Can aya expanse 32b heretic MPOA i1 run on RTX 4500 Ada 24GB?

Q: What should I upgrade first if aya expanse 32b heretic MPOA i1 feels slow on RTX 4500 Ada 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D37Poor

Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~26.6 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 26.6 GB, 10.6 tok/s, Very compromised (needs ~1.9 GB host RAM)

26.6 GB required24.0 GB available

111% VRAM needed

2.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

10.6 tok/s

TTFT

18290 ms

Safe context

Memory

26.6 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsaya expanse 32b heretic MPOA i1 on RTX 4500 Ada 24GB
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: 10.6 tok/s decode · 18.3s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.5 GB host RAM)	12.3 tok/s	8552 ms	5K
Coding	D	Very compromised (needs ~1.9 GB host RAM)	10.6 tok/s	18290 ms	5K
Agentic Coding	F	Too heavy	8.0 tok/s	35127 ms	5K
Reasoning	D	Very compromised (needs ~1.9 GB host RAM)	10.6 tok/s	21615 ms	5K
RAG	F	Too heavy	8.0 tok/s	43908 ms

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C50
Q3_K_S	3	15.7 GB	Low	C49
NVFP4Best for your GPU

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

Upgrade options

Hardware that runs aya expanse 32b heretic MPOA i1 well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1360)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.9 tok/s decode

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

Raises estimated decode speed by about 276%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+464)

Removes host-memory offload, which is usually the single biggest latency and throughput win.38.6 tok/s decode

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

Raises estimated decode speed by about 264%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+144)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.6 tok/s decode

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

Raises estimated decode speed by about 123%.

~$4,000 MSRP

Frequently asked questions

Can RTX 4500 Ada 24GB run aya expanse 32b heretic MPOA i1?

Yes, RTX 4500 Ada 24GB can run aya expanse 32b heretic MPOA i1 with a D grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 10.6 tok/s.

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

aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 26.6 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 RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, aya expanse 32b heretic MPOA i1 achieves approximately 10.6 tokens per second decode speed with a time-to-first-token of 18290ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run aya expanse 32b heretic MPOA i1 for coding?

For coding workloads, aya expanse 32b heretic MPOA i1 on RTX 4500 Ada 24GB receives a D grade with 10.6 tok/s and 5K context.

What context window can aya expanse 32b heretic MPOA i1 use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, aya expanse 32b heretic MPOA i1 can safely use up to 5K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4500 Ada 24GB See all hardware for aya expanse 32b heretic MPOA i1

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