Can Samantha 7B run on Tesla P40 24GB?

YES — Runs Great

B65Good

Estimated from fit model

Samantha 7B needs ~9.8 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.8 GB, 51.4 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

51.4 tok/s

TTFT

3767 ms

Safe context

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsSamantha 7B 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: 51.4 tok/s decode · 3.8s TTFT (warm) · 129 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	B	Runs well	51.4 tok/s	2055 ms	4K
Coding	B	Runs well	51.4 tok/s	3767 ms	4K
Agentic Coding	B	Runs well	51.4 tok/s	5479 ms	4K
Reasoning	B	Runs well	51.4 tok/s	4452 ms	4K
RAG	B	Runs well	51.4 tok/s	6849 ms	4K

Quantization options

How Samantha 7B (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	B60
Q3_K_S	3	3.4 GB	Low	B61
NVFP4	4	3.9 GB	Medium	B61
Q4_K_M	4	4.3 GB	Medium	B61
Q5_K_M	5	5.0 GB	High	B62
Q6_K	6	5.7 GB	High	B62
Q8_0	8	7.5 GB	Very High	B63
F16Best for your GPU	16	14.3 GB	Maximum	B66

Get started

Copy-paste commands to run Samantha 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
  --hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien Samantha 7B

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 91%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 91%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Samantha 7B?

Yes, Tesla P40 24GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 51.4 tok/s.

How much VRAM does Samantha 7B need?

Samantha 7B (7B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Samantha 7B?

The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Samantha 7B run at on Tesla P40 24GB?

On Tesla P40 24GB, Samantha 7B achieves approximately 51.4 tokens per second decode speed with a time-to-first-token of 3767ms using Q4_K_M quantization.

Can Tesla P40 24GB run Samantha 7B for coding?

For coding workloads, Samantha 7B on Tesla P40 24GB receives a B grade with 51.4 tok/s and 4K context.

What context window can Samantha 7B use on Tesla P40 24GB?

On Tesla P40 24GB, Samantha 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Samantha 7B

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