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Run LLM Locally

Qwen3 1.7B on the RTX 3080 Ti

With 12 GB of VRAM, the RTX 3080 Ti 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 11.6 GB available, with 8.5 GB to spare.

Your hardware
RTX 3080 Ti 12 GB · 64 GB RAM
Estimated memory
~3.1 GB estimate
VRAM (usable memory)
11.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
240–399 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (912 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 8.5 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 3080 Ti

Qwen3 1.7B on RTX 3080 Ti + 64 GB RAM · 8K context
Qwen3 1.7B Q8_08K~3.7 GB · 202–321 tok/s estimateExcellent
Qwen3 1.7B Q6_K8K~3.5 GB · 212–340 tok/s estimateExcellent
Qwen3 1.7B Q5_K_M8K~3.3 GB · 226–368 tok/s estimateExcellent
Qwen3 1.7B Q4_K_M8K~3.1 GB · 240–399 tok/s estimateExcellent
Qwen3 1.7B IQ4_XS8K~3 GB · 249–419 tok/s estimateExcellent
Qwen3 1.7B Q3_K_M8K~2.9 GB · 259–440 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 1.7B Q4_K_M entirely in VRAM

Other models that fit the RTX 3080 Ti

Where to buy a RTX 3080 Ti

Prices and links follow the market you choose.

GeForce RTX 3080 Ti

12 GB GDDR6X · 912 GB/s · 350 W

ExcellentRuns Qwen3 1.7B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 3080 Ti run Qwen3 1.7B?
The model needs about 3.1 GB and fits in the 11.6 GB available, with 8.5 GB to spare.
Which quantization should I use on the RTX 3080 Ti?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~3.7 GB / 11.6 GB.
What is the largest context on the RTX 3080 Ti?
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 3080 Ti)?
We estimate 240–399 tok/s (estimate, confidence low). Based on: memory bandwidth (912 GB/s) and model size.