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Run LLM Locally

Qwen3 4B on the RTX 4090

With 24 GB of VRAM, the RTX 4090 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 23.3 GB available, with 18.8 GB to spare.

Your hardware
RTX 4090 24 GB · 64 GB RAM
Estimated memory
~4.5 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
163–261 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

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

Memory breakdown

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

Fits entirely in VRAM · 18.8 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 4090

Qwen3 4B on RTX 4090 + 64 GB RAM · 8K context
Qwen3 4B Q8_08K~6.2 GB · 119–178 tok/s estimateExcellent
Qwen3 4B Q6_K8K~5.3 GB · 139–215 tok/s estimateExcellent
Qwen3 4B Q5_K_M8K~4.9 GB · 151–236 tok/s estimateExcellent
Qwen3 4B Q4_K_M8K~4.5 GB · 163–261 tok/s estimateExcellent
Qwen3 4B IQ4_XS8K~4.3 GB · 171–277 tok/s estimateExcellent
Qwen3 4B Q3_K_M8K~4.1 GB · 179–293 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 4B 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 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 4090 run Qwen3 4B?
The model needs about 4.5 GB and fits in the 23.3 GB available, with 18.8 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. ~6.2 GB / 23.3 GB.
What is the largest context on the RTX 4090?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 4B (RTX 4090)?
We estimate 163–261 tok/s (estimate, confidence low). Based on: memory bandwidth (1,008 GB/s) and model size.