NVIDIA A100 40GB经济之选40 GB VRAM (+8)1555 GB/s (+655)
B
Raises estimated decode speed by about 117%.72.8 tok/s 解码
Raises estimated decode speed by about 117%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can Aya Expanse 32B run on NVIDIA V100 32GB?
YES — Runs Great
B59Good
Estimated from fit model
Aya Expanse 32B needs ~26.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~34 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 26.1 GB, 33.6 tok/s, Runs well
26.1 GB required32.0 GB available
82% VRAM used
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5763 ms
Safe context
8K
Memory
26.1 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsAya Expanse 32B on NVIDIA V100 32GB
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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 33.6 tok/s | 3143 ms | 8K |
| Coding | B | Runs well | 33.6 tok/s | 5763 ms | 8K |
| Agentic Coding | B | Tight fit | 33.6 tok/s | 8382 ms | 8K |
| Reasoning | B | Runs well | 33.6 tok/s | 6811 ms | 8K |
| RAG | B | Tight fit | 33.6 tok/s | 10478 ms | 8K |
Inference speed
Aya Expanse 32B inference speed — tokens per second by GPU & Mac
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.
Quantization options
How Aya Expanse 32B (32B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C53 |
Q3_K_S | 3 | 15.7 GB | Low | C55 |
NVFP4 | 4 | 17.9 GB | Medium | C55 |
Q4_K_M | 4 | 19.5 GB | Medium | C54 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C54 |
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升级选项
能流畅运行 Aya Expanse 32B 的硬件
Frequently asked questions
Can NVIDIA V100 32GB run Aya Expanse 32B?
Yes, NVIDIA V100 32GB can run Aya Expanse 32B with a B grade (Runs well). Expected decode speed: 33.6 tok/s.
How much VRAM does Aya Expanse 32B need?
Aya Expanse 32B (32B parameters) requires approximately 26.1 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 NVIDIA V100 32GB?
On NVIDIA V100 32GB, Aya Expanse 32B achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5763ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run Aya Expanse 32B for coding?
For coding workloads, Aya Expanse 32B on NVIDIA V100 32GB receives a B grade with 33.6 tok/s and 8K context.
What context window can Aya Expanse 32B use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, 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.
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