RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1120)
B
Raises estimated decode speed by about 158%.43.3 tok/sデコード
Raises estimated decode speed by about 158%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Aya Expanse 32B run on Quadro RTX 6000 24GB?
YES — With Offload
C54Usable
Estimated from fit model
Aya Expanse 32B needs ~25.3 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 25.3 GB, 16.8 tok/s, Runs with offload (needs ~1 GB host RAM)
25.3 GB required24.0 GB available
105% VRAM needed
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1 GB host RAM)
Decode
16.8 tok/s
TTFT
11498 ms
Safe context
8K
Memory
25.3 GB / 24.0 GB
Memory breakdown
Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsAya Expanse 32B on Quadro RTX 6000 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 16.8 tok/s decode · 11.5s TTFT (warm) · 42 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0 GB host RAM) | 18.8 tok/s | 5625 ms | 8K |
| Coding | C | Runs with offload (needs ~1 GB host RAM) | 16.8 tok/s | 11498 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~2.6 GB host RAM) | 13.7 tok/s | 20480 ms | 8K |
| Reasoning | C | Runs with offload (needs ~1 GB host RAM) | 16.8 tok/s | 13588 ms | 8K |
| RAG | C | Very compromised (needs ~2.6 GB host RAM) | 13.7 tok/s | 25600 ms | 8K |
Quantization options
How Aya Expanse 32B (32B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | B56 |
Q3_K_S | 3 | 15.7 GB | Low | B55 |
NVFP4Best for your GPU | 4 | 17.9 GB | Medium | C55 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bアップグレードオプション
Aya Expanse 32Bを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+224)
B
Raises estimated decode speed by about 149%.41.9 tok/sデコード
Raises estimated decode speed by about 149%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)
B
Raises estimated decode speed by about 53%.25.7 tok/sデコード
Raises estimated decode speed by about 53%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can Quadro RTX 6000 24GB run Aya Expanse 32B?
Yes, Quadro RTX 6000 24GB can run Aya Expanse 32B with a C grade (Runs with offload (needs ~1 GB host RAM)). Expected decode speed: 16.8 tok/s.
How much VRAM does Aya Expanse 32B need?
Aya Expanse 32B (32B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Aya Expanse 32B?
The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will Aya Expanse 32B run at on Quadro RTX 6000 24GB?
On Quadro RTX 6000 24GB, Aya Expanse 32B achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11498ms using Q4_K_M quantization.
Can Quadro RTX 6000 24GB run Aya Expanse 32B for coding?
For coding workloads, Aya Expanse 32B on Quadro RTX 6000 24GB receives a C grade with 16.8 tok/s and 8K context.
What context window can Aya Expanse 32B use on Quadro RTX 6000 24GB?
On Quadro RTX 6000 24GB, Aya Expanse 32B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if Aya Expanse 32B feels slow on Quadro RTX 6000 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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