RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.64 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
falcon mamba 7b instruct Q4 K M needs ~6.8 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~75 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
Tight fit
Decode
74.8 tok/s
TTFT
2588 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 74.8 tok/s | 1412 ms | 40K |
| Coding | C | Tight fit | 74.8 tok/s | 2588 ms | 40K |
| Agentic Coding | C | Runs with offload | 74.8 tok/s | 3764 ms | 40K |
| Reasoning | C | Tight fit | 74.8 tok/s | 3059 ms | 40K |
| RAG | C | Runs with offload | 74.8 tok/s | 4705 ms | 40K |
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBNVIDIA upgradeRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 5060 Ti 8GB can run falcon mamba 7b instruct Q4 K M with a C grade (Tight fit). Expected decode speed: 74.8 tok/s.
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 6.8 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 RTX 5060 Ti 8GB, falcon mamba 7b instruct Q4 K M achieves approximately 74.8 tokens per second decode speed with a time-to-first-token of 2588ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on RTX 5060 Ti 8GB receives a C grade with 74.8 tok/s and 40K context.
On RTX 5060 Ti 8GB, falcon mamba 7b instruct Q4 K M can safely use up to 40K 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-rtx-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: