MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
B
This setup is broadly balanced for this model.33 tok/s decode
~$2,499 MSRP
Cerebras-GPT 13B needs ~23.5 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q5_K_M quantization, expect ~31 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
31.2 tok/s
TTFT
6196 ms
Safe context
30K
Memory
23.5 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 | B | Runs well | 31.2 tok/s | 3380 ms | 30K |
| Coding | B | Runs well | 31.2 tok/s | 6196 ms | 30K |
| Agentic Coding | B | Runs with offload (needs ~0.4 GB host RAM) | 21.6 tok/s | 13060 ms | 30K |
| Reasoning | B | Runs well | 31.2 tok/s | 7323 ms | 30K |
| RAG | B | Runs with offload (needs ~0.4 GB host RAM) | 21.6 tok/s | 16325 ms | 30K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B60 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
NVFP4 | 4 | 7.3 GB | Medium | B61 |
Q4_K_M | 4 | 7.9 GB | Medium | B61 |
Q5_K_M | 5 | 9.4 GB | High | B62 |
Q6_K | 6 | 10.7 GB | High | B62 |
Q8_0 | 8 | 13.9 GB | Very High | B64 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B65 |
Copy-paste commands to run Cerebras-GPT 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cerebras/Cerebras-GPT-13B" \
--hf-file "Cerebras-GPT-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
~$2,499 MSRP
NVIDIA A100 40GBBest valueRaises estimated decode speed by about 356%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Yes, Radeon Pro W6800 32GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 31.2 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 23.5 GB of memory with Q5_K_M quantization.
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
On Radeon Pro W6800 32GB, Cerebras-GPT 13B achieves approximately 31.2 tokens per second decode speed with a time-to-first-token of 6196ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on Radeon Pro W6800 32GB receives a B grade with 31.2 tok/s and 30K context.
On Radeon Pro W6800 32GB, Cerebras-GPT 13B can safely use up to 30K tokens of context. The model's official context limit is 131K, 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/cerebras-gpt-13b-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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