Can Aya Expanse 8B run on GTX 1070 8GB?

YES — With Offload

C40Usable

Estimated from fit model

Aya Expanse 8B needs ~8.5 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.5 GB, 21.1 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

21.1 tok/s

TTFT

9191 ms

Safe context

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsAya Expanse 8B on GTX 1070 8GB
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: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	31.0 tok/s	3412 ms	8K
Coding	C	Runs with offload	19.6 tok/s	9881 ms	8K
Agentic Coding	F	Too heavy	12.5 tok/s	22599 ms	8K
Reasoning	C	Runs with offload	19.6 tok/s	11677 ms	8K
RAG	F	Too heavy	12.5 tok/s	28248 ms	8K

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B55
Q3_K_S	3	3.9 GB	Low	C55
NVFP4	4	4.5 GB	Medium	C55
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C54
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

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

Opções de upgrade

Hardware que roda bem Aya Expanse 8B

RTX 3060 12GBOpção econômica

12 GB VRAM (+4)360 GB/s (+104)

Removes host-memory offload, which is usually the single biggest latency and throughput win.44 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 109%.

~$329 MSRP

RTX 5060 Ti 16GBMelhor custo-benefício

16 GB VRAM (+8)448 GB/s (+192)

Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 190%.

~$449 MSRP

RTX 4060 Ti 16GBUpgrade NVIDIA

16 GB VRAM (+8)288 GB/s (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42.6 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 102%.

~$499 MSRP

Frequently asked questions

Can GTX 1070 8GB run Aya Expanse 8B?

Yes, GTX 1070 8GB can run Aya Expanse 8B with a C grade (Runs with offload). Expected decode speed: 19.6 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 8.5 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 GTX 1070 8GB?

On GTX 1070 8GB, Aya Expanse 8B achieves approximately 19.6 tokens per second decode speed with a time-to-first-token of 9881ms using Q4_K_M quantization.

Can GTX 1070 8GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on GTX 1070 8GB receives a C grade with 19.6 tok/s and 8K context.

What context window can Aya Expanse 8B use on GTX 1070 8GB?

On GTX 1070 8GB, 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.

What should I upgrade first if Aya Expanse 8B feels slow on GTX 1070 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for GTX 1070 8GB See all hardware for Aya Expanse 8B

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