Will It Run AI

Can DeepSeek Coder V2 16B run on GTX 1080 Ti 11GB?

YES — With Q2_K

A71Great
Estimated from fit model

DeepSeek Coder V2 16B needs ~11.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~58 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.

DeepSeek Coder V2 16B at Q4_K_M needs 15.4 GB — too much for GTX 1080 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

24.4 tok/s

TTFT

7927 ms

Safe context

4K

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 GTX 1080 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: 24.4 tok/s decode · 7.9s TTFT (warm) · 61 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy31.3 tok/s3370 ms4K
CodingFToo heavy24.4 tok/s7927 ms4K
Agentic CodingFToo heavy15.9 tok/s17671 ms4K
ReasoningFToo heavy24.4 tok/s9368 ms4K
RAGFToo heavy15.9 tok/s22089 ms4K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
6.2 GB
LowA81
Q3_K_SBest for your GPU
3
7.8 GB
LowA80
NVFP4
4
9.0 GB
MediumF0
Q4_K_M
4
9.8 GB
MediumF0
Q5_K_M
5
11.5 GB
HighF0
Q6_K
6
13.1 GB
HighF0
Q8_0
8
17.1 GB
Very HighF0
F16
16
32.8 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien DeepSeek Coder V2 16B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+5)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.67.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+5)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.51.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBMejora NVIDIA
16 GB VRAM (+5)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.53.4 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$625 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run DeepSeek Coder V2 16B?

Yes, GTX 1080 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: 57.5 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 GTX 1080 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 GTX 1080 Ti 11GB the best fitting quantization is Q2_K, which uses 11.8 GB.

What speed will DeepSeek Coder V2 16B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, DeepSeek Coder V2 16B achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3366ms using Q2_K quantization.

Can GTX 1080 Ti 11GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on GTX 1080 Ti 11GB receives a F grade with 24.4 tok/s and 4K context.

What context window can DeepSeek Coder V2 16B use on GTX 1080 Ti 11GB?

On GTX 1080 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 GTX 1080 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 GTX 1080 Ti 11GBSee all hardware for DeepSeek Coder V2 16B
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