RTX A6000 48GBGünstige Wahl48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.29.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $4,650 MSRP
Can aya expanse 32b heretic MPOA i1 run on RTX PRO 4500 Blackwell 32GB?
YES — Tight Fit
C51Usable
Estimated from fit model
aya expanse 32b heretic MPOA i1 needs ~27.7 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 27.7 GB, 38.6 tok/s, Tight fit
27.7 GB required32.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
38.6 tok/s
TTFT
5021 ms
Safe context
34K
Memory
27.7 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsaya expanse 32b heretic MPOA i1 on RTX PRO 4500 Blackwell 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: 38.6 tok/s decode · 5.0s TTFT (warm) · 96 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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 38.6 tok/s | 2739 ms | 34K |
| Coding | C | Tight fit | 38.6 tok/s | 5021 ms | 34K |
| Agentic Coding | C | Runs with offload | 38.6 tok/s | 7303 ms | 34K |
| Reasoning | C | Tight fit | 38.6 tok/s | 5934 ms | 34K |
| RAG | C | Runs with offload | 38.6 tok/s | 9129 ms | 34K |
Quantization options
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C47 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
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 startUpgrade-Optionen
Hardware, die aya expanse 32b heretic MPOA i1 gut ausführt
RTX A6000 48GBGünstige WahlRTX PRO 5000 Blackwell 48GBBestes Preis-Leistungs-Verhältnis48 GB VRAM (+16)1344 GB/s (+448)
C
Raises estimated decode speed by about 50%.57.8 tok/s Dekodierung
Raises estimated decode speed by about 50%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
NVIDIA L40 48GBNVIDIA-Upgrade48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.34.5 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $5,500 MSRP
Frequently asked questions
Can RTX PRO 4500 Blackwell 32GB run aya expanse 32b heretic MPOA i1?
Yes, RTX PRO 4500 Blackwell 32GB can run aya expanse 32b heretic MPOA i1 with a C grade (Tight fit). Expected decode speed: 38.6 tok/s.
How much VRAM does aya expanse 32b heretic MPOA i1 need?
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 27.7 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 PRO 4500 Blackwell 32GB?
On RTX PRO 4500 Blackwell 32GB, aya expanse 32b heretic MPOA i1 achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5021ms using Q4_K_M quantization.
Can RTX PRO 4500 Blackwell 32GB run aya expanse 32b heretic MPOA i1 for coding?
For coding workloads, aya expanse 32b heretic MPOA i1 on RTX PRO 4500 Blackwell 32GB receives a C grade with 38.6 tok/s and 34K context.
What context window can aya expanse 32b heretic MPOA i1 use on RTX PRO 4500 Blackwell 32GB?
On RTX PRO 4500 Blackwell 32GB, aya expanse 32b heretic MPOA i1 can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf-on-rtx-pro-4500-blackwell-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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