aya expanse 32b heretic MPOA i1 needs ~25.3 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
17.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
80337 ms
Safe context
4K
Memory
25.3 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.3 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.4 tok/s | 43820 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 80337 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116854 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 94944 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146068 ms | 4K |
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on RTX 3070 8GB (8.0 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 |
Upgrade options
RTX 3090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 725%.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 867%.
~$1,599 MSRP
RTX 5090 32GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
No, aya expanse 32b heretic MPOA i1 requires more memory than RTX 3070 8GB provides.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 25.3 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 RTX 3070 8GB, aya expanse 32b heretic MPOA i1 achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 80337ms using Q4_K_M quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on RTX 3070 8GB receives a F grade with 2.4 tok/s and 4K context.
On RTX 3070 8GB, 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.
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-rtx-3070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
