blossom v3 baichuan2 7b i1 needs ~16.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~54 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
54.3 tok/s
TTFT
3563 ms
Safe context
1.0M
Memory
16.4 GB / 69.1 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 | 54.3 tok/s | 1944 ms | 1.0M |
| Coding | C | Runs well | 54.3 tok/s | 3563 ms | 1.0M |
| Agentic Coding | C | Runs well | 54.3 tok/s | 5183 ms | 1.0M |
| Reasoning | C | Runs well | 54.3 tok/s | 4211 ms | 1.0M |
| RAG | C | Runs well | 54.3 tok/s | 6479 ms | 1.0M |
How blossom v3 baichuan2 7b i1 (7B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D40 |
Q3_K_S | 3 | 3.4 GB | Low | D40 |
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 80%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 62%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run blossom v3 baichuan2 7b i1 with a C grade (Runs well). Expected decode speed: 54.3 tok/s.
blossom v3 baichuan2 7b i1 (7B parameters) requires approximately 16.4 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 MacBook Pro M2 Max 96GB, blossom v3 baichuan2 7b i1 achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.
For coding workloads, blossom v3 baichuan2 7b i1 on MacBook Pro M2 Max 96GB receives a C grade with 54.3 tok/s and 1.0M context.
On MacBook Pro M2 Max 96GB, blossom v3 baichuan2 7b i1 can safely use up to 1.0M 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-m2-max-96gb" 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 |
| D40 |
Q4_K_M | 4 | 4.3 GB | Medium | D40 |
Q5_K_M | 5 | 5.0 GB | High | D40 |
Q6_K | 6 | 5.7 GB | High | D40 |
Q8_0 | 8 | 7.5 GB | Very High | D40 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C41 |
Not always. MacBook Pro M2 Max 96GB 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.