Can aya expanse 8b orthogonal heretic run on Tesla P40 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

aya expanse 8b orthogonal heretic needs ~9.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~42 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) — 9.4 GB, 41.8 tok/s, Runs well

9.4 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

41.8 tok/s

TTFT

4628 ms

Safe context

265K

Memory

9.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsaya expanse 8b orthogonal heretic 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: 41.8 tok/s decode · 4.6s TTFT (warm) · 105 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	41.8 tok/s	2524 ms	265K
Coding	C	Runs well	41.8 tok/s	4628 ms	265K
Agentic Coding	C	Runs well	41.8 tok/s	6732 ms	265K
Reasoning	C	Runs well	41.8 tok/s	5470 ms	265K
RAG	C	Runs well	41.8 tok/s	8415 ms	265K

Inference speed

aya expanse 8b orthogonal heretic inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for aya expanse 8b orthogonal heretic at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How aya expanse 8b orthogonal heretic (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	C44
Q3_K_S	3	3.9 GB	Low	C44
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C45
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C46
Q8_0	8	8.6 GB	Very High	C47
F16Best for your GPU	16	16.4 GB	Maximum	C49

Get started

Copy-paste commands to run aya expanse 8b orthogonal heretic on your machine.

Run

lms load hf-mradermacher--aya-expanse-8b-orthogonal-heretic-gguf && lms server start

Upgrade-Optionen

Hardware, die aya expanse 8b orthogonal heretic gut ausführt

RTX 5090 32GBGünstige Wahl

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

Raises estimated decode speed by about 168%.112 tok/s Dekodierung

Raises estimated decode speed by about 168%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

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

Raises estimated decode speed by about 168%.112 tok/s Dekodierung

Raises estimated decode speed by about 168%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

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

Raises estimated decode speed by about 126%.94.4 tok/s Dekodierung

Raises estimated decode speed by about 126%.

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

ca. $4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run aya expanse 8b orthogonal heretic?

Yes, Tesla P40 24GB can run aya expanse 8b orthogonal heretic with a C grade (Runs well). Expected decode speed: 41.8 tok/s.

How much VRAM does aya expanse 8b orthogonal heretic need?

aya expanse 8b orthogonal heretic (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for aya expanse 8b orthogonal heretic?

The recommended quantization for aya expanse 8b orthogonal heretic is Q4_K_M, which balances quality and memory efficiency.

What speed will aya expanse 8b orthogonal heretic run at on Tesla P40 24GB?

On Tesla P40 24GB, aya expanse 8b orthogonal heretic achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4628ms using Q4_K_M quantization.

Can Tesla P40 24GB run aya expanse 8b orthogonal heretic for coding?

For coding workloads, aya expanse 8b orthogonal heretic on Tesla P40 24GB receives a C grade with 41.8 tok/s and 265K context.

What context window can aya expanse 8b orthogonal heretic use on Tesla P40 24GB?

On Tesla P40 24GB, aya expanse 8b orthogonal heretic can safely use up to 265K 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 aya expanse 8b orthogonal heretic

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