RTX 3060 12GBOpção econômica12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.64 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can falcon mamba 7b instruct Q4 K M run on RTX 3060 Ti 8GB?
YES — Tight Fit
C53Usable
Estimated from fit model
falcon mamba 7b instruct Q4 K M needs ~6.8 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~82 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 6.8 GB, 82.0 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
82.0 tok/s
TTFT
2360 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsfalcon mamba 7b instruct Q4 K M on RTX 3060 Ti 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 82.0 tok/s decode · 2.4s TTFT (warm) · 205 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 82.0 tok/s | 1287 ms | 40K |
| Coding | C | Tight fit | 82.0 tok/s | 2360 ms | 40K |
| Agentic Coding | C | Runs with offload | 82.0 tok/s | 3432 ms | 40K |
| Reasoning | C | Tight fit | 82.0 tok/s | 2789 ms | 40K |
| RAG | C | Runs with offload | 82.0 tok/s | 4290 ms | 40K |
Quantization options
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on RTX 3060 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 |
Get started
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 startOpções de upgrade
Hardware que roda bem falcon mamba 7b instruct Q4 K M
RTX 3060 12GBOpção econômicaRTX 5070 12GBMelhor custo-benefício12 GB VRAM (+4)672 GB/s (+224)
C
Raises estimated decode speed by about 39%.114 tok/s decodificação
Raises estimated decode speed by about 39%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBUpgrade NVIDIA12 GB VRAM (+4)504 GB/s (+56)
C
Adds memory headroom for longer context windows and future model growth.101.8 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 3060 Ti 8GB run falcon mamba 7b instruct Q4 K M?
Yes, RTX 3060 Ti 8GB can run falcon mamba 7b instruct Q4 K M with a C grade (Tight fit). Expected decode speed: 82.0 tok/s.
How much VRAM does falcon mamba 7b instruct Q4 K M need?
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
What is the best quantization for falcon mamba 7b instruct Q4 K M?
The recommended quantization for falcon mamba 7b instruct Q4 K M is Q4_K_M, which balances quality and memory efficiency.
What speed will falcon mamba 7b instruct Q4 K M run at on RTX 3060 Ti 8GB?
On RTX 3060 Ti 8GB, falcon mamba 7b instruct Q4 K M achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2360ms using Q4_K_M quantization.
Can RTX 3060 Ti 8GB run falcon mamba 7b instruct Q4 K M for coding?
For coding workloads, falcon mamba 7b instruct Q4 K M on RTX 3060 Ti 8GB receives a C grade with 82.0 tok/s and 40K context.
What context window can falcon mamba 7b instruct Q4 K M use on RTX 3060 Ti 8GB?
On RTX 3060 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.
Embed this result▼
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-3060-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: