Can aya expanse 32b heretic MPOA i1 run on Quadro RTX 8000 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~29.3 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.3 GB, 23.8 tok/s, Runs well

29.3 GB required48.0 GB available

61% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8150 ms

Safe context

96K

Memory

29.3 GB / 48.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsaya expanse 32b heretic MPOA i1 on Quadro RTX 8000 48GB
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: 23.8 tok/s decode · 8.2s TTFT (warm) · 59 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	23.8 tok/s	4446 ms	96K
Coding	C	Runs well	23.8 tok/s	8150 ms	96K
Agentic Coding	C	Runs well	23.8 tok/s	11855 ms	96K
Reasoning	C	Runs well	23.8 tok/s	9632 ms	96K
RAG	C	Runs well	23.8 tok/s	14818 ms	96K

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C44
Q3_K_S	3	15.7 GB	Low	C45
NVFP4	4	17.9 GB	Medium	C45
Q4_K_M	4	19.5 GB	Medium	C46
Q5_K_M	5	23.0 GB	High	C47
Q6_K	6	26.2 GB	High	C48
Q8_0Best for your GPU	8	34.2 GB	Very High	C48
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run aya expanse 32b heretic MPOA i1 on your machine.

Run

lms load hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien aya expanse 32b heretic MPOA i1

AMD Instinct MI210 64GBOpción económica

64 GB VRAM (+16)1638 GB/s (+966)

Sube la velocidad estimada de decodificación alrededor de un 140%.57.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 140%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run aya expanse 32b heretic MPOA i1?

Yes, Quadro RTX 8000 48GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 23.8 tok/s.

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

aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 29.3 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, aya expanse 32b heretic MPOA i1 achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8150ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run aya expanse 32b heretic MPOA i1 for coding?

For coding workloads, aya expanse 32b heretic MPOA i1 on Quadro RTX 8000 48GB receives a C grade with 23.8 tok/s and 96K context.

What context window can aya expanse 32b heretic MPOA i1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, aya expanse 32b heretic MPOA i1 can safely use up to 96K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for aya expanse 32b heretic MPOA i1

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