Best Local AI Models for NVIDIA RTX 2060 (6 GB VRAM)

Q: What is the VRAM and memory bandwidth of the NVIDIA RTX 2060?

The NVIDIA RTX 2060 features 6 GB of VRAM and a memory bandwidth of 336 GB/s (GDDR6 192-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

Q: What is the best local AI model to run on a NVIDIA RTX 2060?

The best overall model for the NVIDIA RTX 2060 is Qwen/Qwen3-4B-GGUF using the recommended Q8_0 quantization (5.35 GB total memory). With 6 GB of VRAM, this GPU typically runs smaller models, typically up to about 3–4B at full GPU speed.

The NVIDIA RTX 2060 has 6 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.

Verdict: Same 6 GB and 336 GB/s as the GTX 1660 Super, so similar token generation, but its tensor cores speed up prompt processing in CUDA backends. 3B–8B models fit in VRAM in 4-bit quantization; 12B and larger spill into system RAM.

VRAM & Memory

6 GB

GDDR6 192-bit

Bandwidth

336 GB/s

Turing

Fits in VRAM

21 models

Zero offload

TDP / Assumed RAM

160W

RAM: 16.0 GB

At a glance — 21 of the 36 most popular models run fully in VRAM on the NVIDIA RTX 2060 (6 GB), with about 56 tokens/s estimated on an 8B model in Q4. A solid pick is Qwen/Qwen3-4B-GGUF (Q8_0, 5.35 GB, ~84.3 t/s estimated).

The NVIDIA RTX 2060 is built on NVIDIA's Turing architecture featuring GDDR6 192-bit delivering 336 GB/s of raw memory bandwidth. Same 6 GB and 336 GB/s as the GTX 1660 Super, so similar token generation, but its tensor cores speed up prompt processing in CUDA backends. 3B–8B models fit in VRAM in 4-bit quantization; 12B and larger spill into system RAM.

Equipped with 6 GB of dedicated VRAM, the NVIDIA RTX 2060 can run 21 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-4B-GGUF, Qwen2.5-7B-Instruct-GGUF, NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF.

With a memory bandwidth of 336 GB/s, this card can generate tokens at an estimated peak rate of ~56.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 160W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.

New to this? Read: How much VRAM do you need?

How the NVIDIA RTX 2060 compares with similar cards

Compared with the NVIDIA GTX 1660 Super, the NVIDIA RTX 2060 has the same 6 GB of VRAM, so the same models fit fully on the GPU; the same 336 GB/s of memory bandwidth, so the same estimated ~56 tokens/s on an 8B model in Q4 and a higher board power (160 vs 125 W). For local LLMs the two generate tokens at the same speed: the difference comes down to GPU compute, which mainly affects prompt processing. Side-by-side comparison
Compared with the NVIDIA GTX 1660 Ti, the NVIDIA RTX 2060 has the same 6 GB of VRAM, so the same models fit fully on the GPU; about 17% faster token generation (~56 vs ~48 tokens/s on an 8B model in Q4, from 336 vs 288 GB/s) and a higher board power (160 vs 120 W). Side-by-side comparison
Compared with the NVIDIA GTX 1660, the NVIDIA RTX 2060 has the same 6 GB of VRAM, so the same models fit fully on the GPU; about 75% faster token generation (~56 vs ~32 tokens/s on an 8B model in Q4, from 336 vs 192 GB/s) and a higher board power (160 vs 120 W). Side-by-side comparison

Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.

21 fit fully in VRAM · 15 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
Qwen/Qwen3-4B-GGUF	4.02B	Q8_0	Excellent	5.35 GB	84.3 t/s	Fits in VRAM
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	Q5_0	Very good	5.97 GB	67.9 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	Q4_K_XL	Very good	5.57 GB	71.3 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	GGUF	Excellent	4.23 GB	101.3 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	284.9 t/s	Fits in VRAM
unsloth/Ornith-1.0-9B-GGUF	—	Q4_K_S	Excellent	5.98 GB	66.5 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	Q5_0	Very good	5.97 GB	67.9 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	227.4 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	BF16	Excellent	4.08 GB	104.1 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	Q8_0	Excellent	5.73 GB	76.9 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	Q8_0	Excellent	4.31 GB	99.8 t/s	Fits in VRAM
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q4_K_M	Good	5.86 GB	73.3 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	Q8_K_XL	Excellent	5.15 GB	85.8 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	154.0 t/s	Fits in VRAM
lmstudio-community/gemma-3-1b-it-GGUF	1.0B	Q8_0	Excellent	1.96 GB	337.4 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	564.2 t/s	Fits in VRAM
bartowski/Qwen_Qwen3-0.6B-GGUF	0.75B	BF16	Excellent	2.64 GB	239.0 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	BF16	Excellent	4.45 GB	104.7 t/s	Fits in VRAM
LiquidAI/LFM2.5-350M-GGUF	0.35B	BF16	Excellent	1.51 GB	507.1 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	Q4_K_S	Good	5.83 GB	75.1 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	190.4 t/s	Fits in VRAM
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	6.2 t/s	Offload
unsloth/gpt-oss-20b-GGUF	20.91B	F16	Very good	13.74 GB	3.9 t/s	Offload
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	Q4_K_L	Good	20.83 GB	2.6 t/s	Offload
unsloth/LFM2.5-24B-A2B-GGUF	23.97B	Q6_K_XL	Excellent	21.24 GB	2.5 t/s	Offload
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q5_K_XL	Very good	21.42 GB	2.5 t/s	Offload
Qwen/Qwen3-14B-GGUF	14.77B	Q8_0	Excellent	16.05 GB	3.4 t/s	Offload
unsloth/flint-GGUF	39.79B	Q3_K_XL	Fair	18.13 GB	2.9 t/s	Offload
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q5_K_XL	Very good	21.21 GB	2.5 t/s	Offload
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	Q4_1	Very good	21.34 GB	2.4 t/s	Offload
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q4_0	Good	18.42 GB	2.8 t/s	Offload
bartowski/Llama-3.3-70B-Instruct-GGUF	70.55B	IQ2_XS	Very low	21.91 GB	2.5 t/s	Offload
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	3.4 t/s	Offload
unsloth/Qwen3-Coder-Next-GGUF	79.67B	IQ1_M	Very low	21.39 GB	2.5 t/s	Offload
bartowski/Nanbeige_Nanbeige4.3-33B-GGUF	33.12B	Q4_1	Very good	21.36 GB	2.5 t/s	Offload
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	3.4 t/s	Offload

"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 2060's 336 GB/s memory bandwidth (offloaded models are limited by system RAM).

Frequently asked questions

What is the VRAM and memory bandwidth of the NVIDIA RTX 2060?

The NVIDIA RTX 2060 features 6 GB of VRAM and a memory bandwidth of 336 GB/s (GDDR6 192-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

What is the best local AI model to run on a NVIDIA RTX 2060?

The best overall model for the NVIDIA RTX 2060 is Qwen/Qwen3-4B-GGUF using the recommended Q8_0 quantization (5.35 GB total memory). With 6 GB of VRAM, this GPU typically runs smaller models, typically up to about 3–4B at full GPU speed.

What models can you run on an 8 GB GPU like the NVIDIA RTX 2060?

With 8 GB of VRAM, the NVIDIA RTX 2060 comfortably runs 7B and 8B models (such as Llama 3.1 8B, Mistral 7B, or Qwen 2.5 7B) using Q4_K_M or Q5_K_M quantizations (~5.5 to 6.8 GB). Running 14B models requires offloading memory to system RAM, which lowers token generation speed.