RTX 5060 Ti 16GBBudget pick448 GB/s (+298)
C
Raises estimated decode speed by about 154%.74.8 tok/s decode
Raises estimated decode speed by about 154%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
falcon mamba 7b instruct Q4 K M needs ~7.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 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
29.5 tok/s
TTFT
6565 ms
Safe context
114K
Memory
7.9 GB / 13.0 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 | 29.5 tok/s | 3581 ms | 114K |
| Coding | C | Runs well | 29.5 tok/s | 6565 ms | 114K |
| Agentic Coding | C | Runs well | 29.5 tok/s | 9549 ms | 114K |
| Reasoning | C | Runs well | 29.5 tok/s | 7758 ms | 114K |
| RAG | C | Runs well | 29.5 tok/s | 11936 ms | 114K |
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C48 |
Q3_K_S | 3 | 3.4 GB | Low | C49 |
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
RTX 5060 Ti 16GBBudget pickRaises estimated decode speed by about 154%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Raises estimated decode speed by about 232%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, MacBook Pro M3 Pro 18GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 29.5 tok/s.
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 7.9 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 M3 Pro 18GB, falcon mamba 7b instruct Q4 K M achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6565ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on MacBook Pro M3 Pro 18GB receives a C grade with 29.5 tok/s and 114K context.
On MacBook Pro M3 Pro 18GB, falcon mamba 7b instruct Q4 K M can safely use up to 114K 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-m3-pro-18gb" 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 |
| C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C50 |
Q5_K_M | 5 | 5.0 GB | High | C51 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M3 Pro 18GB 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.