MacBook Pro M4 Pro 64GBコスパ重視
64 GB Unified (+40)
B
Adds memory headroom for longer context windows and future model growth.20.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Can Baichuan 13B run on Intel Arc Pro B60 24GB?
YES — With Offload
B65Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q5_K_M (High quality) — 24.9 GB, 19.1 tok/s, Runs with offload (needs ~0.3 GB host RAM)
24.9 GB required24.0 GB available
104% VRAM needed
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
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsBaichuan 13B on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 19.1 tok/s decode · 10.1s TTFT (warm) · 48 tok/s prefill
What limits this setup
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.
Best improvement path
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.
Performance by workload
| 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 | 8K |
Quantization options
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 | 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 |
Get started
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 99アップグレードオプション
Baichuan 13Bを快適に動かすハードウェア
Frequently asked questions
Can Intel Arc Pro B60 24GB run Baichuan 13B?
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.
How much VRAM does Baichuan 13B need?
Baichuan 13B (13B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.
What is the best quantization for Baichuan 13B?
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Baichuan 13B run at on Intel Arc Pro B60 24GB?
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.
Can Intel Arc Pro B60 24GB run Baichuan 13B for coding?
For coding workloads, Baichuan 13B on Intel Arc Pro B60 24GB receives a B grade with 19.1 tok/s and 8K context.
What context window can Baichuan 13B use on Intel Arc Pro B60 24GB?
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.
What should I upgrade first if Baichuan 13B feels slow on Intel Arc Pro B60 24GB?
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.
Would CUDA be a better path than Intel Arc Pro B60 24GB for Baichuan 13B?
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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<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>
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