Can Llama 3 8B Instruct 32k v0.1 run on GTX 1080 Ti 11GB?

YES — Runs Great

B56Good

Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~8.1 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.1 GB, 58.5 tok/s, Runs well

8.1 GB required11.0 GB available

74% VRAM used

Fit status

Runs well

Decode

58.5 tok/s

TTFT

3308 ms

Safe context

65K

Memory

8.1 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on GTX 1080 Ti 11GB
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: 58.5 tok/s decode · 3.3s TTFT (warm) · 146 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	58.5 tok/s	1805 ms	65K
Coding	B	Runs well	58.5 tok/s	3308 ms	65K
Agentic Coding	C	Tight fit	58.5 tok/s	4812 ms	65K
Reasoning	B	Runs well	58.5 tok/s	3910 ms	65K
RAG	C	Tight fit	58.5 tok/s	6015 ms	65K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Llama 3 8B Instruct 32k v0.1 well

RTX 5070 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+188)

Raises estimated decode speed by about 48%.86.8 tok/s decode

Raises estimated decode speed by about 48%.

~$549 MSRP

RTX 4070 Super 12GBBest value

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 36%.79.5 tok/s decode

Raises estimated decode speed by about 36%.

~$599 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 32%.77.5 tok/s decode

Raises estimated decode speed by about 32%.

~$599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Llama 3 8B Instruct 32k v0.1?

Yes, GTX 1080 Ti 11GB can run Llama 3 8B Instruct 32k v0.1 with a B grade (Runs well). Expected decode speed: 58.5 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 58.5 tokens per second decode speed with a time-to-first-token of 3308ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on GTX 1080 Ti 11GB receives a B grade with 58.5 tok/s and 65K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 65K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for Llama 3 8B Instruct 32k v0.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: