MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Baichuan 7B needs ~15.6 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 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
15.9 tok/s
TTFT
12157 ms
Safe context
8K
Memory
15.6 GB / 17.3 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 15.9 tok/s | 6631 ms | 8K |
| Coding | B | Tight fit | 15.9 tok/s | 12157 ms | 8K |
| Agentic Coding | F | Too heavy | 10.5 tok/s | 26856 ms | 8K |
| Reasoning | B | Tight fit | 15.9 tok/s | 14367 ms | 8K |
| RAG | F | Too heavy | 10.5 tok/s | 33570 ms | 8K |
How Baichuan 7B (7B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B62 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 |
Copy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 516%.
~$899 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, MacBook Air M3 24GB can run Baichuan 7B with a B grade (Tight fit). Expected decode speed: 15.9 tok/s.
Baichuan 7B (7B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M3 24GB, Baichuan 7B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12157ms using Q4_K_M quantization.
For coding workloads, Baichuan 7B on MacBook Air M3 24GB receives a B grade with 15.9 tok/s and 8K context.
On MacBook Air M3 24GB, Baichuan 7B 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-7b-on-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B63 |
Q4_K_M | 4 | 4.3 GB | Medium | B64 |
Q5_K_M | 5 | 5.0 GB | High | B64 |
Q6_K | 6 | 5.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B66 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Air M3 24GB 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.