder V2 16B achieves approximately 80.6 tokens per second decode speed with a time-to-first-token of 2401ms using Q2_K quantization.

Can RTX 2080 Ti 11GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RTX 2080 Ti 11GB receives a F grade with 34.2 tok/s and 4K context.

What context window can DeepSeek Coder V2 16B use on RTX 2080 Ti 11GB?

What should I upgrade first if DeepSeek Coder V2 16B feels slow on RTX 2080 Ti 11GB?

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.