Intel Arc B580 12GBBudget pick456 GB/s (+256)
C
Raises estimated decode speed by about 69%.51.3 tok/s decode
Raises estimated decode speed by about 69%.
~$249 MSRP
baichuan2 7b chat needs ~7.7 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~30 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
Runs well
Decode
30.4 tok/s
TTFT
6359 ms
Safe context
90K
Memory
7.7 GB / 11.5 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 | C | Runs well | 30.4 tok/s | 3469 ms | 90K |
| Coding | C | Runs well | 30.4 tok/s | 6359 ms | 90K |
| Agentic Coding | C | Runs well | 30.4 tok/s | 9249 ms | 90K |
| Reasoning | C | Runs well | 30.4 tok/s | 7515 ms | 90K |
| RAG | C | Runs well | 30.4 tok/s | 11562 ms | 90K |
How baichuan2 7b chat (7B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run baichuan2 7b chat on your machine.
Run
lms load hf-shaowenchen--baichuan2-7b-chat-gguf && lms server startUpgrade options
Intel Arc B580 12GBBudget pickRaises estimated decode speed by about 69%.
~$249 MSRP
Raises estimated decode speed by about 100%.
~$449 MSRP
Yes, MacBook Pro M1 Pro 16GB can run baichuan2 7b chat with a C grade (Runs well). Expected decode speed: 30.4 tok/s.
baichuan2 7b chat (7B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for baichuan2 7b chat is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, baichuan2 7b chat achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6359ms using Q4_K_M quantization.
For coding workloads, baichuan2 7b chat on MacBook Pro M1 Pro 16GB receives a C grade with 30.4 tok/s and 90K context.
On MacBook Pro M1 Pro 16GB, baichuan2 7b chat can safely use up to 90K tokens of context. The model's official context limit is —, 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/hf-shaowenchen--baichuan2-7b-chat-gguf-on-m1-pro-16gb" 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 |
| C51 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M1 Pro 16GB 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.