MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+40)
B
Adds memory headroom for longer context windows and future model growth.20.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Baichuan 13B needs ~24.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q5_K_M quantization, expect ~19 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
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
19.1 tok/s
TTFT
10143 ms
Safe context
8K
Memory
24.9 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 | 8K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 19.1 tok/s | 10143 ms | 8K |
| Agentic Coding | F | Too heavy | 8.5 tok/s | 33322 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 19.1 tok/s | 11987 ms | 8K |
| RAG | F | Too heavy | 8.5 tok/s | 41652 ms |
How Baichuan 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 |
Copy-paste commands to run Baichuan 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-13B-Chat" \
--hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 115%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Yes, Intel Arc Pro B60 24GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 19.1 tok/s.
Baichuan 13B (13B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Baichuan 13B achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10143ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on Intel Arc Pro B60 24GB receives a B grade with 19.1 tok/s and 8K context.
On Intel Arc Pro B60 24GB, Baichuan 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/baichuan-13b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
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 |
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.