Will It Run AI

Can DeepSeek LLM 7B run on GTX 1080 Ti 11GB?

YES — With Q3_K_S

D33Poor
Estimated from fit model

DeepSeek LLM 7B needs ~13.1 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~39 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 LLM 7B at Q4_K_M needs 13.9 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q3_K_S (13.1 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.9 GB, exceeds 11.0 GB available
13.9 GB required11.0 GB available
126% VRAM needed

2.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

29.2 tok/s

TTFT

6629 ms

Safe context

4K

Memory

13.9 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB
KV Cache7.3 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 LLM 7B on GTX 1080 Ti 11GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 29.2 tok/s decode · 6.6s TTFT (warm) · 73 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 20% 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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit66.9 tok/s1579 ms4K
CodingFToo heavy29.2 tok/s6629 ms4K
Agentic CodingFToo heavy11.5 tok/s24432 ms4K
ReasoningFToo heavy29.2 tok/s7834 ms4K
RAGFToo heavy11.5 tok/s30540 ms4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC48
Q3_K_S
3
3.4 GB
LowC49
NVFP4
4
3.9 GB
MediumC50
Q4_K_M
4
4.3 GB
MediumC50
Q5_K_M
5
5.0 GB
HighC51
Q6_K
6
5.7 GB
HighC51
Q8_0Best for your GPU
8
7.5 GB
Very HighC50
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run DeepSeek LLM 7B on your machine.

Run

ollama run deepseek-llm

Upgrade-Optionen

Hardware, die DeepSeek LLM 7B gut ausführt

NVIDIARTX 3060 12GBBestes Preis-Leistungs-Verhältnis
12 GB VRAM (+1)
D
Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/s Dekodierung

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

ca. $329 MSRP

NVIDIARTX 5060 Ti 16GBGünstige Wahl
16 GB VRAM (+5)
C
Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/s Dekodierung

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.

ca. $449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA-Upgrade
16 GB VRAM (+5)
C
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/s Dekodierung

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.

ca. $499 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run DeepSeek LLM 7B?

Yes, GTX 1080 Ti 11GB can run DeepSeek LLM 7B at Q3_K_S quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 13.9 GB which exceeds available memory, but at Q3_K_S it needs only 13.1 GB. Expected decode speed: 38.8 tok/s.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 13.9 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q3_K_S using 13.1 GB.

What is the best quantization for DeepSeek LLM 7B?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 13.1 GB.

What speed will DeepSeek LLM 7B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, DeepSeek LLM 7B achieves approximately 38.8 tokens per second decode speed with a time-to-first-token of 4993ms using Q3_K_S quantization.

Can GTX 1080 Ti 11GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on GTX 1080 Ti 11GB receives a F grade with 29.2 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, DeepSeek LLM 7B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek LLM 7B 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 LLM 7B
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