falcon mamba 7b instruct Q4 K M needs ~16.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~63 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
62.5 tok/s
TTFT
3098 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 | 62.5 tok/s | 1690 ms | 1.0M |
| Coding | C | Runs well | 62.5 tok/s | 3098 ms | 1.0M |
| Agentic Coding | C | Runs well | 62.5 tok/s | 4507 ms | 1.0M |
| Reasoning | C | Runs well | 62.5 tok/s | 3662 ms | 1.0M |
| RAG | C | Runs well | 62.5 tok/s | 5634 ms | 1.0M |
How falcon mamba 7b instruct Q4 K M (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 falcon mamba 7b instruct Q4 K M on your machine.
Run
lms load hf-tiiuae--falcon-mamba-7b-instruct-q4-k-m-gguf && lms server startUpgrade options
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 48%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 62.5 tok/s.
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.
The recommended quantization for falcon mamba 7b instruct Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 96GB, falcon mamba 7b instruct Q4 K M achieves approximately 62.5 tokens per second decode speed with a time-to-first-token of 3098ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on MacBook Pro M2 Max 96GB receives a C grade with 62.5 tok/s and 1.0M context.
On MacBook Pro M2 Max 96GB, falcon mamba 7b instruct Q4 K M 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-tiiuae--falcon-mamba-7b-instruct-q4-k-m-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 |
| C40 |
Q4_K_M | 4 | 4.3 GB | Medium | C40 |
Q5_K_M | 5 | 5.0 GB | High | C40 |
Q6_K | 6 | 5.7 GB | High | C40 |
Q8_0 | 8 | 7.5 GB | Very High | C40 |
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.