aya expanse 32b heretic MPOA i1 needs ~19.0 GB VRAM. RTX 5060 Ti 16GB has 16.0 GB. With Q2_K quantization, expect ~10 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
10.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.0 tok/s
TTFT
48166 ms
Safe context
4K
Memory
26.1 GB / 16.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.7 tok/s | 22562 ms | 4K |
| Coding | F | Too heavy | 4.0 tok/s | 48166 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 92159 ms | 4K |
| Reasoning | F | Too heavy | 4.0 tok/s | 56924 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 115199 ms | 4K |
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on RTX 5060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | F0 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
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 startOpciones de mejora
RTX 3090 24GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 395%.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 480%.
~$1,599 MSRP
RTX 5090 32GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
Yes, RTX 5060 Ti 16GB can run aya expanse 32b heretic MPOA i1 at Q2_K quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 26.1 GB which exceeds available memory, but at Q2_K it needs only 19.0 GB. Expected decode speed: 10.2 tok/s.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 26.1 GB at Q4_K_M quantization. On RTX 5060 Ti 16GB, it fits at Q2_K using 19.0 GB.
The recommended quantization is Q4_K_M, but on RTX 5060 Ti 16GB the best fitting quantization is Q2_K, which uses 19.0 GB.
On RTX 5060 Ti 16GB, aya expanse 32b heretic MPOA i1 achieves approximately 10.2 tokens per second decode speed with a time-to-first-token of 19063ms using Q2_K quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on RTX 5060 Ti 16GB receives a F grade with 4.0 tok/s and 4K context.
On RTX 5060 Ti 16GB, aya expanse 32b heretic MPOA i1 can safely use up to 4K tokens of context at Q2_K quantization. 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.
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