Will It Run AI

Can DeepSeek LLM 7B run on RTX 4070 Super 12GB?

BARELY — Tight on Memory

C40Usable
Estimated from fit model

DeepSeek LLM 7B needs ~14.0 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~49 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.0 GB, 49.3 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

49.3 tok/s

TTFT

3926 ms

Safe context

4K

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 4070 Super 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: 49.3 tok/s decode · 3.9s TTFT (warm) · 123 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit90.9 tok/s1162 ms4K
CodingCVery compromised (needs ~0.6 GB host RAM)49.3 tok/s3926 ms4K
Agentic CodingFToo heavy20.3 tok/s13849 ms4K
ReasoningCVery compromised (needs ~0.6 GB host RAM)49.3 tok/s4639 ms4K
RAGFToo heavy20.3 tok/s17312 ms4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC47
Q3_K_S
3
3.4 GB
LowC48
NVFP4
4
3.9 GB
MediumC49
Q4_K_M
4
4.3 GB
MediumC49
Q5_K_M
5
5.0 GB
HighC50
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

Opciones de mejora

Hardware que ejecuta bien DeepSeek LLM 7B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.65 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 32%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.49.2 tok/s decodificación

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

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBMejora NVIDIA
16 GB VRAM (+4)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.51.3 tok/s decodificación

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

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$625 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run DeepSeek LLM 7B?

Yes, RTX 4070 Super 12GB can run DeepSeek LLM 7B with a C grade (Very compromised (needs ~0.6 GB host RAM)). Expected decode speed: 49.3 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 4070 Super 12GB?

On RTX 4070 Super 12GB, DeepSeek LLM 7B achieves approximately 49.3 tokens per second decode speed with a time-to-first-token of 3926ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on RTX 4070 Super 12GB receives a C grade with 49.3 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on RTX 4070 Super 12GB?

On RTX 4070 Super 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 4070 Super 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.

See all results for RTX 4070 Super 12GBSee all hardware for DeepSeek LLM 7B
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