Can Yi 1.5 9B run on Tesla P40 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi 1.5 9B needs ~10.6 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 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) — 10.6 GB, 40.4 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

40.4 tok/s

TTFT

4788 ms

Safe context

Memory

10.6 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 1.5 9B 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: 40.4 tok/s decode · 4.8s TTFT (warm) · 101 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	40.4 tok/s	2611 ms	4K
Coding	C	Runs well	40.4 tok/s	4788 ms	4K
Agentic Coding	C	Runs well	40.4 tok/s	6964 ms	4K
Reasoning	C	Runs well	40.4 tok/s	5658 ms	4K
RAG	C	Runs well	40.4 tok/s	8705 ms	4K

Quantization options

How Yi 1.5 9B (9B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C49
Q3_K_S	3	4.4 GB	Low	C50
NVFP4	4	5.0 GB	Medium	C50
Q4_K_M	4	5.5 GB	Medium	C50
Q5_K_M	5	6.5 GB	High	C51
Q6_K	6	7.4 GB	High	C51
Q8_0	8	9.6 GB	Very High	C53
F16Best for your GPU	16	18.5 GB	Maximum	C54

Get started

Copy-paste commands to run Yi 1.5 9B on your machine.

Run

lms load Yi-1.5-9B-Chat && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi 1.5 9B

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 212%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 212%.

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 212%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 212%.

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 126%.91.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 126%.

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 Yi 1.5 9B?

Yes, Tesla P40 24GB can run Yi 1.5 9B with a C grade (Runs well). Expected decode speed: 40.4 tok/s.

How much VRAM does Yi 1.5 9B need?

Yi 1.5 9B (9B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 9B?

The recommended quantization for Yi 1.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 9B run at on Tesla P40 24GB?

On Tesla P40 24GB, Yi 1.5 9B achieves approximately 40.4 tokens per second decode speed with a time-to-first-token of 4788ms using Q4_K_M quantization.

Can Tesla P40 24GB run Yi 1.5 9B for coding?

For coding workloads, Yi 1.5 9B on Tesla P40 24GB receives a C grade with 40.4 tok/s and 4K context.

What context window can Yi 1.5 9B use on Tesla P40 24GB?

On Tesla P40 24GB, Yi 1.5 9B 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 Yi 1.5 9B

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