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URL: https://willitrunai.com/can-run/hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf-on-rtx-pro-6000-blackwell-server-96gb

⇱ aya expanse 32b heretic MPOA i1 on RTX PRO 6000 Blackwell S…

Can aya expanse 32b heretic MPOA i1 run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

C48Usable
○Estimated from fit model

aya expanse 32b heretic MPOA i1 needs ~34.1 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~69 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 34.1 GB, 68.7 tok/s, Runs well
34.1 GB required96.0 GB available
36% VRAM used

Fit status

Runs well

Decode

68.7 tok/s

TTFT

2817 ms

Safe context

280K

Memory

34.1 GB / 96.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsaya expanse 32b heretic MPOA i1 on RTX PRO 6000 Blackwell Server Edition 96GB
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: 68.7 tok/s decode · 2.8s TTFT (warm) · 172 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 well68.7 tok/s1537 ms280K
CodingCRuns well68.7 tok/s2817 ms280K
Agentic CodingCRuns well68.7 tok/s4098 ms280K
ReasoningCRuns well68.7 tok/s3329 ms280K
RAGCRuns well68.7 tok/s5122 ms280K

Quantization options

How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD39
Q3_K_S
3
15.7 GB
LowD40
NVFP4
4
17.9 GB
MediumC40
Q4_K_M
4
19.5 GB
MediumC40
Q5_K_M
5
23.0 GB
HighC41
Q6_K
6
26.2 GB
HighC41
Q8_0
8
34.2 GB
Very HighC43
F16Best for your GPU
16
65.6 GB
MaximumC48

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

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Server Edition 96GBSee all hardware for aya expanse 32b heretic MPOA i1