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Run LLM Locally

Qwen3 4B on the RTX 3080

With 10 GB of VRAM, the RTX 3080 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 9.7 GB available, with 5.1 GB to spare.

Your hardware
RTX 3080 10 GB · 64 GB RAM
Estimated memory
~4.5 GB estimate
VRAM (usable memory)
9.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
134–206 tok/s estimate
Largest context in fast memory
43K tokens
Confidence: low

Based on: memory bandwidth (760 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 5.1 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 3080

Qwen3 4B on RTX 3080 + 64 GB RAM · 8K context
Qwen3 4B Q8_08K~6.2 GB · 95–138 tok/s estimateExcellent
Qwen3 4B Q6_K8K~5.3 GB · 113–169 tok/s estimateExcellent
Qwen3 4B Q5_K_M8K~4.9 GB · 123–186 tok/s estimateExcellent
Qwen3 4B Q4_K_M8K~4.5 GB · 134–206 tok/s estimateExcellent
Qwen3 4B IQ4_XS8K~4.3 GB · 142–220 tok/s estimateExcellent
Qwen3 4B Q3_K_M8K~4.1 GB · 149–233 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 4B Q4_K_M entirely in VRAM

Other models that fit the RTX 3080

Where to buy a RTX 3080

Prices and links follow the market you choose.

GeForce RTX 3080 (10 GB)

10 GB GDDR6X · 760 GB/s · 320 W

ExcellentRuns Qwen3 4B Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 3080 run Qwen3 4B?
The model needs about 4.5 GB and fits in the 9.7 GB available, with 5.1 GB to spare.
Which quantization should I use on the RTX 3080?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~6.2 GB / 9.7 GB.
What is the largest context on the RTX 3080?
At Q4_K_M, about 43K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 4B (RTX 3080)?
We estimate 134–206 tok/s (estimate, confidence low). Based on: memory bandwidth (760 GB/s) and model size.