Can DeepSeek LLM 7B run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if DeepSeek LLM 7B feels slow on RTX 4000 Ada Laptop 12GB?

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.

BARELY — Tight on Memory

D40Poor

Estimated from fit model

DeepSeek LLM 7B needs ~14.0 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.0 GB, 40.1 tok/s, Very compromised (needs ~0.6 GB host RAM)

14.0 GB required12.0 GB available

117% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.6 GB host RAM)

Decode

40.1 tok/s

TTFT

4831 ms

Safe context

Memory

14.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 7B on RTX 4000 Ada Laptop 12GB
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: 40.1 tok/s decode · 4.8s TTFT (warm) · 100 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.

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 0.6 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	73.9 tok/s	1430 ms	4K
Coding	D	Very compromised (needs ~0.6 GB host RAM)	40.1 tok/s	4831 ms	4K
Agentic Coding	F	Too heavy	16.5 tok/s	17043 ms	4K
Reasoning	D	Very compromised (needs ~0.6 GB host RAM)	40.1 tok/s	5709 ms	4K
RAG	F	Too heavy	16.5 tok/s	21304 ms

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C47
Q3_K_S	3	3.4 GB	Low	C48
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 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.65 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 62%.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.49.2 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)896 GB/s (+464)

Removes host-memory offload, which is usually the single biggest latency and throughput win.98 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 144%.

~$749 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run DeepSeek LLM 7B?

Yes, RTX 4000 Ada Laptop 12GB can run DeepSeek LLM 7B with a D grade (Very compromised (needs ~0.6 GB host RAM)). Expected decode speed: 40.1 tok/s.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 7B?

The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 7B run at on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, DeepSeek LLM 7B achieves approximately 40.1 tokens per second decode speed with a time-to-first-token of 4831ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on RTX 4000 Ada Laptop 12GB receives a D grade with 40.1 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. 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 RTX 4000 Ada Laptop 12GB?

See all results for RTX 4000 Ada Laptop 12GB See all hardware for DeepSeek LLM 7B

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