Can Llama 3.2 1B Instruct run on GTX 1080 8GB?

YES — Runs Great

C43Usable

Estimated from fit model

Llama 3.2 1B Instruct needs ~2.7 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 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) — 2.7 GB, 14.0 tok/s, Runs well

2.7 GB required8.0 GB available

34% VRAM used

Fit status

Runs well

Decode

14.0 tok/s

TTFT

13829 ms

Safe context

736K

Memory

2.7 GB / 8.0 GB

Memory breakdown

Weights0.6 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3.2 1B Instruct on GTX 1080 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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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	14.0 tok/s	7543 ms	431K
Coding	C	Runs well	14.0 tok/s	13829 ms	736K
Agentic Coding	C	Runs well	14.0 tok/s	20114 ms	736K
Reasoning	C	Runs well	14.0 tok/s	16343 ms	736K
RAG	C	Runs well	14.0 tok/s	25143 ms	736K

Inference speed

Llama 3.2 1B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.2 1B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~19 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	19.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	16.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	16.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	14.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	14.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	14.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	14.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	14.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	14.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	14.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	14.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	14.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	14.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	14.0	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 Llama 3.2 1B Instruct (1B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	C49
Q3_K_S	3	0.5 GB	Low	C49
NVFP4	4	0.6 GB	Medium	C49
Q4_K_M	4	0.6 GB	Medium	C49
Q5_K_M	5	0.7 GB	High	C49
Q6_K	6	0.8 GB	High	C50
Q8_0	8	1.1 GB	Very High	C50
F16Best for your GPU	16	2.1 GB	Maximum	C52

Get started

Copy-paste commands to run Llama 3.2 1B Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-2-1b-instruct-gguf && lms server start

Frequently asked questions

Can GTX 1080 8GB run Llama 3.2 1B Instruct?

Yes, GTX 1080 8GB can run Llama 3.2 1B Instruct with a C grade (Runs well). Expected decode speed: 14.0 tok/s.

How much VRAM does Llama 3.2 1B Instruct need?

Llama 3.2 1B Instruct (1B parameters) requires approximately 2.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 1B Instruct?

The recommended quantization for Llama 3.2 1B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 1B Instruct run at on GTX 1080 8GB?

On GTX 1080 8GB, Llama 3.2 1B Instruct achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q4_K_M quantization.

Can GTX 1080 8GB run Llama 3.2 1B Instruct for coding?

For coding workloads, Llama 3.2 1B Instruct on GTX 1080 8GB receives a C grade with 14.0 tok/s and 736K context.

What context window can Llama 3.2 1B Instruct use on GTX 1080 8GB?

On GTX 1080 8GB, Llama 3.2 1B Instruct can safely use up to 736K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 8GB See all hardware for Llama 3.2 1B Instruct

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