Can Meta Llama 3.1 8B Instruct run on RTX 4000 Ada 20GB?

YES — Runs Great

C50Usable

Estimated from fit model

Meta Llama 3.1 8B Instruct needs ~9.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 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) — 9.0 GB, 57.5 tok/s, Runs well

9.0 GB required20.0 GB available

45% VRAM used

Fit status

Runs well

Decode

57.5 tok/s

TTFT

3365 ms

Safe context

203K

Memory

9.0 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsMeta Llama 3.1 8B Instruct on RTX 4000 Ada 20GB
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: 57.5 tok/s decode · 3.4s TTFT (warm) · 144 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	57.5 tok/s	1835 ms	203K
Coding	C	Runs well	57.5 tok/s	3365 ms	203K
Agentic Coding	C	Runs well	57.5 tok/s	4894 ms	203K
Reasoning	C	Runs well	57.5 tok/s	3976 ms	203K
RAG	C	Runs well	57.5 tok/s	6117 ms	203K

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C47
Q4_K_M	4	4.9 GB	Medium	C47
Q5_K_M	5	5.8 GB	High	C48
Q6_K	6	6.6 GB	High	C48
Q8_0Best for your GPU	8	8.6 GB	Very High	C50
F16	16	16.4 GB	Maximum	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 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 95%.112 tok/s decode

Raises estimated decode speed by about 95%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 95%.112 tok/s decode

Raises estimated decode speed by about 95%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 95%.112 tok/s decode

Raises estimated decode speed by about 95%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Meta Llama 3.1 8B Instruct?

Yes, RTX 4000 Ada 20GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 57.5 tok/s.

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

Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

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

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

What speed will Meta Llama 3.1 8B Instruct run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Meta Llama 3.1 8B Instruct achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Meta Llama 3.1 8B Instruct for coding?

For coding workloads, Meta Llama 3.1 8B Instruct on RTX 4000 Ada 20GB receives a C grade with 57.5 tok/s and 203K context.

What context window can Meta Llama 3.1 8B Instruct use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Meta Llama 3.1 8B Instruct can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Meta Llama 3.1 8B Instruct

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<iframe src="https://willitrunai.com/embed/hf-bartowski--meta-llama-3-1-8b-instruct-gguf-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

57.5 tok/s

1835 ms

203K

Coding

Runs well

57.5 tok/s

3365 ms

203K

Agentic Coding

Runs well

57.5 tok/s

4894 ms

203K

Reasoning

Runs well

57.5 tok/s

3976 ms

203K

RAG

Runs well

57.5 tok/s

6117 ms

203K

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C47
Q4_K_M	4	4.9 GB	Medium	C47
Q5_K_M	5	5.8 GB	High	C48
Q6_K	6	6.6 GB	High	C48
Q8_0Best for your GPU	8	8.6 GB	Very High	C50
F16	16	16.4 GB	Maximum	F0