32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 153%.133 tok/s decode
Raises estimated decode speed by about 153%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can falcon mamba 7b instruct Q4 K M run on NVIDIA L4 24GB?
YES — Runs Great
C47Usable
○Estimated from fit model
falcon mamba 7b instruct Q4 K M needs ~8.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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) — 8.4 GB, 52.5 tok/s, Runs well
8.4 GB required24.0 GB available
35% VRAM used
Fit status
Runs well
Decode
52.5 tok/s
TTFT
3686 ms
Safe context
320K
Memory
8.4 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsfalcon mamba 7b instruct Q4 K M on NVIDIA L4 24GB
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: 52.5 tok/s decode · 3.7s TTFT (warm) · 131 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 | C | Runs well | 52.5 tok/s | 2011 ms | 320K |
| Coding | C | Runs well | 52.5 tok/s | 3686 ms | 320K |
| Agentic Coding | C | Runs well | 52.5 tok/s | 5362 ms | 320K |
| Reasoning | C | Runs well | 52.5 tok/s | 4357 ms | 320K |
| RAG | C | Runs well | 52.5 tok/s | 6703 ms | 320K |
Quantization options
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 |
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 startUpgrade options
Hardware that runs falcon mamba 7b instruct Q4 K M well
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 87%.98 tok/s decode
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 87%.98 tok/s decode
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP