Can Llama 3.2 3B run on RTX 2060 6GB?

YES — Tight Fit

B64Good

Estimated from fit model

Llama 3.2 3B needs ~5.3 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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) — 5.3 GB, 42.0 tok/s, Tight fit

5.3 GB required6.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

22K

Memory

5.3 GB / 6.0 GB

Memory breakdown

Weights1.8 GB

KV Cache1.7 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsLlama 3.2 3B on RTX 2060 6GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	42.0 tok/s	2514 ms	22K
Coding	B	Tight fit	42.0 tok/s	4610 ms	22K
Agentic Coding	C	Very compromised	42.0 tok/s	6705 ms	22K
Reasoning	B	Tight fit	42.0 tok/s	5448 ms	22K
RAG	C	Very compromised	42.0 tok/s	8381 ms	22K

Quantization options

How Llama 3.2 3B (3B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B66
Q3_K_S	3	1.5 GB	Low	B66
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 3B on your machine.

Run

ollama run llama3.2

Upgrade options

Hardware that runs Llama 3.2 3B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

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

~$249 MSRP

RTX 4060 8GBBest value

8 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

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

~$299 MSRP

RTX 5060 8GBNVIDIA upgrade

8 GB VRAM (+2)448 GB/s (+112)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

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

~$299 MSRP

Frequently asked questions

Can RTX 2060 6GB run Llama 3.2 3B?

Yes, RTX 2060 6GB can run Llama 3.2 3B with a B grade (Tight fit). Expected decode speed: 42.0 tok/s.

How much VRAM does Llama 3.2 3B need?

Llama 3.2 3B (3B parameters) requires approximately 5.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 3B?

The recommended quantization for Llama 3.2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 3B run at on RTX 2060 6GB?

On RTX 2060 6GB, Llama 3.2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can RTX 2060 6GB run Llama 3.2 3B for coding?

For coding workloads, Llama 3.2 3B on RTX 2060 6GB receives a B grade with 42.0 tok/s and 22K context.

What context window can Llama 3.2 3B use on RTX 2060 6GB?

On RTX 2060 6GB, Llama 3.2 3B can safely use up to 22K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 2060 6GB See all hardware for Llama 3.2 3B

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