Can DeepSeek LLM 7B run on RTX 2080 Ti 11GB?

Q: What should I upgrade first if DeepSeek LLM 7B 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.

YES — With Q3_K_S

D35Poor

Estimated from fit model

DeepSeek LLM 7B needs ~13.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~54 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 LLM 7B at Q4_K_M needs 13.9 GB — too much for RTX 2080 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

41.0 tok/s

TTFT

4728 ms

Safe context

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 RTX 2080 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: 41.0 tok/s decode · 4.7s TTFT (warm) · 102 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	93.8 tok/s	1126 ms	4K
Coding	F	Too heavy	41.0 tok/s	4728 ms	4K
Agentic Coding	F	Too heavy	16.2 tok/s	17424 ms	4K
Reasoning	F	Too heavy	41.0 tok/s	5587 ms	4K
RAG	F	Too heavy	16.2 tok/s	21781 ms	4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C48
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4

Get started

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

Run

ollama run deepseek-llm

Upgrade options

Hardware that runs DeepSeek LLM 7B well

RTX 3060 12GBBest value

12 GB VRAM (+1)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/s decode

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

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.65 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 16GBNVIDIA upgrade

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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

Frequently asked questions

Can RTX 2080 Ti 11GB run DeepSeek LLM 7B?

Yes, RTX 2080 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: 54.4 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 RTX 2080 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 RTX 2080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 13.1 GB.

What speed will DeepSeek LLM 7B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, DeepSeek LLM 7B achieves approximately 54.4 tokens per second decode speed with a time-to-first-token of 3561ms using Q3_K_S quantization.

Can RTX 2080 Ti 11GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on RTX 2080 Ti 11GB receives a F grade with 41.0 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on RTX 2080 Ti 11GB?

On RTX 2080 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.

See all results for RTX 2080 Ti 11GB See all hardware for DeepSeek LLM 7B

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