Can Aya Expanse 8B run on Tesla P40 24GB?

YES — Runs Great

C50Usable

Estimated from fit model

Aya Expanse 8B needs ~10.1 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~45 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) — 10.1 GB, 45.0 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

45.0 tok/s

TTFT

4305 ms

Safe context

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsAya Expanse 8B 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: 45.0 tok/s decode · 4.3s TTFT (warm) · 112 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	45.0 tok/s	2348 ms	8K
Coding	C	Runs well	45.0 tok/s	4305 ms	8K
Agentic Coding	C	Runs well	45.0 tok/s	6262 ms	8K
Reasoning	C	Runs well	45.0 tok/s	5088 ms	8K
RAG	C	Runs well	45.0 tok/s	7828 ms	8K

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C47
Q4_K_M	4	4.9 GB	Medium	C47
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C48
Q8_0	8	8.6 GB	Very High	C49
F16Best for your GPU	16	16.4 GB	Maximum	C51

Get started

Copy-paste commands to run Aya Expanse 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "CohereForAI/aya-expanse-8b" \
  --hf-file "aya-expanse-8b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

Aya Expanse 8Bを快適に動かすハードウェア

RTX 5090 32GBコスパ重視

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

Raises estimated decode speed by about 238%.152 tok/sデコード

Raises estimated decode speed by about 238%.

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

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

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

Raises estimated decode speed by about 149%.112 tok/sデコード

Raises estimated decode speed by about 149%.

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

〜$2,499 MSRP

RTX 5000 Ada 32GBNVIDIAアップグレード

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

Raises estimated decode speed by about 126%.101.5 tok/sデコード

Raises estimated decode speed by about 126%.

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

〜$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Aya Expanse 8B?

Yes, Tesla P40 24GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 45.0 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 8B?

The recommended quantization for Aya Expanse 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 8B run at on Tesla P40 24GB?

On Tesla P40 24GB, Aya Expanse 8B achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4305ms using Q4_K_M quantization.

Can Tesla P40 24GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on Tesla P40 24GB receives a C grade with 45.0 tok/s and 8K context.

What context window can Aya Expanse 8B use on Tesla P40 24GB?

On Tesla P40 24GB, Aya Expanse 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Aya Expanse 8B

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