20 GB VRAM (+4)360 GB/s (+72)
C
Raises estimated decode speed by about 34%.65.8 tok/s decode
Raises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can DeepSeek LLM 7B run on RTX 4060 Ti 16GB?
YES — Tight Fit
C50Usable
○Estimated from fit model
DeepSeek LLM 7B needs ~14.4 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.4 GB, 49.2 tok/s, Tight fit
14.4 GB required16.0 GB available
90% VRAM used
Fit status
Tight fit
Decode
49.2 tok/s
TTFT
3932 ms
Safe context
4K
Memory
14.4 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 7B on RTX 4060 Ti 16GB
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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 49.2 tok/s | 2145 ms | 4K |
| Coding | C | Tight fit | 49.2 tok/s | 3932 ms | 4K |
| Agentic Coding | F | Too heavy | 19.4 tok/s | 14509 ms | 4K |
| Reasoning | C | Tight fit | 49.2 tok/s | 4647 ms | 4K |
| RAG | F | Too heavy | 19.4 tok/s | 18136 ms | 4K |
Quantization options
How DeepSeek LLM 7B (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C45 |
Q3_K_S | 3 | 3.4 GB | Low | C46 |
NVFP4 | 4 |
Get started
Copy-paste commands to run DeepSeek LLM 7B on your machine.
Run
ollama run deepseek-llmUpgrade options
Hardware that runs DeepSeek LLM 7B well
24 GB VRAM (+8)936 GB/s (+648)
C
Raises estimated decode speed by about 99%.98 tok/s decode
Raises estimated decode speed by about 99%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)1008 GB/s (+720)
C
Raises estimated decode speed by about 99%.98 tok/s decode
Raises estimated decode speed by about 99%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP