Will It Run AI

Can aya expanse 32b heretic MPOA i1 run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

C49Usable
Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~27.4 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 27.4 GB, 19.3 tok/s, Tight fit
27.4 GB required32.0 GB available
86% VRAM used

Fit status

Tight fit

Decode

19.3 tok/s

TTFT

10008 ms

Safe context

36K

Memory

27.4 GB / 32.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsaya expanse 32b heretic MPOA i1 on Radeon AI PRO R9700 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: 19.3 tok/s decode · 10.0s TTFT (warm) · 48 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 well19.3 tok/s5459 ms36K
CodingCTight fit19.3 tok/s10008 ms36K
Agentic CodingCRuns with offload19.3 tok/s14557 ms36K
ReasoningCTight fit19.3 tok/s11828 ms36K
RAGCRuns with offload19.3 tok/s18197 ms36K

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC47
Q3_K_S
3
15.7 GB
LowC49
NVFP4
4
17.9 GB
MediumC49
Q4_K_M
4
19.5 GB
MediumC49
Q5_K_MBest for your GPU
5
23.0 GB
HighC48
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

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-Optionen

Hardware, die aya expanse 32b heretic MPOA i1 gut ausführt

Radeon Pro W7900 48GBGünstige Wahl
48 GB VRAM (+16)864 GB/s (+224)
C
Raises estimated decode speed by about 35%.26.1 tok/s Dekodierung

Raises estimated decode speed by about 35%.

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

ca. $3,999 MSRP

Radeon PRO W7900 DS 48GBBestes Preis-Leistungs-Verhältnis
48 GB VRAM (+16)864 GB/s (+224)
C
Raises estimated decode speed by about 35%.26.1 tok/s Dekodierung

Raises estimated decode speed by about 35%.

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

ca. $3,999 MSRP

AMD Instinct MI210 64GBAMD-Upgrade
64 GB VRAM (+32)1638 GB/s (+998)
C
Raises estimated decode speed by about 196%.57.1 tok/s Dekodierung

Raises estimated decode speed by about 196%.

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

ca. $10,000 MSRP

NVIDIANVIDIA A100 40GBGrößter Sprung
40 GB VRAM (+8)1555 GB/s (+915)
B
Raises estimated decode speed by about 247%.66.9 tok/s Dekodierung

Raises estimated decode speed by about 247%.

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

ca. $10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run aya expanse 32b heretic MPOA i1?

Yes, Radeon AI PRO R9700 32GB can run aya expanse 32b heretic MPOA i1 with a C grade (Tight fit). Expected decode speed: 19.3 tok/s.

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

aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 27.4 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, aya expanse 32b heretic MPOA i1 achieves approximately 19.3 tokens per second decode speed with a time-to-first-token of 10008ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run aya expanse 32b heretic MPOA i1 for coding?

For coding workloads, aya expanse 32b heretic MPOA i1 on Radeon AI PRO R9700 32GB receives a C grade with 19.3 tok/s and 36K context.

What context window can aya expanse 32b heretic MPOA i1 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, aya expanse 32b heretic MPOA i1 can safely use up to 36K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GBSee all hardware for aya expanse 32b heretic MPOA i1
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