Radeon AI PRO R9700 32GBBudget pick
32 GB VRAM (+8)
B
Adds memory headroom for longer context windows and future model growth.21 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Aya Expanse 32B needs ~25.3 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Operating mode
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
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1 GB host RAM)
Decode
25.9 tok/s
TTFT
7467 ms
Safe context
8K
Memory
25.3 GB / 24.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~0 GB host RAM) | 28.8 tok/s | 3669 ms | 8K |
| Coding | B | Runs with offload (needs ~1 GB host RAM) | 25.9 tok/s | 7467 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~2.6 GB host RAM) | 21.4 tok/s | 13188 ms | 8K |
| Reasoning | B | Runs with offload (needs ~1 GB host RAM) | 25.9 tok/s | 8824 ms | 8K |
| RAG | C | Very compromised (needs ~2.6 GB host RAM) | 21.4 tok/s | 16485 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Aya Expanse 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~43 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 43.3 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 33.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 33.5 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 31.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 25.9 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 25.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 24.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.1 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 16.6 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 15.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 13.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 12.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 10.2 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Aya Expanse 32B (32B params) fits at each quantization level on RX 7900 XTX 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 |
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,249 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, RX 7900 XTX 24GB can run Aya Expanse 32B with a B grade (Runs with offload (needs ~1 GB host RAM)). Expected decode speed: 25.9 tok/s.
Aya Expanse 32B (32B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.
On RX 7900 XTX 24GB, Aya Expanse 32B achieves approximately 25.9 tokens per second decode speed with a time-to-first-token of 7467ms using Q4_K_M quantization.
For coding workloads, Aya Expanse 32B on RX 7900 XTX 24GB receives a B grade with 25.9 tok/s and 8K context.
On RX 7900 XTX 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/aya-expanse-32b-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: