GeForce RTX 4090
24 GB GDDR6X · 1,008 GB/s · 450 W
RTX 4090 24 GB · 64 GB RAM → Qwen3 32B
GPU · NVIDIA
RTX 4090 for local LLMs: which models run?
With 24 GB of VRAM and 1008 GB/s of bandwidth, the RTX 4090 runs models up to about Qwen3 32B entirely on the GPU.
- VRAM24 GB GDDR6X
- Bandwidth1,008 GB/s
- Board power450 W
- ArchitectureAda Lovelace
Specifications
- VRAM
- 24 GB
- Memory type
- GDDR6X
- Memory bus
- 384-bit
- Bandwidth
- 1,008 GB/s
- Board power
- 450 W
- Backends
- CUDA, Vulkan
- Interface
- PCIe 4.0 x16
- Released
- October 2022
- Segment
- Consumer
- FP16 compute
- Not available
Official specData updated 24 days agoSource: NVIDIA specifications
What the RTX 4090 runs
| Qwen3 30B-A3BQ4_K_M · 8K~19.7 GB · 125–209 tok/s estimate | Excellent |
|---|---|
| Qwen3 Coder 30B-A3B InstructQ4_K_M · 8K~19.7 GB · 125–209 tok/s estimate | Excellent |
| gpt-oss-20bMXFP4 · 8K~13 GB · 212–243 tok/s measured | Excellent |
| Qwen3 14BQ8_0 · 8K~17.5 GB · 43–58 tok/s estimate | Excellent |
| DeepSeek R1 Distill Qwen 14BQ8_0 · 8K~17.8 GB · 42–57 tok/s estimate | Excellent |
| Qwen2.5 14B InstructQ8_0 · 8K~17.8 GB · 42–57 tok/s estimate | Excellent |
| Phi-4 14BQ8_0 · 8K~17.6 GB · 43–58 tok/s estimate | Excellent |
| Mistral Nemo 12B InstructQ8_0 · 8K~14.9 GB · 50–69 tok/s estimate | Excellent |
| Gemma 3 12B ITQ8_0 · 8K~14.1 GB · 51–72 tok/s estimate | Excellent |
| Gemma 2 9B ITQ8_0 · 8K~12.7 GB · 61–85 tok/s estimate | Excellent |
| Qwen3 8BQ8_0 · 8K~10.5 GB · 71–99 tok/s estimate | Excellent |
| Llama 3.1 8B InstructQ8_0 · 8K~10.2 GB · 74–102 tok/s estimate | Excellent |
| DeepSeek R1 Distill Llama 8BQ8_0 · 8K~10.2 GB · 74–102 tok/s estimate | Excellent |
| Qwen2.5 7B InstructQ8_0 · 8K~9.2 GB · 81–112 tok/s estimate | Excellent |
| Mistral 7B Instruct v0.3Q8_0 · 8K~9.2 GB · 80–112 tok/s estimate | Excellent |
| Gemma 3 4B ITQ8_0 · 8K~5.4 GB · 133–200 tok/s estimate | Excellent |
| Qwen3 4BQ8_0 · 8K~6.2 GB · 119–178 tok/s estimate | Excellent |
| Phi-4 Mini Instruct 3.8BQ8_0 · 8K~6 GB · 127–189 tok/s estimate | Excellent |
| Llama 3.2 3B InstructQ8_0 · 8K~5.1 GB · 150–224 tok/s estimate | Excellent |
| Qwen3 1.7BQ8_0 · 8K~3.7 GB · 216–348 tok/s estimate | Excellent |
| Llama 3.2 1B InstructQ8_0 · 8K~2.5 GB · 354–561 tok/s estimate | Excellent |
| Qwen3 0.6BQ8_0 · 8K~2.5 GB · 322–597 tok/s estimate | Excellent |
| Mixtral 8x7B InstructQ3_K_M · 8K~21.6 GB · 81–117 tok/s estimate | Compatible |
| Qwen3 32BQ4_K_M · 8K~22.3 GB · 33–45 tok/s estimate | Compatible |
| DeepSeek R1 Distill Qwen 32BQ4_K_M · 8K~22.4 GB · 33–45 tok/s estimate | Compatible |
| Qwen2.5 32B InstructQ4_K_M · 8K~22.4 GB · 33–45 tok/s estimate | Compatible |
| Qwen2.5 Coder 32B InstructQ4_K_M · 8K~22.4 GB · 33–45 tok/s estimate | Compatible |
| QwQ 32BQ4_K_M · 8K~22.4 GB · 33–45 tok/s estimate | Compatible |
| Gemma 3 27B ITQ5_K_M · 8K~21 GB · 34–47 tok/s estimate | Compatible |
| Gemma 2 27B ITQ5_K_M · 8K~22.3 GB · 34–46 tok/s estimate | Compatible |
| Mistral Small 3.2 24B InstructQ6_K · 8K~21 GB · 36–48 tok/s estimate | Compatible |
| gpt-oss-120bMXFP4 · 8K~63.2 GB · 10–20 tok/s estimate | Possible but slow |
| Llama 4 Scout 17B-16E InstructQ3_K_M · 8K~55.7 GB · 3.5–7 tok/s estimate | Possible but slow |
| Qwen2.5 72B InstructQ3_K_M · 8K~38.2 GB · 1.3–2.5 tok/s estimate | Possible but slow |
| Llama 3.3 70B InstructQ3_K_M · 8K~37 GB · 1.4–2.5 tok/s estimate | Possible but slow |
| DeepSeek R1 Distill Llama 70BQ3_K_M · 8K~37 GB · 1.4–2.5 tok/s estimate | Possible but slow |
| DeepSeek R1 (671B)Q8_0 · 8K~699 GB · No data | Incompatible |
| Qwen3 235B-A22BQ8_0 · 8K~247 GB · No data | Incompatible |
For each model, the highest-precision quantization that runs entirely in fast memory; otherwise the one with the best result. Speeds are ranges; "estimate" unless a benchmark exists for this exact hardware.
EstimateData updated 24 days agoSource: Run LLM Locally engine
Limits in plain words
24 GB of VRAM holds the weights and KV cache of models up to about Qwen3 32B at Q4_K_M. Bigger models spill into system RAM and slow down sharply: generation speed is then limited by RAM bandwidth, not by the GPU.
Where to buy a RTX 4090
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Frequently asked questions
Which LLMs can the RTX 4090 run?
Entirely on the GPU: Mixtral 8x7B Instruct (Q3_K_M), Qwen3 32B (Q4_K_M), DeepSeek R1 Distill Qwen 32B (Q4_K_M), Qwen2.5 32B Instruct (Q4_K_M), Qwen2.5 Coder 32B Instruct (Q4_K_M), QwQ 32B (Q4_K_M), Qwen3 30B-A3B (Q4_K_M), Qwen3 Coder 30B-A3B Instruct (Q4_K_M). Larger models need CPU/GPU offload.
Can the RTX 4090 run a 70B model?
Not recommended: a 70B model at Q4_K_M needs about 45.1 GB; the RTX 4090 has 24 GB.
How is the speed estimated?
Token generation reads every active weight once per token, so it is limited by memory bandwidth (1008 GB/s here). We apply an efficiency range per backend and label the result as an estimate until real benchmarks exist.