Can aya expanse 32b heretic MPOA i1 run on GTX 1080 Ti 11GB?

Q: What should I upgrade first if aya expanse 32b heretic MPOA i1 feels slow on GTX 1080 Ti 11GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~25.6 GB but GTX 1080 Ti 11GB only has 11.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 25.6 GB, exceeds 11.0 GB available

25.6 GB required11.0 GB available

233% VRAM needed

14.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.2 tok/s

TTFT

88226 ms

Safe context

Memory

25.6 GB / 11.0 GB

Offload

60%

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsaya expanse 32b heretic MPOA i1 on GTX 1080 Ti 11GB
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: 2.2 tok/s decode · 88.2s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 25.6 GB, but this setup only exposes 11.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.2 tok/s	48123 ms	4K
Coding	F	Too heavy	2.2 tok/s	88226 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	128329 ms	4K
Reasoning	F	Too heavy	2.2 tok/s	104267 ms	4K
RAG	F	Too heavy	2.2 tok/s	160411 ms	4K

Inference speed

aya expanse 32b heretic MPOA i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for aya expanse 32b heretic MPOA i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 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	61.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.8	Fits

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs aya expanse 32b heretic MPOA i1 well

RTX 3090 24GBBest value

24 GB VRAM (+13)936 GB/s (+452)

Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 800%.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+13)1008 GB/s (+524)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 955%.

~$1,599 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+21)1792 GB/s (+1308)

Makes the model fit on the accelerator instead of staying completely out of reach.61.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run aya expanse 32b heretic MPOA i1?

No, aya expanse 32b heretic MPOA i1 requires more memory than GTX 1080 Ti 11GB provides.

How much VRAM does aya expanse 32b heretic MPOA i1 need?

aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 32b heretic MPOA i1?

The recommended quantization for aya expanse 32b heretic MPOA i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will aya expanse 32b heretic MPOA i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, aya expanse 32b heretic MPOA i1 achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 88226ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run aya expanse 32b heretic MPOA i1 for coding?

For coding workloads, aya expanse 32b heretic MPOA i1 on GTX 1080 Ti 11GB receives a F grade with 2.2 tok/s and 4K context.

What context window can aya expanse 32b heretic MPOA i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, aya expanse 32b heretic MPOA i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for aya expanse 32b heretic MPOA i1

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