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Run LLM Locally

Qwen3 8B on the RTX 4080

With 16 GB of VRAM, the RTX 4080 runs Qwen3 8B at Q4_K_M entirely on the GPU with an 8K context (about 6.9 GB needed). At Q8_0 it fits entirely.

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Qwen3 8B

Q4_K_M · 8K context

Excellent

The model needs about 6.9 GB and fits in the 15.5 GB available, with 8.6 GB to spare.

Your hardware
RTX 4080 16 GB · 64 GB RAM
Estimated memory
~6.9 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
81–115 tok/s estimate
Largest context in fast memory
66K tokens
Confidence: low

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

Memory breakdown

~6.9 GB (6.5 GB–7.4 GB) needed · estimate

Fits entirely in VRAM · 8.6 GB left

  • Model weights4.7 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 4080

Qwen3 8B on RTX 4080 + 64 GB RAM · 8K context
Qwen3 8B Q8_08K~10.5 GB · 53–72 tok/s estimateExcellent
Qwen3 8B Q6_K8K~8.6 GB · 65–90 tok/s estimateExcellent
Qwen3 8B Q5_K_M8K~7.7 GB · 72–102 tok/s estimateExcellent
Qwen3 8B Q4_K_M8K~6.9 GB · 81–115 tok/s estimateExcellent
Qwen3 8B IQ4_XS8K~6.5 GB · 87–125 tok/s estimateExcellent
Qwen3 8B Q3_K_M8K~6.1 GB · 94–135 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 8B 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 8B 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 4080 run Qwen3 8B?
The model needs about 6.9 GB and fits in the 15.5 GB available, with 8.6 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. ~10.5 GB / 15.5 GB.
What is the largest context on the RTX 4080?
At Q4_K_M, about 66K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 8B (RTX 4080)?
We estimate 81–115 tok/s (estimate, confidence low). Based on: memory bandwidth (717 GB/s) and model size.