aya expanse 32b heretic MPOA i1 needs ~26.9 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~6 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
2.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.1 GB host RAM)
Decode
5.9 tok/s
TTFT
32776 ms
Safe context
4K
Memory
26.9 GB / 24.0 GB
Offload
10%
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.
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 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.8 GB host RAM) | 6.9 tok/s | 15353 ms | 4K |
| Coding | D | Very compromised (needs ~2.1 GB host RAM) | 5.9 tok/s | 32776 ms | 4K |
| Agentic Coding | F | Too heavy | 4.5 tok/s | 62764 ms | 4K |
| Reasoning | D | Very compromised (needs ~2.1 GB host RAM) | 5.9 tok/s | 38735 ms | 4K |
| RAG | F | Too heavy | 4.5 tok/s | 78455 ms | 4K |
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on NVIDIA L4 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 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
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 startOpções de upgrade
RTX 5090 32GBOpção econômicaRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 942%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMelhor custo-benefícioRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 554%.
~$2,499 MSRP
RTX 5000 Ada 32GBUpgrade NVIDIARemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 300%.
~$4,000 MSRP
Yes, NVIDIA L4 24GB can run aya expanse 32b heretic MPOA i1 with a D grade (Very compromised (needs ~2.1 GB host RAM)). Expected decode speed: 5.9 tok/s.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 26.9 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 L4 24GB, aya expanse 32b heretic MPOA i1 achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32776ms using Q4_K_M quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on NVIDIA L4 24GB receives a D grade with 5.9 tok/s and 4K context.
On NVIDIA L4 24GB, aya expanse 32b heretic MPOA i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
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