Mac mini M4 64GBGünstige Wahl
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.8.3 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,099 MSRP
Can Baichuan 13B run on MacBook Pro M3 Pro 36GB?
YES — With Offload
B63Good
Estimated from fit model
Baichuan 13B needs ~26.4 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q5_K_M quantization, expect ~12 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 26.4 GB, 11.5 tok/s, Runs with offload (needs ~0.2 GB host RAM)
26.4 GB required25.9 GB available
102% VRAM needed
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
11.5 tok/s
TTFT
16857 ms
Safe context
8K
Memory
26.4 GB / 25.9 GB
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsBaichuan 13B on MacBook Pro M3 Pro 36GB
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: 11.5 tok/s decode · 16.9s TTFT (warm) · 29 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement 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 | 11.9 tok/s | 8850 ms | 8K |
| Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 11.5 tok/s | 16857 ms | 8K |
| Agentic Coding | F | Too heavy | 7.0 tok/s | 39982 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.2 GB host RAM) | 11.5 tok/s | 19921 ms | 8K |
| RAG | F | Too heavy | 7.0 tok/s | 49977 ms | 8K |
Inference speed
Baichuan 13B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Baichuan 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 | 61.0 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 52.4 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 48.7 | Offloads |
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 |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 20.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 7.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.5 | 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.
Quantization options
How Baichuan 13B (13B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B61 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 | 7.3 GB | Medium | B62 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B63 |
Q6_K | 6 | 10.7 GB | High | B64 |
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 99Upgrade-Optionen
Hardware, die Baichuan 13B gut ausführt
MacBook Pro M4 Pro 64GBBestes Preis-Leistungs-Verhältnis
64 GB Unified (+28)273 GB/s (+123)
B
Raises estimated decode speed by about 76%.20.2 tok/s Dekodierung
Raises estimated decode speed by about 76%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,599 MSRP
MacBook Pro M4 Max 48GBApple-Upgrade
48 GB Unified (+12)546 GB/s (+396)
B
Raises estimated decode speed by about 187%.33 tok/s Dekodierung
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Pro 36GB run Baichuan 13B?
Yes, MacBook Pro M3 Pro 36GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 11.5 tok/s.
How much VRAM does Baichuan 13B need?
Baichuan 13B (13B parameters) requires approximately 26.4 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 MacBook Pro M3 Pro 36GB?
On MacBook Pro M3 Pro 36GB, Baichuan 13B achieves approximately 11.5 tokens per second decode speed with a time-to-first-token of 16857ms using Q5_K_M quantization.
Can MacBook Pro M3 Pro 36GB run Baichuan 13B for coding?
For coding workloads, Baichuan 13B on MacBook Pro M3 Pro 36GB receives a B grade with 11.5 tok/s and 8K context.
What context window can Baichuan 13B use on MacBook Pro M3 Pro 36GB?
On MacBook Pro M3 Pro 36GB, 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 MacBook Pro M3 Pro 36GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Baichuan 13B?
Not always. MacBook Pro M3 Pro 36GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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