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Run LLM Locally

Qwen3 1.7B on the RTX 5080

With 16 GB of VRAM, the RTX 5080 runs Qwen3 1.7B at Q4_K_M entirely on the GPU with an 8K context (about 3.1 GB needed). At Q8_0 it fits entirely.

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Qwen3 1.7B

Q4_K_M · 8K context

Excellent

The model needs about 3.1 GB and fits in the 15.5 GB available, with 12.4 GB to spare.

Your hardware
RTX 5080 16 GB · 64 GB RAM
Estimated memory
~3.1 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
247–415 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (960 GB/s) and model size

Memory breakdown

~3.1 GB (2.8 GB–3.5 GB) needed · estimate

Fits entirely in VRAM · 12.4 GB left

  • Model weights1.2 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 5080

Qwen3 1.7B on RTX 5080 + 64 GB RAM · 8K context
Qwen3 1.7B Q8_08K~3.7 GB · 209–334 tok/s estimateExcellent
Qwen3 1.7B Q6_K8K~3.5 GB · 219–355 tok/s estimateExcellent
Qwen3 1.7B Q5_K_M8K~3.3 GB · 233–384 tok/s estimateExcellent
Qwen3 1.7B Q4_K_M8K~3.1 GB · 247–415 tok/s estimateExcellent
Qwen3 1.7B IQ4_XS8K~3 GB · 257–436 tok/s estimateExcellent
Qwen3 1.7B Q3_K_M8K~2.9 GB · 266–457 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 1.7B Q4_K_M entirely in VRAM

Other models that fit the RTX 5080

Where to buy a RTX 5080

Prices and links follow the market you choose.

GeForce RTX 5080

16 GB GDDR7 · 960 GB/s · 360 W

ExcellentRuns Qwen3 1.7B 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 5080 run Qwen3 1.7B?
The model needs about 3.1 GB and fits in the 15.5 GB available, with 12.4 GB to spare.
Which quantization should I use on the RTX 5080?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~3.7 GB / 15.5 GB.
What is the largest context on the RTX 5080?
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 1.7B (RTX 5080)?
We estimate 247–415 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.