Can Meta Llama 3.1 8B Instruct run on RTX 4050 Laptop 6GB?

Q: What should I upgrade first if Meta Llama 3.1 8B Instruct feels slow on RTX 4050 Laptop 6GB?

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.

YES — With Q3_K_S

D39Poor

Estimated from fit model

Meta Llama 3.1 8B Instruct needs ~6.7 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q3_K_S quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very 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.

Meta Llama 3.1 8B Instruct at Q4_K_M needs 7.6 GB — too much for RTX 4050 Laptop 6GB (6.0 GB). Runs at Q3_K_S (6.7 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.6 GB, exceeds 6.0 GB available

7.6 GB required6.0 GB available

127% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.0 tok/s

TTFT

14854 ms

Safe context

Memory

7.6 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMeta Llama 3.1 8B Instruct on RTX 4050 Laptop 6GB
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: 13.0 tok/s decode · 14.9s TTFT (warm) · 33 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 0.4 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	D	Very compromised (needs ~0.8 GB host RAM)	14.9 tok/s	7088 ms	4K
Coding	F	Too heavy	13.0 tok/s	14854 ms	4K
Agentic Coding	F	Too heavy	10.2 tok/s	27586 ms	4K
Reasoning	F	Too heavy	13.0 tok/s	17555 ms	4K
RAG	F	Too heavy	10.2 tok/s	34482 ms	4K

Inference speed

Meta Llama 3.1 8B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Meta Llama 3.1 8B Instruct 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

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	F0

Get started

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

Run

lms load hf-bartowski--meta-llama-3-1-8b-instruct-gguf && lms server start

Upgrade options

Hardware that runs Meta Llama 3.1 8B Instruct well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.30.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+2)272 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$299 MSRP

Frequently asked questions

Can RTX 4050 Laptop 6GB run Meta Llama 3.1 8B Instruct?

Yes, RTX 4050 Laptop 6GB can run Meta Llama 3.1 8B Instruct at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.6 GB which exceeds available memory, but at Q3_K_S it needs only 6.7 GB. Expected decode speed: 20.0 tok/s.

How much VRAM does Meta Llama 3.1 8B Instruct need?

Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 7.6 GB at Q4_K_M quantization. On RTX 4050 Laptop 6GB, it fits at Q3_K_S using 6.7 GB.

What is the best quantization for Meta Llama 3.1 8B Instruct?

The recommended quantization is Q4_K_M, but on RTX 4050 Laptop 6GB the best fitting quantization is Q3_K_S, which uses 6.7 GB.

What speed will Meta Llama 3.1 8B Instruct run at on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Meta Llama 3.1 8B Instruct achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9662ms using Q3_K_S quantization.

Can RTX 4050 Laptop 6GB run Meta Llama 3.1 8B Instruct for coding?

For coding workloads, Meta Llama 3.1 8B Instruct on RTX 4050 Laptop 6GB receives a F grade with 13.0 tok/s and 4K context.

What context window can Meta Llama 3.1 8B Instruct use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Meta Llama 3.1 8B Instruct can safely use up to 5K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4050 Laptop 6GB See all hardware for Meta Llama 3.1 8B Instruct

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