aya expanse 8b orthogonal heretic i1 needs ~8.2 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~65 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
64.6 tok/s
TTFT
2996 ms
Safe context
81K
Memory
8.2 GB / 12.0 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 | 64.6 tok/s | 1634 ms | 81K |
| Coding | B | Runs well | 64.6 tok/s | 2996 ms | 81K |
| Agentic Coding | B | Runs well | 64.6 tok/s | 4358 ms | 81K |
| Reasoning | B | Runs well | 64.6 tok/s | 3541 ms | 81K |
| RAG | B | Runs well | 64.6 tok/s | 5447 ms | 81K |
Inference speed
Estimated decode speed (tokens/sec) for aya expanse 8b orthogonal heretic i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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 | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
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 8b orthogonal heretic i1 (8B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C49 |
Q3_K_S | 3 | 3.9 GB | Low | C50 |
NVFP4 | 4 | 4.5 GB | Medium | C51 |
Q4_K_M | 4 | 4.9 GB | Medium | C52 |
Q5_K_M | 5 | 5.8 GB | High | C52 |
Q6_K | 6 | 6.6 GB | High | C52 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run aya expanse 8b orthogonal heretic i1 on your machine.
Run
lms load hf-mradermacher--aya-expanse-8b-orthogonal-heretic-i1-gguf && lms server startYes, RTX 4000 Ada Laptop 12GB can run aya expanse 8b orthogonal heretic i1 with a B grade (Runs well). Expected decode speed: 64.6 tok/s.
aya expanse 8b orthogonal heretic i1 (8B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
The recommended quantization for aya expanse 8b orthogonal heretic i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada Laptop 12GB, aya expanse 8b orthogonal heretic i1 achieves approximately 64.6 tokens per second decode speed with a time-to-first-token of 2996ms using Q4_K_M quantization.
For coding workloads, aya expanse 8b orthogonal heretic i1 on RTX 4000 Ada Laptop 12GB receives a B grade with 64.6 tok/s and 81K context.
On RTX 4000 Ada Laptop 12GB, aya expanse 8b orthogonal heretic i1 can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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