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Run LLM Locally

Qwen3 1.7B on the RTX 4090

With 24 GB of VRAM, the RTX 4090 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 23.3 GB available, with 20.1 GB to spare.

Your hardware
RTX 4090 24 GB · 64 GB RAM
Estimated memory
~3.1 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
255–431 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (1,008 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 20.1 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 4090

Qwen3 1.7B on RTX 4090 + 64 GB RAM · 8K context
Qwen3 1.7B Q8_08K~3.7 GB · 216–348 tok/s estimateExcellent
Qwen3 1.7B Q6_K8K~3.5 GB · 226–369 tok/s estimateExcellent
Qwen3 1.7B Q5_K_M8K~3.3 GB · 240–398 tok/s estimateExcellent
Qwen3 1.7B Q4_K_M8K~3.1 GB · 255–431 tok/s estimateExcellent
Qwen3 1.7B IQ4_XS8K~3 GB · 264–452 tok/s estimateExcellent
Qwen3 1.7B Q3_K_M8K~2.9 GB · 273–474 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 1.7B Q4_K_M entirely in VRAM

Other models that fit the RTX 4090

Where to buy a RTX 4090

Prices and links follow the market you choose.

GeForce RTX 4090

24 GB GDDR6X · 1,008 GB/s · 450 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 4090 run Qwen3 1.7B?
The model needs about 3.1 GB and fits in the 23.3 GB available, with 20.1 GB to spare.
Which quantization should I use on the RTX 4090?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~3.7 GB / 23.3 GB.
What is the largest context on the RTX 4090?
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 4090)?
We estimate 255–431 tok/s (estimate, confidence low). Based on: memory bandwidth (1,008 GB/s) and model size.