aya expanse 32b heretic MPOA i1 needs ~38.6 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~207 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
Runs well
Decode
206.6 tok/s
TTFT
937 ms
Safe context
453K
Memory
38.6 GB / 141.0 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 | 206.6 tok/s | 511 ms | 453K |
| Coding | C | Runs well | 206.6 tok/s | 937 ms | 453K |
| Agentic Coding | C | Runs well | 206.6 tok/s | 1363 ms | 453K |
| Reasoning | C | Runs well | 206.6 tok/s | 1108 ms | 453K |
| RAG | C | Runs well | 206.6 tok/s | 1704 ms | 453K |
Inference speed
Estimated decode speed (tokens/sec) for aya expanse 32b heretic MPOA i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 61.5 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 30.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 30.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 28.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 23.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 23.2 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 22.5 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 21.4 | Heavy offload |
| RTX 3090 24GB | 24 GB | Q4_K_M | 19.8 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 19.4 | Tight |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 12.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 11.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 8.4 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.9 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | D38 |
Q3_K_S | 3 | 15.7 GB | Low | D38 |
NVFP4 | 4 | 17.9 GB | Medium | D38 |
Q4_K_M | 4 | 19.5 GB | Medium | D38 |
Q5_K_M | 5 | 23.0 GB | High | D39 |
Q6_K | 6 | 26.2 GB | High | D39 |
Q8_0 | 8 | 34.2 GB | Very High | C40 |
F16Best for your GPU | 16 | 65.6 GB | Maximum | C45 |
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 startYes, NVIDIA H200 PCIe 141GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 206.6 tok/s.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 38.6 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 H200 PCIe 141GB, aya expanse 32b heretic MPOA i1 achieves approximately 206.6 tokens per second decode speed with a time-to-first-token of 937ms using Q4_K_M quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on NVIDIA H200 PCIe 141GB receives a C grade with 206.6 tok/s and 453K context.
On NVIDIA H200 PCIe 141GB, aya expanse 32b heretic MPOA i1 can safely use up to 453K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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