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Run LLM Locally

Qwen3 0.6B on the RTX 5060

With 8 GB of VRAM, the RTX 5060 runs Qwen3 0.6B at Q4_K_M entirely on the GPU with an 8K context (about 2.4 GB needed). At Q8_0 it fits entirely.

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Qwen3 0.6B

Q4_K_M · 8K context

Excellent

The model needs about 2.4 GB and fits in the 7.7 GB available, with 5.3 GB to spare.

Your hardware
RTX 5060 8 GB · 64 GB RAM
Estimated memory
~2.4 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
226–368 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (448 GB/s) and model size

Memory breakdown

~2.4 GB (2.1 GB–2.7 GB) needed · estimate

Fits entirely in VRAM · 5.3 GB left

  • Model weights0.5 GB
  • KV cache0.9 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 5060

Qwen3 0.6B on RTX 5060 + 64 GB RAM · 8K context
Qwen3 0.6B Q8_08K~2.5 GB · 205–326 tok/s estimateExcellent
Qwen3 0.6B Q6_K8K~2.5 GB · 207–330 tok/s estimateExcellent
Qwen3 0.6B Q5_K_M8K~2.4 GB · 216–349 tok/s estimateExcellent
Qwen3 0.6B Q4_K_M8K~2.4 GB · 226–368 tok/s estimateExcellent
Qwen3 0.6B IQ4_XS8K~2.3 GB · 231–379 tok/s estimateExcellent
Qwen3 0.6B Q3_K_M8K~2.3 GB · 236–391 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 0.6B Q4_K_M entirely in VRAM

Other models that fit the RTX 5060

Where to buy a RTX 5060

Prices and links follow the market you choose.

GeForce RTX 5060

8 GB GDDR7 · 448 GB/s · 145 W

ExcellentRuns Qwen3 0.6B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 5060 run Qwen3 0.6B?
The model needs about 2.4 GB and fits in the 7.7 GB available, with 5.3 GB to spare.
Which quantization should I use on the RTX 5060?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~2.5 GB / 7.7 GB.
What is the largest context on the RTX 5060?
At Q4_K_M, about 32K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 0.6B (RTX 5060)?
We estimate 226–368 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size.