Will It Run AI

Can Aya Expanse 32B run on NVIDIA A16 64GB?

YES — Runs Great

C53Usable
Estimated from fit model

Aya Expanse 32B needs ~29.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.6 GB, 26.1 tok/s, Runs well
29.6 GB required64.0 GB available
46% VRAM used

Fit status

Runs well

Decode

26.1 tok/s

TTFT

7425 ms

Safe context

8K

Memory

29.6 GB / 64.0 GB

Memory breakdown

Weights19.5 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on NVIDIA A16 64GB
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: 26.1 tok/s decode · 7.4s TTFT (warm) · 65 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well26.1 tok/s4050 ms8K
CodingCRuns well26.1 tok/s7425 ms8K
Agentic CodingCRuns well26.1 tok/s10800 ms8K
ReasoningCRuns well26.1 tok/s8775 ms8K
RAGCRuns well26.1 tok/s13500 ms8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC47
Q3_K_S
3
15.7 GB
LowC48
NVFP4
4
17.9 GB
MediumC48
Q4_K_M
4
19.5 GB
MediumC49
Q5_K_M
5
23.0 GB
HighC50
Q6_K
6
26.2 GB
HighC50
Q8_0Best for your GPU
8
34.2 GB
Very HighC53
F16
16
65.6 GB
MaximumF0

Get started

Copy-paste commands to run Aya Expanse 32B on your machine.

Run

ollama run aya-expanse:32b

Upgrade-Optionen

Hardware, die Aya Expanse 32B gut ausführt

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl
96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 221%.83.9 tok/s Dekodierung

Raises estimated decode speed by about 221%.

Adds memory headroom for longer context windows and future model growth.

ca. $9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-Verhältnis
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 186%.74.7 tok/s Dekodierung

Raises estimated decode speed by about 186%.

Adds memory headroom for longer context windows and future model growth.

ca. $9,999 MSRP

NVIDIANVIDIA H20 96GBNVIDIA-Upgrade
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 592%.180.5 tok/s Dekodierung

Raises estimated decode speed by about 592%.

Adds memory headroom for longer context windows and future model growth.

ca. $12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Aya Expanse 32B?

Yes, NVIDIA A16 64GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 26.1 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 29.6 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 A16 64GB?

On NVIDIA A16 64GB, Aya Expanse 32B achieves approximately 26.1 tokens per second decode speed with a time-to-first-token of 7425ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on NVIDIA A16 64GB receives a C grade with 26.1 tok/s and 8K context.

What context window can Aya Expanse 32B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.

See all results for NVIDIA A16 64GBSee all hardware for Aya Expanse 32B
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