Cerebras-GPT 13B needs ~22.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q5_K_M quantization, expect ~27 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
Tight fit
Decode
26.8 tok/s
TTFT
7215 ms
Safe context
18K
Memory
22.7 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 26.8 tok/s | 3935 ms | 18K |
| Coding | B | Tight fit | 26.8 tok/s | 7215 ms | 18K |
| Agentic Coding | F | Too heavy | 11.1 tok/s | 25458 ms | 18K |
| Reasoning | B | Tight fit | 26.8 tok/s | 8527 ms | 18K |
| RAG | F | Too heavy | 11.1 tok/s | 31823 ms | 18K |
Inference speed
Estimated decode speed (tokens/sec) for Cerebras-GPT 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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 | Q5_K_M | 130.8 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 83.5 | Tight |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 75.3 | Tight |
| RTX 3090 24GB | 24 GB | Q5_K_M | 71.4 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 25.7 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 9.0 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 5.7 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_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 Cerebras-GPT 13B (13B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 | 7.3 GB | Medium | B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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 99Opciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 53%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,899 MSRP
RTX 5090 32GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 388%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
No es solo un salto de hardware. También te deja en un ecosistema de runtimes más limpio para LLMs locales.
~$1,999 MSRP
Yes, Intel Arc Pro B60 24GB can run Cerebras-GPT 13B with a B grade (Tight fit). Expected decode speed: 26.8 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 22.7 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 Intel Arc Pro B60 24GB, Cerebras-GPT 13B achieves approximately 26.8 tokens per second decode speed with a time-to-first-token of 7215ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on Intel Arc Pro B60 24GB receives a B grade with 26.8 tok/s and 18K context.
On Intel Arc Pro B60 24GB, Cerebras-GPT 13B can safely use up to 18K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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