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Run LLM Locally

Qwen3 0.6B on the RTX 4080

With 16 GB of VRAM, the RTX 4080 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 15.5 GB available, with 13.1 GB to spare.

Your hardware
RTX 4080 16 GB · 64 GB RAM
Estimated memory
~2.4 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
294–524 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (717 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 13.1 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 4080

Qwen3 0.6B on RTX 4080 + 64 GB RAM · 8K context
Qwen3 0.6B Q8_08K~2.5 GB · 272–470 tok/s estimateExcellent
Qwen3 0.6B Q6_K8K~2.5 GB · 274–476 tok/s estimateExcellent
Qwen3 0.6B Q5_K_M8K~2.4 GB · 284–499 tok/s estimateExcellent
Qwen3 0.6B Q4_K_M8K~2.4 GB · 294–524 tok/s estimateExcellent
Qwen3 0.6B IQ4_XS8K~2.3 GB · 299–538 tok/s estimateExcellent
Qwen3 0.6B Q3_K_M8K~2.3 GB · 305–553 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 0.6B Q4_K_M entirely in VRAM

Other models that fit the RTX 4080

Where to buy a RTX 4080

Prices and links follow the market you choose.

GeForce RTX 4080

16 GB GDDR6X · 716.8 GB/s · 320 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 4080 run Qwen3 0.6B?
The model needs about 2.4 GB and fits in the 15.5 GB available, with 13.1 GB to spare.
Which quantization should I use on the RTX 4080?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~2.5 GB / 15.5 GB.
What is the largest context on the RTX 4080?
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 4080)?
We estimate 294–524 tok/s (estimate, confidence low). Based on: memory bandwidth (717 GB/s) and model size.