Can Llama 3.1 8B run on RTX 3000 Ada Laptop 8GB?

Q: What should I upgrade first if Llama 3.1 8B feels slow on RTX 3000 Ada Laptop 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.

BARELY — Tight on Memory

B61Good

Estimated from fit model

Llama 3.1 8B needs ~8.8 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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) — 8.8 GB, 28.2 tok/s, Very compromised (needs ~0.5 GB host RAM)

8.8 GB required8.0 GB available

110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.5 GB host RAM)

Decode

28.2 tok/s

TTFT

6866 ms

Safe context

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on RTX 3000 Ada Laptop 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 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.

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 0.5 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	A	Runs with offload	46.3 tok/s	2280 ms	9K
Coding	B	Very compromised (needs ~0.5 GB host RAM)	28.2 tok/s	6866 ms	9K
Agentic Coding	F	Too heavy	18.5 tok/s	15207 ms	9K
Reasoning	B	Very compromised (needs ~0.5 GB host RAM)	28.2 tok/s	8114 ms	9K
RAG	F	Too heavy	18.5 tok/s	19009 ms

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A75
Q3_K_S	3	3.9 GB	Low	A74
NVFP4	4

Get started

Copy-paste commands to run Llama 3.1 8B on your machine.

Run

ollama run llama3.1

Upgrade options

Hardware that runs Llama 3.1 8B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+72)

Removes host-memory offload, which is usually the single biggest latency and throughput win.52.3 tok/s decode

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

Raises estimated decode speed by about 85%.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+160)

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

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

Raises estimated decode speed by about 117%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.46.3 tok/s decode

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

Raises estimated decode speed by about 64%.

~$499 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run Llama 3.1 8B?

Yes, RTX 3000 Ada Laptop 8GB can run Llama 3.1 8B with a B grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 28.2 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 8B?

The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 8B run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Llama 3.1 8B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6866ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on RTX 3000 Ada Laptop 8GB receives a B grade with 28.2 tok/s and 9K context.

What context window can Llama 3.1 8B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Llama 3.1 8B can safely use up to 9K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 8B feels slow on RTX 3000 Ada Laptop 8GB?

See all results for RTX 3000 Ada Laptop 8GB See all hardware for Llama 3.1 8B

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