blossom v3 baichuan2 7b i1 needs ~12.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~20 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
18.6 tok/s
TTFT
10400 ms
Safe context
663K
Memory
12.9 GB / 46.1 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 | 18.6 tok/s | 5673 ms | 663K |
| Coding | C | Runs well | 20.2 tok/s | 9568 ms | 663K |
| Agentic Coding | C | Runs well | 18.6 tok/s | 15127 ms | 663K |
| Reasoning | C | Runs well | 18.6 tok/s | 12291 ms | 663K |
| RAG | C | Runs well | 18.6 tok/s | 18909 ms | 663K |
How blossom v3 baichuan2 7b i1 (7B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run blossom v3 baichuan2 7b i1 on your machine.
Run
lms load hf-mradermacher--blossom-v3-baichuan2-7b-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 202%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 427%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run blossom v3 baichuan2 7b i1 with a C grade (Runs well). Expected decode speed: 20.2 tok/s.
blossom v3 baichuan2 7b i1 (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for blossom v3 baichuan2 7b i1 is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, blossom v3 baichuan2 7b i1 achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9568ms using Q4_K_M quantization.
For coding workloads, blossom v3 baichuan2 7b i1 on Mac mini M4 64GB receives a C grade with 20.2 tok/s and 663K context.
On Mac mini M4 64GB, blossom v3 baichuan2 7b i1 can safely use up to 663K 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-mradermacher--blossom-v3-baichuan2-7b-i1-gguf-on-m4-mini-64gb" 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 |
| C41 |
Q4_K_M | 4 | 4.3 GB | Medium | C41 |
Q5_K_M | 5 | 5.0 GB | High | C41 |
Q6_K | 6 | 5.7 GB | High | C41 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Not always. Mac mini M4 64GB 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.