Will It Run AI

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
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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.

Capabilities:

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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well52.5 tok/s2011 ms320K
CodingCRuns well52.5 tok/s3686 ms320K
Agentic CodingCRuns well52.5 tok/s5362 ms320K
ReasoningCRuns well52.5 tok/s4357 ms320K
RAGCRuns well52.5 tok/s6703 ms320K

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).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC44
Q3_K_S
3
3.4 GB
LowC44
NVFP4
4
3.9 GB
MediumC45
Q4_K_M
4
4.3 GB
MediumC45
Q5_K_M
5
5.0 GB
HighC45
Q6_K
6
5.7 GB
HighC46
Q8_0
8
7.5 GB
Very HighC47
F16Best for your GPU
16
14.3 GB
MaximumC50

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 start

Opciones de mejora

Hardware que ejecuta bien falcon mamba 7b instruct Q4 K M

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+1492)
C
Sube la velocidad estimada de decodificación alrededor de un 153%.133 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 153%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)896 GB/s (+596)
C
Sube la velocidad estimada de decodificación alrededor de un 87%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 87%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)576 GB/s (+276)
C
Sube la velocidad estimada de decodificación alrededor de un 87%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 87%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run falcon mamba 7b instruct Q4 K M?

Yes, NVIDIA L4 24GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 52.5 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 8.4 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, falcon mamba 7b instruct Q4 K M achieves approximately 52.5 tokens per second decode speed with a time-to-first-token of 3686ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run falcon mamba 7b instruct Q4 K M for coding?

For coding workloads, falcon mamba 7b instruct Q4 K M on NVIDIA L4 24GB receives a C grade with 52.5 tok/s and 320K context.

What context window can falcon mamba 7b instruct Q4 K M use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, falcon mamba 7b instruct Q4 K M can safely use up to 320K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GBSee all hardware for falcon mamba 7b instruct Q4 K M
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