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Run LLM Locally

Qwen3 4B on the RTX 4060 Ti 8 GB

With 8 GB of VRAM, the RTX 4060 Ti 8 GB runs Qwen3 4B at Q4_K_M entirely on the GPU with an 8K context (about 4.5 GB needed). At Q8_0 it fits entirely.

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Qwen3 4B

Q4_K_M · 8K context

Excellent

The model needs about 4.5 GB and fits in the 7.7 GB available, with 3.1 GB to spare.

Your hardware
RTX 4060 Ti 8 GB · 64 GB RAM
Estimated memory
~4.5 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
62–86 tok/s estimate
Largest context in fast memory
29K tokens
Confidence: low

Based on: memory bandwidth (288 GB/s) and model size

Memory breakdown

~4.5 GB (4.2 GB–4.9 GB) needed · estimate

Fits entirely in VRAM · 3.1 GB left

  • Model weights2.3 GB
  • KV cache1.1 GB
  • Runtime overhead0.8 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RTX 4060 Ti 8 GB

Qwen3 4B on RTX 4060 Ti 8 GB + 64 GB RAM · 8K context
Qwen3 4B Q8_08K~6.2 GB · 41–56 tok/s estimateExcellent
Qwen3 4B Q6_K8K~5.3 GB · 51–69 tok/s estimateExcellent
Qwen3 4B Q5_K_M8K~4.9 GB · 56–77 tok/s estimateExcellent
Qwen3 4B Q4_K_M8K~4.5 GB · 62–86 tok/s estimateExcellent
Qwen3 4B IQ4_XS8K~4.3 GB · 66–92 tok/s estimateExcellent
Qwen3 4B Q3_K_M8K~4.1 GB · 70–99 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 4B Q4_K_M entirely in VRAM

Other models that fit the RTX 4060 Ti 8 GB

Where to buy a RTX 4060 Ti 8 GB

Prices and links follow the market you choose.

GeForce RTX 4060 Ti 8 GB

8 GB GDDR6 · 288 GB/s · 160 W

ExcellentRuns Qwen3 4B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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Frequently asked questions

Can the RTX 4060 Ti 8 GB run Qwen3 4B?
The model needs about 4.5 GB and fits in the 7.7 GB available, with 3.1 GB to spare.
Which quantization should I use on the RTX 4060 Ti 8 GB?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~6.2 GB / 7.7 GB.
What is the largest context on the RTX 4060 Ti 8 GB?
At Q4_K_M, about 29K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 4B (RTX 4060 Ti 8 GB)?
We estimate 62–86 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size.