RTX A6000 48GBBudget pick48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.29.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
aya expanse 32b heretic MPOA i1 needs ~27.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Operating mode
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
Fit status
Tight fit
Decode
30.9 tok/s
TTFT
6267 ms
Safe context
34K
Memory
27.7 GB / 32.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 30.9 tok/s | 3418 ms | 34K |
| Coding | C | Tight fit | 30.9 tok/s | 6267 ms | 34K |
| Agentic Coding | C | Runs with offload | 30.9 tok/s | 9116 ms | 34K |
| Reasoning | C | Tight fit | 30.9 tok/s | 7407 ms | 34K |
| RAG | C | Runs with offload | 30.9 tok/s | 11395 ms | 34K |
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on NVIDIA V100 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 |
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 options
RTX A6000 48GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRaises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
NVIDIA L40 48GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$5,500 MSRP
Yes, NVIDIA V100 32GB can run aya expanse 32b heretic MPOA i1 with a C grade (Tight fit). Expected decode speed: 30.9 tok/s.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
The recommended quantization for aya expanse 32b heretic MPOA i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, aya expanse 32b heretic MPOA i1 achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6267ms using Q4_K_M quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on NVIDIA V100 32GB receives a C grade with 30.9 tok/s and 34K context.
On NVIDIA V100 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf-on-v100-32gb" 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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