Can I Run Aya Expanse on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated October 8, 2024
Yes
Yes — Aya Expanse 32B at Q4_K_M needs about 21.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) (~2.4 GB spare), at ~34.6 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~34.6 tok/s
See what else this hardware can run →
or compare on Vast.ai from $0.35/hr (typical low · varies)
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Aya Expanse on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.7 GB | ✗ No | — | — | 64.6 GB |
| Q8_0 | 36.5 GB | ✗ No | — | — | 34.3 GB |
| Q6_K | 28.6 GB | ✗ No | — | — | 26.5 GB |
| Q5_K_M | 25 GB | ✗ No | — | — | 22.9 GB |
| Q4_K_M | 21.6 GB | ✓ Yes | 16K | ~34.6 tok/s | 19.5 GB |
| Q3_K_M | 15.9 GB | ✓ Yes | 32K | ~46.8 tok/s | 13.8 GB |
| Q2_K | 12.8 GB | ✓ Yes | 64K | ~58.2 tok/s | 10.6 GB |
Which Aya Expanse sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Aya Expanse 32B | 21.6 GB | ✓ Fits | ~34.6 tok/s |
| Aya Expanse 8B | 6.7 GB | ✓ Fits | ~106.7 tok/s |
RTX 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second card.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 24 GB of VRAM on the NVIDIA GeForce RTX 4090 at 1008 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Aya Expanse on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes — Aya Expanse 32B at Q4_K_M needs about 21.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) (~2.4 GB spare), at ~34.6 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Aya Expanse should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Q4_K_M — it needs about 21.6 GB of the 24 GB available, downloads as roughly 19.5 GB, and runs at an estimated 34.6 tokens/sec with up to 16K of context.
What limits Aya Expanse on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- BitNet b1.58 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Bonsai 27B on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Codestral on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Cogito v1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Command R Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
Aya Expanse on GPUs
- Aya Expanse on NVIDIA GeForce RTX 5090
- Aya Expanse on NVIDIA GeForce RTX 5070
- Aya Expanse on NVIDIA GeForce RTX 5060 Ti 8GB
- Aya Expanse on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for document analysis
- Best local LLMs for translation
- Best local LLMs for enterprise assistant
Model & Tools
← Can I Run It? | Aya Expanse model page | Check your hardware