Aya Expanse 32B needs ~27.2 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~73 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
Fit status
Runs well
Decode
72.8 tok/s
TTFT
2660 ms
Safe context
8K
Memory
27.2 GB / 40.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 72.8 tok/s | 1451 ms | 8K |
| Coding | B | Runs well | 72.8 tok/s | 2660 ms | 8K |
| Agentic Coding | B | Runs well | 72.8 tok/s | 3870 ms | 8K |
| Reasoning | B | Runs well | 72.8 tok/s | 3144 ms | 8K |
| RAG | B | Runs well | 72.8 tok/s | 4837 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 NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C51 |
Q3_K_S | 3 | 15.7 GB | Low | C52 |
NVFP4 | 4 | 17.9 GB | Medium | C53 |
Q4_K_M | 4 | 19.5 GB | Medium | C54 |
Q5_K_M | 5 | 23.0 GB | High | C54 |
Q6_KBest for your GPU | 6 | 26.2 GB | High | C54 |
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:32bYes, NVIDIA A100 40GB can run Aya Expanse 32B with a B grade (Runs well). Expected decode speed: 72.8 tok/s.
Aya Expanse 32B (32B parameters) requires approximately 27.2 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 NVIDIA A100 40GB, Aya Expanse 32B achieves approximately 72.8 tokens per second decode speed with a time-to-first-token of 2660ms using Q4_K_M quantization.
For coding workloads, Aya Expanse 32B on NVIDIA A100 40GB receives a B grade with 72.8 tok/s and 8K context.
On NVIDIA A100 40GB, 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.
Paste this snippet into any page to show a live fit card.
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