Can falcon mamba 7b instruct Q4 K M run on Tesla P40 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

falcon mamba 7b instruct Q4 K M needs ~8.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.4 GB, 55.0 tok/s, Runs well

8.4 GB required24.0 GB available

35% VRAM used

Fit status

Runs well

Decode

55.0 tok/s

TTFT

3521 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 Tesla P40 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: 55.0 tok/s decode · 3.5s TTFT (warm) · 137 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	55.0 tok/s	1921 ms	320K
Coding	C	Runs well	55.0 tok/s	3521 ms	320K
Agentic Coding	C	Runs well	55.0 tok/s	5122 ms	320K
Reasoning	C	Runs well	55.0 tok/s	4162 ms	320K
RAG	C	Runs well	55.0 tok/s	6402 ms	320K

Quantization options

How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on Tesla P40 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 start

Upgrade options

Hardware that runs falcon mamba 7b instruct Q4 K M well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 142%.133 tok/s decode

Raises estimated decode speed by about 142%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 78%.98 tok/s decode

Raises estimated decode speed by about 78%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 78%.98 tok/s decode

Raises estimated decode speed by about 78%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run falcon mamba 7b instruct Q4 K M?

Yes, Tesla P40 24GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 55.0 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 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 Tesla P40 24GB?

On Tesla P40 24GB, falcon mamba 7b instruct Q4 K M achieves approximately 55.0 tokens per second decode speed with a time-to-first-token of 3521ms using Q4_K_M quantization.

Can Tesla P40 24GB run falcon mamba 7b instruct Q4 K M for coding?

For coding workloads, falcon mamba 7b instruct Q4 K M on Tesla P40 24GB receives a C grade with 55.0 tok/s and 320K context.

What context window can falcon mamba 7b instruct Q4 K M use on Tesla P40 24GB?

On Tesla P40 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 Tesla P40 24GB See all hardware for falcon mamba 7b instruct Q4 K M

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