MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
C
Raises estimated decode speed by about 31%.76.8 tok/s decode
Raises estimated decode speed by about 31%.
~$2,499 MSRP
aya expanse 8b needs ~9.9 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 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
58.8 tok/s
TTFT
3295 ms
Safe context
393K
Memory
9.9 GB / 32.0 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 | 58.8 tok/s | 1797 ms | 393K |
| Coding | C | Runs well | 58.8 tok/s | 3295 ms | 393K |
| Agentic Coding | C | Runs well | 58.8 tok/s | 4793 ms | 393K |
| Reasoning | C | Runs well | 58.8 tok/s | 3894 ms | 393K |
| RAG | C | Runs well | 58.8 tok/s | 5991 ms | 393K |
How aya expanse 8b (8B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 | 4.5 GB | Medium | C43 |
Q4_K_M | 4 | 4.9 GB | Medium | C43 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
Copy-paste commands to run aya expanse 8b on your machine.
Run
lms load hf-bartowski--aya-expanse-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 31%.
~$2,499 MSRP
~$2,499 MSRP
Yes, Radeon Pro W6800 32GB can run aya expanse 8b with a C grade (Runs well). Expected decode speed: 58.8 tok/s.
aya expanse 8b (8B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
The recommended quantization for aya expanse 8b is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W6800 32GB, aya expanse 8b achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.
For coding workloads, aya expanse 8b on Radeon Pro W6800 32GB receives a C grade with 58.8 tok/s and 393K context.
On Radeon Pro W6800 32GB, aya expanse 8b can safely use up to 393K 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-bartowski--aya-expanse-8b-gguf-on-radeon-pro-w6800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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